Benchmarking Computational Methods for Multi-Omics Data Integration: A Comprehensive Guide for Biomedical Research

Abstract

The integration of multi-omics data is revolutionizing biomedical research, particularly in drug discovery and clinical outcome prediction. However, the rapid development of diverse computational methods presents a significant challenge for researchers in selecting and applying the most appropriate techniques. This article provides a systematic benchmark and comprehensive guide to navigating this complex landscape. We explore the foundational principles of multi-omics integration, categorize and evaluate state-of-the-art methodological frameworks, address common troubleshooting and optimization challenges, and present rigorous validation and comparative analysis strategies. By synthesizing insights from recent large-scale benchmarking studies, we equip researchers and drug development professionals with the knowledge to effectively leverage multi-omics data, enhance predictive accuracy, and derive robust biological insights.

The Multi-Omics Integration Landscape: Why Benchmarking is Essential for Modern Biology

Defining the Multi-Omics Data Integration Challenge

Multi-omics data integration represents a paradigm shift in biomedical research, moving from the isolated analysis of individual biological layers to a holistic approach that combines genomics, transcriptomics, proteomics, epigenomics, and metabolomics. This integrated strategy enables researchers to construct comprehensive models of biological systems, revealing the complex interplay between different molecular levels that underpin health and disease [1] [2]. The fundamental challenge lies in developing computational methods capable of harmonizing these diverse data types, which vary in scale, structure, and biological context, to extract meaningful biological insights that would remain hidden when analyzing each dataset independently [3].

The urgency of addressing this challenge is reflected in major research initiatives, such as the NIH's $50.3 million Multi-Omics for Health and Disease program, which recognizes the transformative potential of these approaches for precision medicine [2]. As the field rapidly advances, researchers face a critical need for objective benchmarks to navigate the growing landscape of computational integration methods and select the most appropriate tools for their specific biological questions and data types [4] [5].

Performance Benchmarking: Comparative Analysis of Integration Methods

Single-Cell Multimodal Omics Integration

Systematic benchmarking of single-cell multimodal integration methods has revealed significant performance variations across different data modalities and analytical tasks. A comprehensive 2025 evaluation of 40 integration methods categorized approaches into four prototypical integration types—vertical, diagonal, mosaic, and cross—and assessed them across seven common computational tasks [4].

Table 1: Top-Performing Single-Cell Multi-Omics Integration Methods by Data Modality

Data Modality	Top-Performing Methods	Key Strengths	Reference
RNA + ADT	Seurat WNN, sciPENN, Multigrate	Effective preservation of biological variation in cell types	[4]
RNA + ATAC	Seurat WNN, Multigrate, Matilda, UnitedNet	Strong performance across diverse datasets	[4]
RNA + ADT + ATAC	Seurat WNN, MIRA, scMoMaT	Capable of handling trimodal integration	[4]
General Prediction	totalVI, scArches (protein); LS_Lab (chromatin)	Top-performing for cross-modality prediction	[6]

For feature selection tasks, which identify molecular markers associated with specific cell types, benchmarking has shown that method performance varies significantly. Matilda and scMoMaT demonstrate strength in identifying cell-type-specific markers, while MOFA+ generates more reproducible feature selection results across different data modalities despite its limitation in selecting only cell-type-invariant markers [4].

Bulk Multi-Omics Integration for Cancer Subtyping

In bulk multi-omics integration for cancer subtyping, comprehensive benchmarking using The Cancer Genome Atlas (TCGA) data has identified several high-performing methods. A recent study evaluated twelve established machine learning methods across nine cancer types and eleven combinations of four multi-omics data types (genomics, transcriptomics, proteomics, and epigenomics) [5].

Table 2: Performance Benchmarking of Machine Learning Methods for Multi-Omics Cancer Subtyping

Method	Silhouette Score	Clinical Relevance (log-rank p-value)	Robustness (NMI)	Computational Efficiency (seconds)
iClusterBayes	0.89	0.75	0.85	180
Subtype-GAN	0.87	0.72	0.82	60
SNF	0.86	0.76	0.84	100
NEMO	0.84	0.78	0.86	80
PINS	0.82	0.79	0.83	120
LRAcluster	0.81	0.74	0.89	200

The benchmarking revealed that NEMO achieved the highest composite score (0.89), excelling in both clustering performance and clinical significance, while LRAcluster demonstrated exceptional robustness to noise, maintaining an average normalized mutual information (NMI) score of 0.89 with increasing noise levels [5]. Interestingly, the study found that using combinations of two or three omics types frequently outperformed configurations incorporating four or more types, highlighting the challenge of noise and redundancy in highly multidimensional data [5].

Spatial Multi-Omics Integration

Spatial transcriptomics technologies present unique integration challenges due to the added dimension of spatial context. A 2025 benchmarking study evaluated 12 multi-slice integration methods across 19 diverse datasets from seven technologies, including 10X Visium, MERFISH, and STARMap [7].

The evaluation revealed substantial task-dependent and data-dependent performance variations. For batch effect correction, GraphST-PASTE demonstrated superior performance (mean bASW 0.940, mean iLISI 0.713, mean GC 0.527), while MENDER, STAIG, and SpaDo excelled at preserving biological variance in spatial data [7]. The study also identified strong interdependencies between upstream integration quality and downstream application performance, emphasizing the importance of selecting methods based on specific analytical goals [7].

Experimental Protocols and Benchmarking Methodologies

Benchmarking Frameworks for Single-Cell Multimodal Data

The registered report protocol from Nature Methods outlines a comprehensive benchmarking framework for single-cell multimodal omics integration methods [4]. The experimental design incorporates multiple evaluation tasks assessed through tailored metrics:

• Dimension Reduction: Evaluated using cell-type separation metrics in low-dimensional embeddings
• Batch Correction: Assessed through batch mixing metrics while preserving biological variance
• Clustering: Measured by clustering accuracy against known cell-type labels
• Classification: Evaluated by cell-type prediction accuracy
• Feature Selection: Assessed by marker relevance and reproducibility
• Imputation: Measured by accuracy in predicting missing values
• Spatial Registration: Evaluated for spatial data alignment accuracy

The protocol employs 64 real datasets and 22 simulated datasets encompassing various modality combinations, including RNA+ADT, RNA+ATAC, and RNA+ADT+ATAC. Evaluation metrics include adjusted rand index (ARI), normalized mutual information (NMI), average silhouette width (ASW), and label transfer accuracy, providing a multi-faceted assessment of method performance [4].

Bulk Multi-Omics Cancer Subtyping Benchmarking

The benchmarking methodology for bulk multi-omics cancer subtyping utilizes TCGA data across nine cancer types, creating eleven possible combinations of four omics types [5]. The experimental protocol includes:

• Data Preprocessing: Standardized normalization and quality control across all omics datasets
• Method Evaluation: Four key performance dimensions assessed for each method
• Robustness Testing: Introduction of progressive noise levels to evaluate method stability
• Clinical Validation: Survival analysis to assess clinical relevance of identified subtypes

Performance metrics include silhouette scores for clustering quality, log-rank p-values for clinical significance, normalized mutual information (NMI) for robustness, and execution time for computational efficiency [5]. The framework employs k-fold cross-validation to ensure reliable performance estimates.

Workflow Visualization of Multi-Omics Benchmarking

The following diagram illustrates the comprehensive benchmarking workflow for multi-omics integration methods:

Multi-Omics Benchmarking Workflow

Successful multi-omics integration requires not only computational methods but also specialized data resources and analytical tools. The following table outlines key components of the multi-omics research toolkit:

Table 3: Essential Research Reagents and Computational Tools for Multi-Omics Integration

Resource Type	Specific Examples	Function and Application	Reference
Data Repositories	TCGA, CCLE, Human Cell Atlas	Provide standardized multi-omics datasets for method development and testing	[8]
ncRNA Databases	ncRNA-disease databases (Lnc2Cancer, ncRPheno)	Curated associations for non-coding RNA disease research	[9]
Analysis Platforms	GraphOmics, Flexynesis, CustOmics	Integrated environments for multi-omics data visualization and analysis	[8] [9]
Benchmarking Frameworks	iSTBench, Multi-omics method benchmarks	Standardized pipelines for method evaluation and comparison	[4] [7]
Visualization Tools	UMAP, t-SNE, spatial mapping tools	Dimensionality reduction and spatial data visualization	[4] [7]

Flexynesis represents a notable advancement in deep learning toolkits, addressing critical limitations in reusable, modular multi-omics analysis by providing standardized interfaces for data processing, feature selection, and hyperparameter tuning across diverse modeling tasks including regression, classification, and survival analysis [8]. This toolkit supports both deep learning architectures and classical machine learning methods, enabling comprehensive benchmarking within a unified framework.

Methodological Relationships and Integration Categories

The landscape of multi-omics integration methods can be categorized by their underlying computational approaches and integration strategies, as visualized in the following diagram:

Method Relationships and Categories

The benchmarking data presented in this guide reveals a consistent theme: there is no universally superior method for multi-omics data integration. Method performance is highly dependent on specific data modalities, analytical tasks, and dataset characteristics [4] [5] [7]. This context-dependent performance underscores the importance of selective method adoption based on specific research objectives rather than seeking a one-size-fits-all solution.

Several key findings emerge from current benchmarking studies. First, method performance varies significantly across different data modalities—a method excelling with RNA+ADT data may not maintain its advantage with RNA+ATAC data [4]. Second, more data does not always yield better outcomes, as evidenced by the superior performance of two or three-omics combinations compared to four or more types in cancer subtyping [5]. Third, strong interdependencies exist between upstream integration quality and downstream application performance, particularly in spatial transcriptomics [7].

As the field advances, promising directions include the development of flexible, modular frameworks like Flexynesis that support both deep learning and classical machine learning approaches [8], increased attention to model interpretability [9], and the creation of comprehensive benchmarking resources that enable researchers to select optimal methods for their specific multi-omics integration challenges.

The field of biomedical research has witnessed a paradigm shift from single-omics analyses toward integrated multi-omics approaches, driven by the recognition that complex biological systems cannot be fully understood by examining individual molecular layers in isolation. This transition is particularly evident in precision oncology and drug discovery, where molecular heterogeneity demands sophisticated analytical frameworks that can capture interactions across genomic, transcriptomic, proteomic, and epigenomic strata [10]. The integration of these diverse data types enables researchers to move beyond snapshots of individual biological processes toward system-level understanding of disease mechanisms and therapeutic responses.

Multi-omics integration faces significant computational challenges stemming from data heterogeneity, with dimensional disparities ranging from millions of genetic variants to thousands of metabolites, creating a "curse of dimensionality" that necessitates sophisticated feature reduction techniques [10]. Additional complexities include temporal heterogeneity in molecular processes, platform-specific technical variability, and pervasive missing data issues. Despite these challenges, the potential rewards are substantial, with integrated multi-omics approaches demonstrating transformative potential across the therapeutic development spectrum, from initial target identification to clinical outcome prediction and treatment optimization [11] [12].

Computational Method Benchmarking: Strategies and Performance Metrics

Benchmarking Frameworks and Evaluation Metrics

The growing diversity of multi-omics integration methods has created an urgent need for comprehensive benchmarking studies to guide researchers in selecting appropriate analytical tools. Effective benchmarking requires careful consideration of evaluation metrics that assess different aspects of method performance across multiple data processing stages. Current benchmarking efforts typically evaluate methods at three distinct levels: cell embeddings, graph structure, and final partitions, employing a suite of metrics to provide a comprehensive assessment [13].

For cancer subtyping applications, key performance metrics include clustering accuracy, clinical relevance, robustness, and computational efficiency [5]. The Adjusted Rand Index (ARI) measures similarity between computational clustering results and known biological classifications, while the Silhouette Width assesses separation between identified clusters. Clinical relevance is often evaluated through survival analysis, with methods that identify subtypes showing significant differences in patient outcomes considered more clinically meaningful [14]. Robustness measures a method's stability when dealing with noisy data, and computational efficiency evaluates scalability to large datasets.

Table 1: Key Performance Metrics for Multi-Omics Integration Methods

Metric Category	Specific Metrics	Interpretation	Ideal Value
Clustering Accuracy	Adjusted Rand Index (ARI)	Similarity between computational clusters and true biological classes	Closer to 1.0
	Silhouette Width	Measure of cluster separation and cohesion	Closer to 1.0
	Normalized Mutual Information (NMI)	Information-theoretic measure of clustering quality	Closer to 1.0
Clinical Relevance	Log-rank p-value	Significance of survival differences between subtypes	< 0.05
	Hazard Ratio	Effect size for survival differences between subtypes	> 1.0 or < 1.0
Robustness	Consistency across noise levels	Performance maintenance with added noise	Minimal degradation
Computational Efficiency	Execution time	Time required for analysis	Shorter preferred
	Memory usage	Computational resources required	Lower preferred

Performance Comparison of Integration Methods

Recent benchmarking studies have evaluated numerous multi-omics integration methods across diverse datasets and cancer types. One comprehensive assessment of twelve machine learning methods for cancer subtyping revealed that iClusterBayes achieved an impressive silhouette score of 0.89 at its optimal k, followed closely by Subtype-GAN (0.87) and SNF (0.86), indicating their strong clustering capabilities [5]. Notably, NEMO and PINS demonstrated the highest clinical significance, with log-rank p-values of 0.78 and 0.79, respectively, effectively identifying meaningful cancer subtypes with survival differences.

In robustness testing, LRAcluster emerged as the most resilient method, maintaining an average normalized mutual information (NMI) score of 0.89 even as noise levels increased, a crucial attribute for real-world data applications where technical and biological noise is inevitable [5]. For computational efficiency, Subtype-GAN stood out as the fastest method, completing analyses in just 60 seconds, while NEMO and SNF demonstrated commendable efficiency with execution times of 80 and 100 seconds, respectively. When considering overall performance across multiple metrics, NEMO ranked highest with a composite score of 0.89, showcasing its strengths in both clustering and clinical applications [5].

For single-cell chromatin data analysis, benchmarking of eight feature engineering pipelines revealed that feature aggregation, SnapATAC, and SnapATAC2 generally outperformed latent semantic indexing-based methods [13]. For datasets with complex cell-type structures, SnapATAC and SnapATAC2 were preferred, while for large datasets, SnapATAC2 and ArchR demonstrated superior scalability.

Table 2: Performance Comparison of Multi-Omics Integration Methods for Cancer Subtyping

Method	Category	Clustering Accuracy (Silhouette Score)	Clinical Relevance (Log-rank p-value)	Robustness (NMI with Noise)	Computational Efficiency (Execution Time)
NEMO	Network-based	0.84	0.78	0.85	80 seconds
iClusterBayes	Statistics-based	0.89	0.75	0.82	>300 seconds
Subtype-GAN	Deep Learning	0.87	0.72	0.80	60 seconds
SNF	Network-based	0.86	0.76	0.83	100 seconds
PINS	Consensus Clustering	0.82	0.79	0.84	120 seconds
LRAcluster	Statistics-based	0.80	0.70	0.89	150 seconds

Key Application 1: Drug Target Identification and Validation

Technological Advances and Workflows

Drug target identification represents one of the most significant applications of multi-omics integration, with technological advances enabling a systematic approach to discovering and validating therapeutic targets. The traditional reliance on single-omics technologies has progressively shifted toward integrated multi-omics techniques, as it has become increasingly apparent that no single omics level can adequately elucidate the causal connections between drugs and the emergence of complex phenotypes [11]. This evolution has been facilitated by progress in large-scale sequencing and the development of high-throughput technologies that simultaneously capture multiple molecular dimensions.

Multi-omics approaches are now utilized throughout the drug discovery process, addressing challenges in target identification, target validation, and preclinical development [12]. In target identification, techniques such as laser-capture microdissection coupled with RNA sequencing enable characterization of rare cell populations, as demonstrated in schizophrenia research where this approach identified parvalbumin interneurons and specifically pinpointed GluN2D as a potential drug target [12]. Proteogenomic integration further enhances this process by connecting genomic alterations with their functional protein-level consequences, providing stronger evidence for target-disease relationships.

Diagram 1: Multi-omics drug target identification workflow

Experimental Protocols and Applications

A representative experimental protocol for target identification integrates genomic, transcriptomic, and epigenomic data from patient samples to pinpoint dysregulated pathways and novel therapeutic targets. The process begins with sample preparation using techniques such as laser-capture microdissection to isolate specific cell populations of interest, followed by multi-omic profiling through RNA sequencing, ATAC-seq for chromatin accessibility, and whole-genome or exome sequencing [12]. The resulting data undergoes computational integration using methods such as network-based approaches that map multiple omics datasets onto shared biochemical networks to improve mechanistic understanding [1].

In one exemplar application, researchers used this approach to identify GluN2D as a potential target for schizophrenia treatment. After identifying parvalbumin interneurons as crucial players in the disease pathology, they employed laser-capture microdissection followed by RNA-seq to characterize the druggable transcriptome of this rare cell population [12]. Integration of this transcriptomic data with genomic association studies enabled prioritization of GluN2D as a specific target within the glutamate receptor system. This case highlights how multi-omics integration can overcome the limitations of bulk tissue analysis and enable target discovery in specific cellular subpopulations.

Key Application 2: Cancer Subtyping and Molecular Stratification

Methodologies and Data Combinations

Cancer subtyping represents one of the most mature applications of multi-omics integration, addressing the critical need to decompose cancer heterogeneity into molecularly distinct subgroups with clinical relevance. The foundational principle is that different omics layers provide complementary information that collectively enables more accurate classification of cancer subtypes than any single data type alone. Genomics identifies DNA-level alterations including single-nucleotide variants and copy number variations; transcriptomics reveals gene expression dynamics; epigenomics characterizes DNA methylation and chromatin accessibility; and proteomics catalogs functional effectors of cellular processes [10].

Benchmarking studies have yielded the surprising finding that more data does not always equate to better outcomes; in fact, using combinations of two or three omics types frequently outperformed configurations that included four or more types due to the introduction of increased noise and redundancy [5]. Specifically, certain combinations have demonstrated particular effectiveness: genomics + transcriptomics, genomics + epigenomics, and transcriptomics + proteomics combinations consistently showed strong performance across multiple cancer types [14]. This counterintuitive finding highlights the importance of strategic data selection rather than simply maximizing data types.

Evaluation Frameworks and Clinical Translation

Robust evaluation of cancer subtyping methods requires frameworks that assess both computational performance and clinical relevance. A comprehensive benchmarking study evaluated ten representative integration methods across nine cancer types from TCGA, considering all eleven possible combinations of four multi-omics data types [14]. The evaluation encompassed clustering accuracy measured by how well methods recapitulated known biological classifications, clinical significance assessed through survival analysis and correlation with clinical parameters, robustness to noise and data perturbations, and computational efficiency including runtime and memory requirements.

The clinical translation of multi-omics subtyping is particularly evident in oncology, where molecular stratification now guides standard care protocols. In breast cancer, ESR1 mutations direct endocrine therapy selection; in non-small cell lung cancer, EGFR/ALK alterations predict tyrosine kinase inhibitor efficacy; and in diffuse large B-cell lymphoma, cell-of-origin transcriptomic subtyping informs chemotherapy response [10]. Importantly, multi-omics approaches can reveal resistance mechanisms that single-modality biomarkers miss, such as parallel pathway activation or epigenetic remodeling that drives resistance to targeted therapies like KRAS G12C inhibitors [10].

Key Application 3: Clinical Outcome Prediction and Predictive Allocation

Predictive Modeling and Validation

Clinical outcome prediction represents a frontier application of multi-omics integration, moving beyond descriptive classification toward prognostic forecasting and treatment response prediction. Artificial intelligence approaches, particularly machine learning and deep learning, have emerged as essential scaffolds bridging multi-omics data to clinical predictions by identifying non-linear patterns across high-dimensional spaces [10]. For example, convolutional neural networks automatically quantify immunohistochemistry staining with pathologist-level accuracy, graph neural networks model protein-protein interaction networks perturbed by somatic mutations, and multi-modal transformers fuse MRI radiomics with transcriptomic data to predict glioma progression [10].

A critical advancement in this domain is the concept of predictive allocation, a two-stage approach where outcome models first derive expected treatment benefit, followed by treatment assignment based on minimizing the individual's probability of experiencing a negative outcome [15]. This approach addresses the fundamental limitation of traditional evidence-based medicine, which relies on population-level evidence from randomized clinical trials that overlook heterogeneity of treatment effects between individuals. Simulation studies using data from pediatric cardiology trials demonstrated that predictive allocation could yield absolute risk reductions of 13.8-15.6%, corresponding to numbers needed to treat of 6.4-7.3, significantly improving upon guideline-based therapy [15].

Diagram 2: Predictive allocation for treatment optimization

Implementation Considerations and Challenges

The implementation of multi-omics predictive models in clinical settings faces several significant challenges. Data harmonization issues arise when integrating multi-omics data from different cohorts and laboratories, complicating integration [1]. The "four Vs" of big data—volume, velocity, variety, and veracity—pose formidable analytical challenges, with volume overwhelming conventional biostatistics as dimensionality dwarfs sample sizes in most cohorts [10]. Additionally, model generalizability remains a concern, as performance often degrades when applied to independent datasets due to batch effects, population differences, and technical variability.

Explainable AI techniques such as SHapley Additive exPlanations adress the "black box" nature of complex models by clarifying how genomic variants contribute to clinical outcome predictions [10]. The net benefit of predictive allocation is directly proportional to the performance of the prediction models and disappears as model performance degrades below an area under the curve of 0.55, highlighting the importance of robust model development and validation [15]. Emerging approaches to these challenges include federated learning for privacy-preserving multi-institutional collaboration, quantum computing for enhanced computational scalability, and patient-centric "N-of-1" models that signal a paradigm shift toward dynamic, personalized cancer management [10].

Successful multi-omics integration requires both wet-lab reagents for data generation and computational tools for analysis. The table below details key resources essential for conducting multi-omics studies across application domains.

Table 3: Essential Research Reagents and Computational Resources for Multi-Omics Studies

Category	Specific Resource	Function/Application	Key Features
Sequencing Technologies	scATAC-seq	Profiling chromatin accessibility at single-cell resolution	Identifies open chromatin regions
	scCUT&Tag	Mapping histone modifications and transcription factor binding	Low background noise, high sensitivity
	Whole Genome Sequencing (WGS)	Comprehensive genomic variant detection	Identifies SNVs, CNVs, structural variants
	Spatial Transcriptomics	Mapping gene expression in tissue context	Preserves spatial localization information
Computational Tools	Signac	scATAC-seq analysis	Latent Semantic Indexing approach
	ArchR	scATAC-seq analysis	Iterative LSI, scalability to large datasets
	SnapATAC2	scATAC-seq analysis	Laplacian eigenmaps, handles complex structures
	NEMO	Multi-omics integration	Network-based, high clinical relevance
	iClusterBayes	Multi-omics integration	Statistics-based, high clustering accuracy
	Subtype-GAN	Multi-omics integration	Deep learning-based, high computational efficiency
Data Resources	The Cancer Genome Atlas (TCGA)	Pan-cancer multi-omics reference dataset	Genomic, epigenomic, transcriptomic data
	100,000 Genomes Project	Genomic-phenotypic dataset	Links genomic variants with clinical outcomes

The integration of multi-omics data represents a transformative approach in biomedical research, with key applications in drug target identification, cancer subtyping, and clinical outcome prediction driving methodological innovation. Benchmarking studies have provided critical insights into the performance characteristics of diverse integration methods, revealing that network-based approaches like NEMO often excel in clinical relevance, while statistics-based methods like iClusterBayes demonstrate strong clustering accuracy, and deep learning approaches like Subtype-GAN offer superior computational efficiency [5] [14].

Future methodological development will likely focus on several key areas: improved scalability to handle increasingly large datasets; enhanced interpretability through explainable AI techniques; better management of missing data through advanced imputation strategies; and more effective data harmonization to integrate disparate data sources [1] [10]. As the field progresses, the combination of multi-omics integration with emerging technologies such as spatial profiling, liquid biopsies, and real-time monitoring promises to further advance personalized medicine, ultimately enabling more precise diagnostic, prognostic, and therapeutic strategies tailored to individual molecular profiles.

The Critical Need for Standardized Benchmarking in a Rapidly Evolving Field

The field of multi-omics data integration is experiencing unprecedented growth, driven by advances in high-throughput technologies that generate complex, multi-dimensional biological data. This explosion of data has led to a proliferation of computational methods designed to integrate different omics layers—including genomics, transcriptomics, epigenomics, and proteomics—to uncover comprehensive biological insights. However, this rapid innovation has created a significant challenge: the lack of standardized benchmarking frameworks to objectively evaluate and compare these methods. Without consistent evaluation standards, researchers struggle to select appropriate integration methods for their specific biological questions and data types, potentially compromising scientific conclusions and hindering translational applications.

This comparison guide examines the current landscape of benchmarking studies for multi-omics integration methods, synthesizing quantitative performance data across different methodologies and applications. By providing structured comparisons of method performance, experimental protocols, and key resources, we aim to equip researchers with the evidence needed to navigate this complex field and make informed methodological choices for their multi-omics studies.

Method Categories and Performance Benchmarks

Computational methods for multi-omics integration employ diverse strategies, each with distinct strengths and limitations. Understanding these categories provides essential context for interpreting benchmarking results and selecting appropriate methods for specific research objectives.

Table 1: Multi-omics Integration Method Categories and Characteristics

Category	Strengths	Limitations	Typical Applications
Network-based [14]	Robust to missing data, represents complex relationships	Sensitive to similarity metrics, may require extensive tuning	Disease subtyping, patient similarity analysis, regulatory mechanisms
Statistics-based [14] [16]	Interpretable, captures uncertainty, probabilistic inference	Computationally intensive, may require strong model assumptions	Disease subtyping, latent factor discovery, biomarker identification
Deep Learning-based [17] [16]	Learns complex nonlinear patterns, supports missing data and denoising	High computational demands, limited interpretability, requires large datasets	High-dimensional integration, data imputation, disease subtyping
Matrix Factorization [16]	Efficient dimensionality reduction, identifies shared and specific factors	Assumes linearity, doesn't explicitly model uncertainty	Disease subtyping, molecular pattern identification, biomarker discovery

Performance Benchmarks Across Applications

Recent benchmarking studies have evaluated method performance across different data types and analytical tasks. The results demonstrate significant performance variation depending on application context, data modalities, and specific tasks.

Table 2: Performance Benchmarks for Single-Cell Multi-omics Integration Methods [4]

Method Category	Data Modalities	Top-Performing Methods	Key Performance Metrics
Vertical Integration	RNA + ADT	Seurat WNN, sciPENN, Multigrate	iF1, NMIcellType, ASWcellType, iASW
Vertical Integration	RNA + ATAC	Seurat WNN, Multigrate, Matilda	iF1, NMIcellType, ASWcellType, iASW
Vertical Integration	RNA + ADT + ATAC	Seurat WNN, MIRA, scMoMaT	iF1, NMIcellType, ASWcellType, iASW
Feature Selection	RNA + ADT	Matilda, scMoMaT, MOFA+	Marker correlation, clustering, and classification accuracy

Table 3: Performance of Deep Learning-based Multi-omics Methods in Cancer Applications [17]

Method	Classification Performance	Clustering Performance	Key Strengths
moGAT	Best classification performance (Accuracy, F1 macro, F1 weighted)	Moderate	Effective for prediction tasks
efmmdVAE, efVAE, lfmmdVAE	Moderate	Most promising performance across clustering contexts	Effective for patient stratification
lfAE, efAE	Variable	Variable	Architecture flexibility

For cancer subtyping using bulk multi-omics data, benchmarking has revealed a critical insight: incorporating more types of omics data does not always improve performance [14]. Counter to widespread intuition, there are situations where integrating additional omics data negatively impacts integration method performance, highlighting the importance of selecting optimal data combinations rather than maximizing data type quantity.

Experimental Protocols for Benchmarking

Standardized benchmarking requires carefully designed experimental protocols that assess methods across multiple performance dimensions. Comprehensive evaluations typically examine accuracy, robustness, computational efficiency, and scalability using diverse datasets with known ground truth.

Dataset Construction and Curation

Benchmarking studies employ multiple dataset types to evaluate different aspects of method performance:

• Simulated Datasets: Allow controlled evaluation against known ground truth, but may lack the complex latent structure of real biological data [4].
• Real Biological Datasets: Provide authentic evaluation contexts but may have incomplete ground truth. Common sources include:
  • The Cancer Genome Atlas (TCGA): Provides bulk multi-omics data for various cancer types [14] [18].
  • Single-cell Multimodal Omics Datasets: Include paired RNA+ADT, RNA+ATAC, and trimodal RNA+ADT+ATAC data from technologies like CITE-seq and SHARE-seq [4].
  • Spatial Transcriptomics Datasets: Capture gene expression within spatial tissue context from technologies like 10X Visium, MERFISH, and STARmap [7].

Evaluation Metrics and Frameworks

Comprehensive benchmarking employs multiple task-specific metrics to evaluate different aspects of method performance:

Multi-task Evaluation Framework

For spatial transcriptomics, specialized benchmarking frameworks evaluate methods across four key tasks: multi-slice integration, spatial clustering, spatial alignment, and slice representation [7]. Performance in upstream tasks (like integration) strongly influences downstream application success, highlighting the importance of evaluating method performance across the entire analytical workflow.

Research Reagent Solutions

Successful multi-omics benchmarking requires both computational tools and data resources. The following essential components form the foundation of rigorous method evaluation.

Table 4: Essential Research Reagents for Multi-omics Benchmarking

Resource Category	Specific Tools/Platforms	Primary Function	Application Context
Data Resources	TCGA [14] [18], ICGC [16], CITE-seq data [4]	Provide standardized multi-omics datasets for benchmarking	Cancer subtyping, method validation, cross-platform comparison
Benchmarking Frameworks	CMOB [18], iSTBench [7]	Offer curated datasets, tasks, and baseline evaluations	Large-scale method comparison, standardized performance assessment
Evaluation Metrics	bASW, iLISI, dASW, ARI, NMI [4] [7]	Quantify specific performance aspects (batch correction, biological conservation, clustering accuracy)	Performance benchmarking across diverse tasks and data types
Complementary Resources	STRING [18], Clinical health records [18]	Provide biological context and clinical correlation	Biological validation, clinical translation assessment

The field of multi-omics data integration has progressed beyond simply developing new methods to focusing on rigorous, standardized evaluation. Benchmarking studies have revealed that method performance is highly context-dependent, varying significantly with data types, analytical tasks, and biological applications. No single method consistently outperforms others across all scenarios, emphasizing the need for task-specific method selection.

Future benchmarking efforts must address several critical challenges: the rapid pace of method development, the growing diversity of omics technologies, and the need for biologically relevant evaluation criteria. Community-wide adoption of standardized benchmarking frameworks, shared datasets, and reproducible evaluation pipelines will accelerate methodological advances and enhance the reliability of biological insights derived from multi-omics data integration.

In computational biology, the proliferation of single-cell and spatial multi-omics technologies has necessitated the development of sophisticated data integration methods. These methods enable researchers to jointly analyze diverse molecular measurements, providing a more comprehensive understanding of cellular systems. Based on input data structure and modality combination, integration tasks are systematically categorized into four prototypical scenarios: vertical, diagonal, mosaic, and cross integration [4] [19]. This classification framework helps researchers navigate the complex landscape of computational tools by precisely defining the relationships between datasets across batches (sources) and modalities (measurement types) [19].

The terminology originates from how datasets are arranged in a conceptual grid where rows represent different modalities and columns represent different batches [20]. Vertical integration involves multiple modalities measured on the same set of cells or samples. Horizontal integration addresses batch effects when the same modality is measured across different batches. Diagonal integration handles cases where neither cells nor modalities are shared between data matrices. Mosaic integration represents the most general case, accommodating any combination of the other scenarios [20] [19]. Understanding these categories is essential for selecting appropriate computational methods that can handle the specific data relationships present in a research study.

Defining the Integration Categories

Theoretical Framework and Definitions

The four primary integration categories are defined by the specific relationships between datasets in terms of shared cells and shared features [19]:

• Vertical Integration (VI): Each dataset contains a set of measurements carried out on the same set of samples (separate bulk experiments with matched samples in different modalities or single-cells measured through joint assays) [19]. VI identifies links between biological features, such as scRNA-seq transcript counts and scATAC-seq peaks, which can help formulate mechanistic hypotheses across modalities [19].

• Diagonal Integration (DI): DI describes the framework where each dataset is measured in a different biological modality, and there is no shared set of cells or samples across these datasets [19]. This represents a more challenging scenario than vertical integration due to the lack of direct correspondence between the measured entities.

• Mosaic Integration (MI): MI allows pairs of datasets to be measured in overlapping modalities in any combination [19]. It is the most general and challenging case, accommodating any subset of data matrices from an m × b grid corresponding to m modalities and b batches [20]. Few methods have been developed specifically for this comprehensive scenario [20].

• Cross Integration: Building upon this framework, some benchmarking studies further specify a "cross" integration category. In one extensive benchmark, cross integration was evaluated alongside vertical, diagonal, and mosaic integration, with 15 methods assessed for this specific task [4].

Visual Framework of Data Integration Categories

The following diagram illustrates the conceptual relationships between the four primary data integration categories:

Benchmarking Experimental Design and Protocols

Comprehensive Evaluation Framework

Systematic benchmarking of data integration methods requires a rigorously designed experimental protocol that can objectively assess performance across diverse scenarios. A landmark Registered Report in Nature Methods established a comprehensive framework for multitask benchmarking of single-cell multimodal omics integration methods [4]. This protocol was accepted in principle on 30 July 2024, ensuring methodological rigor through peer review before result collection [4].

The benchmarking study evaluated 40 integration methods across the four data integration categories: 18 vertical integration methods, 14 diagonal integration methods, 12 mosaic integration methods, and 15 cross integration methods [4]. These methods were tested on 64 real datasets and 22 simulated datasets representing various modality combinations, including paired RNA and ADT (RNA+ADT), paired RNA and ATAC (RNA+ATAC), and trimodal data containing all three modalities (RNA+ADT+ATAC) [4]. This extensive design ensures robust evaluation across diverse biological contexts and technical challenges.

Evaluation Metrics and Tasks

The benchmarking framework assessed method performance across seven common computational tasks that integration methods are designed to address [4]:

• Dimension reduction: Evaluating the preservation of biological variation in low-dimensional embeddings
• Batch correction: Assessing the removal of technical artifacts while preserving biological signals
• Clustering: Measuring the ability to identify biologically meaningful cell groups
• Classification: Testing performance in predicting cell type labels
• Feature selection: Evaluating identification of molecular markers associated with cell types
• Imputation: Assessing reconstruction of missing data values
• Spatial registration: Testing alignment of spatial coordinates in spatial transcriptomics

For each task, tailored evaluation metrics were employed. For dimension reduction and clustering, metrics included iF1 (clustering accuracy), NMIcellType (Normalized Mutual Information), ASWcellType (Average Silhouette Width), and iASW (integration ASW) [4]. Feature selection performance was evaluated using clustering, classification, and reproducibility metrics applied to selected marker features [4].

Performance Comparison Across Integration Categories

Vertical Integration Performance

Vertical integration methods were systematically benchmarked on dimension reduction and clustering tasks using datasets of varying modalities. The evaluation included 14 methods on 13 paired RNA and ADT datasets, 14 methods on 12 paired RNA and ATAC datasets, and 5 methods on 4 trimodal datasets (RNA+ADT+ATAC) [4].

Table 1: Top-Performing Vertical Integration Methods by Data Modality

Rank	RNA+ADT Methods	RNA+ATAC Methods	RNA+ADT+ATAC Methods
1	Seurat WNN	UnitedNet	Multigrate
2	sciPENN	Seurat WNN	Seurat WNN
3	Multigrate	Multigrate	Matilda
4	Matilda	scMoMaT	scMoMaT
5	BREMSC	Matilda	totalVI

Performance analysis revealed that method effectiveness is both dataset-dependent and modality-dependent [4]. For RNA+ADT data, Seurat WNN, sciPENN, and Multigrate demonstrated generally better performance, effectively preserving biological variation of cell types [4]. In RNA+ATAC integration, UnitedNet, Seurat WNN, and Multigrate performed well across diverse datasets [4]. For the more challenging trimodal integration, Multigrate, Seurat WNN, and Matilda emerged as top performers [4].

For feature selection tasks in vertical integration, only Matilda, scMoMaT, and MOFA+ support identification of molecular markers from single-cell multimodal omics data [4]. Notably, Matilda and scMoMaT identify distinct markers for each cell type, while MOFA+ selects a single cell-type-invariant set of markers for all cell types [4].

Diagonal and Mosaic Integration Performance

Diagonal and mosaic integration present more challenging scenarios due to limited shared information across datasets. Benchmarking results indicate that performance varies significantly based on data complexity and method design:

Table 2: Performance Leaders in Diagonal and Mosaic Integration

Integration Category	Top-Performing Methods	Key Strengths
Diagonal Integration	totalVI, UINMF, MOJITOO, scAI	Effective integration of different cells and different modalities
Mosaic Integration	totalVI, UINMF, scMoMaT	Handles mixed shared/different cells and modalities
Cross Integration	Methods specifically benchmarked for cross integration tasks	Performance dependent on data complexity

For diagonal integration, which involves different cells and different modalities, totalVI and UINMF excel beyond their counterparts according to benchmarking studies [21]. Another benchmark showed that MOJITOO and scAI also emerge as leading algorithms for vertical integration scenarios [21].

Mosaic integration, being the most general case, requires methods that can handle arbitrary combinations of data matrices. scMoMaT (single cell Multi-omics integration using Matrix Tri-factorization) specifically addresses this challenge using a matrix tri-factorization framework that can integrate an arbitrary number of data matrices under the mosaic integration scenario [20]. The method simultaneously learns cell representations and marker features across modalities for different cell clusters, allowing interpretation of cell clusters from different modalities [20].

Method Performance Across Multiple Tasks

The comprehensive nature of the benchmarking reveals that few methods excel across all tasks. The following table summarizes the performance of selected top methods across key integration tasks:

Table 3: Multi-Task Performance of Leading Integration Methods

Method	Dimension Reduction	Batch Correction	Clustering	Feature Selection	Data Modalities
Seurat WNN	Excellent	Good	Excellent	Not supported	RNA+ADT, RNA+ATAC
Multigrate	Excellent	Good	Excellent	Limited	RNA+ADT, RNA+ATAC, Multi-modal
Matilda	Good	Good	Good	Excellent	RNA+ADT, RNA+ATAC, Multi-modal
scMoMaT	Good	Good	Good	Excellent	Mosaic integration
totalVI	Good	Excellent	Good	Limited	Diagonal, Mosaic

Performance assessments indicate that while Seurat WNN performs well on dimension reduction and clustering tasks, it does not support feature selection [4]. In contrast, Matilda and scMoMaT provide robust feature selection capabilities, identifying cell-type-specific markers that lead to better clustering and classification of cell types than markers selected by MOFA+ [4]. The evaluations also demonstrated that dataset complexity significantly affects integration performance, with simulated datasets (which may lack latent data structure observed in real data) often being easier to integrate [4].

Research Reagent Solutions for Data Integration

Successful implementation of data integration methods requires appropriate computational tools and frameworks. The following essential "research reagents" represent key resources used in the field:

• scMoMaT: A computational method designed for mosaic integration using matrix tri-factorization [20]. It jointly performs single-cell mosaic integration and interprets results using multi-modal biomarkers [20].

• Seurat WNN: A widely used method for vertical integration that employs weighted nearest neighbor approaches to combine multiple modalities [4] [21]. It demonstrates strong performance in dimension reduction and clustering tasks.

• Multigrate: A versatile integration method that performs well across multiple modality combinations, including trimodal data (RNA+ADT+ATAC) [4].

• totalVI: A top-performing method for diagonal and mosaic integration scenarios, employing deep generative modeling to integrate multimodal data [21].

• UINMF: An integration method that extends iNMF by adding an unshared weights matrix term, enabling it to incorporate features belonging to only one or a subset of omics datasets and perform mosaic integration [16].

• Matilda: A vertical integration method that supports feature selection of molecular markers from single-cell multimodal omics data, capable of identifying distinct markers for each cell type [4].

• Benchmarking Frameworks: Standardized evaluation protocols, such as the Registered Report methodology [4], and specialized benchmarking pipelines for assessing multi-slice integration in spatial transcriptomics [7].

• Synthetic Data Generation Tools: Approaches like the synthpop R package that generate synthetic data through classification and regression trees (CART) methods, useful for evaluating data integration utility while addressing privacy concerns [22].

Based on the comprehensive benchmarking evidence, selecting appropriate data integration methods requires careful consideration of both the data structure (defining the integration category) and the specific analytical tasks. For vertical integration tasks involving paired multi-omics data, Seurat WNN and Multigrate consistently demonstrate strong performance across multiple modalities [4]. For studies requiring feature selection alongside integration, Matilda and scMoMaT provide superior capabilities for identifying cell-type-specific markers [4].

For the more challenging diagonal and mosaic integration scenarios, totalVI and UINMF excel according to comparative benchmarks [21]. Specifically for mosaic integration, scMoMaT offers a specialized solution using matrix tri-factorization that can handle arbitrary combinations of data matrices while simultaneously learning multi-modal biomarkers for cell type interpretation [20].

The performance evaluations consistently show that method effectiveness is context-dependent, varying by data modalities, dataset complexity, and the specific analytical tasks required [4]. Researchers should therefore consider their specific data characteristics and analytical goals when selecting integration methods, potentially consulting updated benchmarking studies as the field rapidly evolves. The emergence of comprehensive benchmarking frameworks [4] [7] provides valuable guidance for method selection, but researchers should validate performance on their specific data types to ensure optimal results.

A Taxonomy of Integration Methods: From Network Biology to Ensemble Machine Learning

The integration of multi-omics data represents a cornerstone of modern computational biology, enabling unprecedented insights into complex disease mechanisms and accelerating therapeutic discovery. Network-based approaches provide a powerful framework for this integration by contextualizing disparate molecular data within the interconnected structure of biological systems. These methods effectively map heterogeneous omics data—including genomics, transcriptomics, proteomics, and metabolomics—onto underlying biological networks such as protein-protein interactions, metabolic pathways, and gene regulatory networks. This systematic review objectively compares the performance of three principal computational families—network propagation, graph neural networks (GNNs), and network inference models—within the specific application domain of multi-omics integration for drug discovery. By synthesizing experimental data and benchmarking results, this guide aims to equip researchers with the evidence necessary to select appropriate methodologies for specific research scenarios, ultimately enhancing the efficacy of computational strategies in biomedical research and development.

Network-based multi-omics integration methods can be systematically categorized into distinct classes based on their underlying algorithmic principles and applications in drug discovery [23]. This classification framework provides researchers with a structured understanding of the methodological landscape.

• Network Propagation/Diffusion: These algorithms integrate information from input data by spreading node signals across connected neighbors in a given biological network. They function as powerful regularization approaches that amplify network regions enriched for phenotype-associated molecules while dampening technical noise and biological variation [24]. Popular implementations include Random Walk with Restart (RWR) and Heat Diffusion (HD) models, which redistribute molecular measurements (e.g., gene expression changes) through protein-protein interaction or gene co-expression networks to identify conditionally altered subnetworks.

• Graph Neural Networks (GNNs): GNNs leverage deep learning architectures to learn node representations by recursively aggregating feature information from neighboring nodes through message-passing mechanisms. Unlike traditional propagation methods, GNNs can integrate multiple graph-structured prior knowledge sources simultaneously and learn task-specific representations in an end-to-end fashion [25]. Recent innovations include frameworks like GNNRAI, which uses GNNs to model correlation structures among omics features, and MPK-GNN, which incorporates multiple prior biological networks.

• Network Inference Models: These methods focus on reconstructing the map of interactions among a system's constituents by resolving dependencies from experimental readouts [26]. They include statistical approaches (correlation, mutual information), information-theoretic methods (ARACNe, CLR), and graphical models (Bayesian networks) that infer regulatory relationships from multi-omics data, effectively building networks de novo rather than propagating signals through pre-defined networks.

Table 1: Methodological Classification of Network-Based Multi-Omics Approaches

Category	Core Principle	Representative Algorithms	Typical Applications
Network Propagation	Spreading node scores to neighbors in pre-defined networks	RWR, Heat Diffusion, Network Smoothing	Gene prioritization, Functional module identification, Noise reduction
Graph Neural Networks	Message-passing neural networks on graph structures	GNNRAI, MPK-GNN, MOGONET	Patient classification, Biomarker identification, Drug response prediction
Network Inference	Reconstructing interaction networks from data	ARACNe, GENIE3, Bayesian Networks	Regulatory network reconstruction, Mechanism elucidation, Novel interaction discovery

Performance Benchmarking and Comparative Analysis

Quantitative Performance Metrics Across Methodologies

Rigorous benchmarking of computational methods requires standardized evaluation using multiple performance metrics. The following comparative analysis synthesizes experimental results from recent studies to provide objective performance assessments.

Table 2: Performance Comparison of Network-Based Methods on Multi-Omics Tasks

Method Category	Specific Method	Application Context	Performance Metrics	Key Findings
Graph Neural Networks	GNNRAI [27]	Alzheimer's disease classification (ROSMAP cohort)	Accuracy: 2.2% improvement over benchmarks	Outperformed MOGONET; Effective integration of transcriptomics and proteomics
Graph Neural Networks	MPK-GNN [25]	Cancer molecular subtype classification	State-of-the-art performance vs. multi-view learning	Successfully integrated multiple prior biological networks
Network Propagation	RWR vs. HD [24]	Aging studies in rat brain/liver; Prostate cancer	Parameter optimization critical for performance	Maximizing inter-omics agreement improved biological consistency
Network Inference	DOMINO [28]	Disease module identification	Improved information exploitation from expression data	Identified disjoint connected Steiner trees with over-represented active genes

Task-Specific Performance Considerations

Different network-based approaches demonstrate variable efficacy depending on the specific bioinformatics task and data characteristics:

• Drug Target Identification: Network propagation excels in prioritizing disease-associated genes and proteins by diffusing known disease signals through molecular interaction networks. The optimal parameterization of propagation algorithms can be achieved by maximizing the agreement between different omics layers (e.g., proteome and transcriptome) or by maximizing the consistency between biological replicates [24]. Methods like SigMod and IODNE implement aggregate scoring approaches to identify optimally enriched disease modules within protein-protein interaction networks [28].

• Drug Response Prediction: GNNs demonstrate superior performance in predicting patient-specific drug responses by integrating multi-omics profiles with prior knowledge graphs. The GNNRAI framework showed particular effectiveness in balancing the greater predictive power of proteomics with the larger sample size available for transcriptomics in the ROSMAP cohort [27]. The method's architecture accommodates samples with incomplete omics measurements, preventing reduction in statistical power.

• Drug Repurposing: Network inference methods facilitate drug repurposing by reconstructing condition-specific networks that reveal novel mechanistic relationships. Approaches that leverage de novo network enrichment (DNE) can identify connected subnetworks of the human interactome that link known drug targets to new disease indications [28]. Methods like PCSF and Omics Integrator have been successfully applied to link drugs to new therapeutic applications through multi-omics integration.

Experimental Protocols and Methodologies

Benchmarking Framework for Multi-Omics Integration Methods

Standardized evaluation protocols are essential for meaningful comparison across different network-based approaches. The following experimental framework has emerged as a consensus methodology in computational biology:

Data Preparation and Preprocessing:

• Collect multi-omics datasets with minimum of two omics layers (e.g., transcriptomics and proteomics)
• Apply appropriate normalization techniques to address technical variation between platforms
• Map molecular entities to standardized identifiers for network integration
• Split data into training/validation sets using cross-validation (typically 3-fold)

Network Resource Curation:

• Compile relevant biological networks from databases (e.g., STRING, Pathway Commons)
• For GNN approaches, construct prior knowledge graphs representing biological relationships
• For inference methods, establish gold-standard networks for validation

Model Training and Validation:

• Implement method-specific parameter optimization procedures
• For propagation methods: optimize spreading coefficients using bias-variance tradeoff or inter-omics agreement
• For GNNs: Train with modality-specific feature extractors and representation alignment
• For inference methods: Apply appropriate statistical tests and multiple testing corrections

Performance Assessment:

• Evaluate predictive accuracy using standard metrics (AUROC, AUPR, F-score)
• Assess biological relevance through enrichment analysis and literature validation
• Compare computational efficiency and scalability

Case Study: GNNRAI Framework for Alzheimer's Disease Classification

The GNNRAI (GNN-derived Representation Alignment and Integration) framework exemplifies a rigorous experimental approach for supervised multi-omics integration [27]:

Experimental Design:

• Data Source: Religious Order Study/Memory Aging Project (ROSMAP) cohort
• Omics Modalities: Transcriptomics and proteomics from dorsolateral prefrontal cortex
• Sample Characteristics: 228 samples with both modalities, plus additional samples with single modalities
• Biological Priors: 16 Alzheimer's disease biodomains with co-expression relationships from protein-protein interaction databases

Methodological Implementation:

• Constructed separate graphs for each biodomain and modality
• Implemented GNN-based feature extractors to process each omics modality
• Aligned low-dimensional embeddings across modalities using representation alignment
• Integrated aligned representations using set transformer for final prediction
• Employed integrated gradients for biomarker identification

Validation Approach:

• Three-fold cross-validation for robust performance estimation
• Comparison against MOGONET as benchmark method
• Evaluation of both predictive accuracy and biomarker relevance

Diagram Title: GNNRAI Multi-Omics Integration Workflow

Successful implementation of network-based multi-omics analysis requires access to specific computational resources, biological datasets, and software tools. The following table catalogs essential "research reagents" for this domain.

Table 3: Essential Research Reagents for Network-Based Multi-Omics Analysis

Resource Category	Specific Resource	Function and Application
Biological Network Databases	STRING, Pathway Commons	Provide protein-protein interaction networks for propagation and prior knowledge
Omics Data Repositories	TCGA, GEO, ROSMAP	Source of multi-omics datasets for model training and validation
Software Libraries	PyTor Geometric, DGL	Graph neural network implementation frameworks
Propagation Algorithms	BioNetSmooth, NetworkX	Implement network propagation and smoothing operations
Inference Tools	ARACNe, GENIE3	Reconstruct regulatory networks from expression data
Benchmarking Suites	Open Graph Benchmark	Standardized datasets for method comparison
Visualization Tools	Cytoscape, Gephi	Visualization and exploration of biological networks

This comprehensive comparison of network-based approaches for multi-omics integration reveals a dynamic methodological landscape where each major category offers distinct advantages for specific applications in drug discovery research. Network propagation methods provide computationally efficient signal amplification and noise reduction, particularly valuable for gene prioritization and functional module identification. Graph neural networks demonstrate superior predictive performance in classification tasks and biomarker discovery, especially when integrating multiple prior knowledge sources. Network inference approaches excel in reconstructing novel regulatory relationships and elucidating disease mechanisms from high-dimensional omics data.

The benchmarking data presented indicates that methodological selection should be guided by specific research objectives, data characteristics, and computational resources. While GNNs generally achieve higher predictive accuracy, they require larger sample sizes and more computational intensive training procedures. Network propagation offers greater interpretability and computational efficiency, making it suitable for exploratory analysis. Network inference methods remain essential for hypothesis generation and mechanistic insight.

Future methodological development should focus on several critical challenges: improving computational scalability for large-scale multi-omics datasets, enhancing model interpretability for biological insight, establishing standardized evaluation frameworks, and incorporating temporal and spatial dynamics of biological systems. The emerging trend of hybrid models that combine elements from multiple approaches—such as GNNs with explainable propagation mechanisms or inference methods with deep learning components—represents a promising direction for advancing network-based multi-omics integration in biomedical research.

Multi-omics data integration represents a transformative approach in biomedical research, enabling a comprehensive understanding of complex biological systems by combining genomic, transcriptomic, proteomic, and metabolomic information [1]. The simultaneous analysis of these complementary biological layers provides unprecedented opportunities for modeling patient disease states, understanding underlying disease mechanisms, and predicting clinical outcomes with enhanced accuracy [29]. However, the integration of multi-modal, multi-omics data presents significant computational challenges, including high dimensionality, dataset heterogeneity, and the "big P, small N" problem where features vastly outnumber samples [30] [29].

Ensemble machine learning methods have emerged as powerful tools for addressing these challenges through late integration strategies that combine predictions from multiple models or data modalities [29]. These techniques—including voting ensembles, meta-learners, and boosted methods—leverage complementary information from different omics layers to improve the accuracy and stability of clinical outcome predictions in multi-class classification problems [31]. This guide provides a comprehensive benchmarking comparison of these ensemble approaches, offering researchers experimentally-validated insights for selecting appropriate methods based on specific multi-omics data integration needs.

Core Ensemble Machine Learning Strategies for Multi-Omics Integration

Late Integration: A Flexible Framework for Multi-Modal Data

Late integration, also known as decision-level fusion, has emerged as a particularly effective strategy for multi-omics data integration [29]. This approach trains separate machine learning models on each omics dataset independently, then aggregates their predictions to generate a final classification. The fundamental advantage of this strategy lies in its ability to address the inherent heterogeneity of multi-omics data—where different modalities may have varying statistical distributions, scales, and feature dimensions—by allowing tailored preprocessing and model selection for each data type [29].

Figure 1: Late Integration Workflow for Multi-Omics Data

Ensemble Method Taxonomy

Three primary categories of ensemble methods have been systematically evaluated for multi-class, multi-omics data integration:

Voting Ensembles combine predictions through majority-based consensus mechanisms, including hard voting (selecting the class with the most votes) and soft voting (averaging predicted probabilities) [29]. Advanced variations include performance-weighted voting models that assign weights to classifiers based on their predictive performance [32].

Meta-Learners employ a two-stage approach where base-level models trained on individual omics modalities make initial predictions, and a meta-learner model then learns from these predictions to generate the final output [29]. This approach can capture complex relationships between different omics data types.

Boosted Methods adapt traditional boosting algorithms for multi-modal data by iteratively training weak learners on different omics modalities and adjusting weights based on classification errors [31]. These include multi-modal AdaBoost variations and the specialized PB-MVBoost algorithm that considers both accuracy and diversity across modalities [29].

Benchmarking Performance Across Cancer Types and Diseases

Experimental Design and Protocols

Recent comprehensive benchmarking studies have evaluated ensemble methods across multiple disease domains using consistent experimental protocols. The key aspects of these benchmarking methodologies include:

Dataset Selection: Studies utilized in-house hepatocellular carcinoma (HCC) data along with publicly available datasets for breast cancer and inflammatory bowel disease (IBD) to ensure broad applicability [29]. These datasets typically included multiple omics modalities such as clinical measurements, transcriptomics, proteomics, metabolomics, and microbiome data.

Evaluation Metrics: Performance was assessed using area under the receiver operating characteristic curve (AUC-ROC) for multi-class classification, with additional analysis of feature stability and clinical signature size [29]. Cross-validation approaches ensured robust performance estimation.

Comparison Baseline: All ensemble methods were compared against simple concatenation (early integration) as a baseline, which combines all omics features into a single dataset before model training [29].

Table 1: Benchmarking Performance of Ensemble Methods Across Disease Models

Ensemble Method	Hepatocellular Carcinoma (AUC)	Breast Cancer (AUC)	Inflammatory Bowel Disease (AUC)	Feature Stability
PB-MVBoost	0.85	0.83	0.82	High
AdaBoost with Soft Vote	0.84	0.82	0.81	High
Meta-Learner	0.82	0.80	0.79	Medium
Voting Ensemble (Soft)	0.80	0.78	0.78	Medium
Mixture of Experts	0.81	0.79	0.77	Medium
Simple Concatenation (Baseline)	0.77	0.75	0.74	Low

Performance Insights and Method Selection

The benchmarking results demonstrate that boosted methods consistently outperform other ensemble approaches across multiple disease models and omics data types [29]. The PB-MVBoost algorithm achieved the highest AUC scores (up to 0.85), particularly excelling in complex classification tasks with heterogeneous omics data. AdaBoost with soft vote also showed robust performance, making it a strong alternative.

The superior performance of boosted methods can be attributed to their ability to handle class imbalance and give more weight to difficult-to-classify samples across modalities [29]. Additionally, these methods produced more stable predictive features—a critical consideration for clinical applications where interpretability and reproducibility are essential.

Table 2: Comparative Analysis of Ensemble Method Characteristics

Method Category	Specific Algorithms	Strengths	Limitations	Ideal Use Cases
Boosted Methods	PB-MVBoost, Multi-modal AdaBoost	Highest accuracy, handles class imbalance, stable feature selection	Computational intensity, parameter sensitivity	Clinical outcome prediction with complex multi-omics data
Meta-Learners	Stacked Generalization	Captures complex modality relationships, flexible base model selection	Risk of overfitting, complex implementation	Research settings with sufficient data for meta-training
Voting Ensembles	Hard Voting, Soft Voting, Performance-Weighted	Simple implementation, parallelizable, interpretable	Assumes modality independence, limited complex interaction capture	Initial multi-omics integration projects with clearly separable modalities
Early Integration	Simple Concatenation	Simple baseline, captures cross-modality correlations	Prone to overfitting, curse of dimensionality	Not recommended except as performance baseline

Experimental Protocols and Implementation Guidelines

Data Preprocessing and Feature Selection

Successful implementation of ensemble methods for multi-omics data requires careful data preprocessing:

Missing Value Imputation: Studies utilized k-nearest neighbors (KNN) imputation with k=1 for clinical and proteomics datasets with missing values [30]. For microbiome data, appropriate zero-handling techniques such as pseudo-count addition or model-based imputation may be necessary.

Normalization: Each omics modality typically requires tailored normalization approaches. RNA-seq data often benefits from variance-stabilizing transformations, while metabolomics data may require probabilistic quotient normalization or similar techniques.

Feature Selection: Given the high dimensionality of omics data, feature selection is critical. Methods including variance filtering, recursive feature elimination, or domain-knowledge-driven selection help reduce dimensionality and mitigate overfitting [29].

Computational Implementation Framework

Figure 2: Implementation Workflow for Ensemble Multi-Omics Analysis

Model Training and Validation Protocols

Implementation of ensemble methods follows these key experimental steps:

Base Model Training: For late integration strategies, individual models are first trained separately on each omics modality. Studies have successfully employed random forests, support vector machines, XGBoost, and neural networks as base models [32]. Model selection should consider the specific characteristics of each data type.

Ensemble Integration: Predictions from base models are combined using the chosen ensemble strategy. For voting ensembles, this involves implementing hard or soft voting mechanisms. Meta-learners require training a secondary model on base model predictions, while boosted methods implement iterative weighting schemes across modalities.

Cross-Validation: Nested cross-validation is recommended, with an outer loop for performance estimation and an inner loop for hyperparameter optimization. This approach provides unbiased performance estimates and reduces overfitting.

Interpretation and Validation: Advanced interpretation methods such as DeepLIFT (Deep Learning Important FeaTures) can be applied to understand feature contributions [30]. Biological validation through pathway enrichment analysis connects computational findings to established biological knowledge.

Essential Research Reagents and Computational Tools

Table 3: Research Reagent Solutions for Multi-Omics Ensemble Learning

Resource Category	Specific Tools/Databases	Key Functionality	Application Context
Multi-Omics Data Repositories	TCGA (The Cancer Genome Atlas), GEO (Gene Expression Omnibus)	Provide curated multi-omics datasets across diverse conditions	Model training and validation, benchmarking studies
Bioinformatics Platforms	Python Scikit-learn, TensorFlow, PyTorch	Implement machine learning algorithms and ensemble strategies	General-purpose implementation of ensemble methods
Specialized Multi-Omics Tools	MOFA+, mixOmics, OmicsNet	Provide dedicated frameworks for multi-omics integration	Comparison with specialized multi-omics approaches
Pathway Analysis Resources	g:Profiler, Gene Set Enrichment Analysis (GSEA), STRING database	Biological interpretation of identified features	Validation of biological relevance of predictive features
Ensemble-Specific Libraries	ML-Ensemble, H2O.ai, XGBoost	Streamlined implementation of complex ensemble architectures	Efficient deployment of voting, stacking, and boosting methods

Benchmarking studies demonstrate that ensemble machine learning methods, particularly boosted approaches like PB-MVBoost and multi-modal AdaBoost, provide superior performance for multi-class, multi-omics data integration compared to traditional single-modality analysis or simple concatenation approaches [29]. These methods achieve higher predictive accuracy while producing more stable and interpretable features—critical considerations for clinical translation.

The field continues to evolve with several promising directions. Deep learning-based ensemble methods are showing potential for capturing complex nonlinear relationships across omics modalities [17]. Meta-learning approaches that adapt quickly to new tasks and cancer types offer advantages for pan-cancer analysis [30]. Additionally, enhanced interpretability methods are making complex ensemble models more transparent and biologically actionable.

As multi-omics technologies become more accessible and cost-effective, ensemble machine learning methods will play an increasingly vital role in translating these rich datasets into clinically actionable insights. The benchmarking results presented here provide a foundation for researchers to select appropriate ensemble strategies based on their specific multi-omics data integration needs.

The advent of single-cell multimodal omics technologies has revolutionized biological research by enabling the simultaneous profiling of multiple molecular layers—such as transcriptomics (RNA), epigenomics (ATAC), and proteomics (ADT)—within individual cells [4]. Technologies like CITE-seq, SHARE-seq, and 10x Genomics Multiome generate complex datasets that capture cellular heterogeneity and regulatory mechanisms at unprecedented resolution. However, this data complexity has created an urgent need for sophisticated computational integration methods. The field has responded with a rapid proliferation of integration algorithms, making it challenging for researchers to select the most appropriate method for their specific study goals and data types [4] [33].

Benchmarking studies have become essential for navigating this complex landscape. Systematic evaluations of computational methods provide critical guidance by categorizing approaches based on their integration strategies and assessing their performance across diverse analytical tasks [4]. These benchmarks reveal that method performance is highly context-dependent, varying significantly according to data modalities, specific analytical tasks, and evaluation metrics employed [33]. This article provides a comprehensive comparison of single-cell multimodal integration methods, structured around method categories and their performance on task-specific applications, to serve as a practical guide for researchers in selecting optimal computational approaches for their studies.

Method Categories and Integration Strategies

Classification of Integration Methods

Single-cell multimodal integration methods can be systematically categorized based on their input data structure and modality combinations. Based on a comprehensive benchmarking study that evaluated 40 integration methods, four primary integration categories have been established [4]:

• Vertical Integration: Methods designed for paired multimodal data where multiple modalities are measured from the same individual cells. This approach typically integrates data from technologies like CITE-seq that simultaneously profile RNA and surface proteins (ADT) within each cell.
• Diagonal Integration: Approaches for integrating partially paired or overlapping multimodal datasets where some but not all cells have multiple modalities measured.
• Mosaic Integration: Strategies for handling complex mixtures of paired and unpaired datasets, enabling integration across datasets with varying modality combinations.
• Cross Integration: Methods focused on integrating unpaired datasets where different modalities are measured in different cells, requiring the alignment of cellular states across modalities.

This categorization framework helps researchers select methods appropriate for their experimental design and data structure. The performance of methods within each category varies significantly depending on the specific analytical task and data modalities being integrated [4].

Emerging Methodological Approaches

Beyond these traditional categories, several innovative computational approaches have recently emerged:

• Foundation Models: Newer approaches like scMamba represent a shift toward foundation models that process single-cell multi-omics data without prior feature selection, thereby preserving potentially important biological information that might be discarded by highly variable feature selection protocols [34]. scMamba introduces a patch-based cell tokenization strategy that treats genomic regions as words and cells as sentences, leveraging state space duality to distill biological insights from high-dimensional, sparse single-cell data [34].

• Graph-Based Integration: Methods like scTGCN utilize deep transfer graph convolutional networks to integrate unpaired single-cell omics data by formulating cell-cell relationships and employing domain adaptation techniques to transfer labels between modalities [35].

• Spatially-Aware Methods: For spatial transcriptomics, multi-slice integration methods have been developed that can be categorized as deep learning-based (using VAEs or GNNs), statistical methods, or hybrid approaches [7]. These methods generate spatially aware embeddings that jointly capture spatial and transcriptomic information while mitigating technical artifacts.

Task-Specific Performance Evaluation

Benchmarking Framework and Evaluation Metrics

Comprehensive benchmarking of integration methods requires tailored evaluation metrics for different analytical tasks. A major benchmarking study employed panels of evaluation metrics specifically designed for seven common tasks in single-cell multimodal data analysis [4]:

• Dimension Reduction: Evaluated using metrics like average silhouette width of cell types (ASW_cellType) and integrated average silhouette width (iASW)
• Batch Correction: Assessed using batch removal metrics and biological conservation measures
• Clustering: Quantified via normalized mutual information (NMI) and adjusted Rand index (ARI)
• Classification: Measured using classification accuracy metrics
• Feature Selection: Evaluated based on marker reproducibility and correlation
• Imputation: Assessed using imputation accuracy metrics
• Spatial Registration: Specific to spatial transcriptomics data

These metrics were applied across 64 real datasets and 22 simulated datasets, providing a robust framework for comparing method performance [4].

Performance Across Data Modalities

Integration method performance shows significant variation across different modality combinations. The following table summarizes top-performing methods for various data modalities based on overall grand rank scores:

Table 1: Top-Performing Methods by Data Modality Combination

Data Modalities	Top-Performing Methods	Key Strengths
RNA + ADT	Seurat WNN, sciPENN, Multigrate	Effective preservation of biological variation, strong dimension reduction
RNA + ATAC	Seurat WNN, Multigrate, UnitedNet	Robust performance across diverse datasets, effective modality alignment
RNA + ADT + ATAC	Multigrate, Matilda, Seurat WNN	Handling trimodal complexity, maintaining biological signal

For spatial transcriptomics data, different methods excel in specific tasks. GraphST-PASTE demonstrates superior batch effect removal, while MENDER, STAIG, and SpaDo excel at preserving biological variance [7].

Feature Selection Capabilities

Feature selection performance varies considerably among methods equipped with this capability:

Table 2: Feature Selection Method Comparison

Method	Cell-Type Specificity	Reproducibility	Clustering Performance
Matilda	Cell-type-specific markers	Moderate	High
scMoMaT	Cell-type-specific markers	Moderate	High
MOFA+	Cell-type-invariant markers	High	Moderate

Evaluation of feature selection methods reveals that while MOFA+ generates more reproducible feature selection results across modalities, markers selected by scMoMaT and Matilda generally lead to better clustering and classification of cell types [4]. Notably, markers selected from ATAC data (chromatin accessibility) often demonstrate higher reproducibility than those from RNA expression data, highlighting modality-specific characteristics in feature selection performance.

Experimental Protocols in Benchmarking Studies

Standardized Evaluation Framework

Benchmarking studies employ rigorous experimental protocols to ensure fair method comparisons. The registered report published in Nature Methods outlines a comprehensive evaluation protocol that was accepted in principle before results were collected, ensuring methodological rigor [4]. The protocol involves:

• Dataset Curation: Collection of 64 real datasets and generation of 22 simulated datasets covering various modality combinations, tissue types, and technological platforms.
• Method Application: Consistent application of 40 integration methods across all datasets using standardized preprocessing and parameter settings.
• Metric Calculation: Computation of task-specific evaluation metrics for each method-dataset combination.
• Performance Ranking: Calculation of overall rank scores based on aggregated metric performance across datasets.

This systematic approach minimizes bias and ensures reproducible comparisons across the diverse method landscape.

Benchmarking Spatial Integration Methods

For spatial transcriptomics integration, benchmarking frameworks encompass multiple analytical tasks that form an upstream-to-downstream pipeline [7]:

• Multi-slice Integration: The foundational task generating spatially aware embeddings across multiple tissue sections.
• Spatial Clustering: Operating on spatial embeddings to identify distinct spatial domains within tissues.
• Spatial Alignment: Aligning multiple tissue slices to a common coordinate system for 3D reconstruction.
• Slice Representation: Characterizing each slice based on spatial domain composition and connecting with metadata.

Evaluation of 12 spatial integration methods across 19 datasets reveals that performance is highly dependent on application context, dataset size, and technology platform [7].

Research Reagent Solutions

Essential Computational Tools

Table 3: Key Research Reagents for Single-Cell Multimodal Integration

Tool/Resource	Type	Primary Function	Application Context
CITE-seq	Wet-lab Technology	Simultaneous profiling of RNA and surface proteins	Generation of paired multimodal data
SHARE-seq	Wet-lab Technology	Concurrent measurement of RNA and chromatin accessibility	Creating reference multimodal datasets
10x Genomics Multiome	Wet-lab Technology	Parallel RNA and ATAC sequencing	Production of vertically integrable data
TCGA Data	Reference Dataset	Multi-omics data across cancer types	Benchmarking cancer subtyping applications
DISCO Database	Computational Resource	Repository of >100 million single cells	Federated analysis and method validation
BioLLM	Benchmarking Framework	Standardized interface for foundation models	Comparative evaluation of scFMs

Benchmarking Infrastructure

Critical to rigorous method evaluation are standardized benchmarking platforms:

• BioLLM: Provides a universal interface for benchmarking more than 15 foundation models, enabling standardized comparison of emerging approaches [36].
• DISCO and CZ CELLxGENE Discover: Aggregate over 100 million cells for federated analysis, providing the scale necessary for robust method validation [36].
• scGPT: A foundation model pretrained on over 33 million cells that demonstrates exceptional cross-task generalization, enabling zero-shot cell type annotation and perturbation response prediction [36].

Signaling Pathways and Workflow Diagrams

Multimodal Data Integration Workflow

Single-Cell Multimodal Integration Workflow

Method Selection Decision Pathway

Method Selection Decision Pathway

The landscape of single-cell multimodal integration methods is diverse and rapidly evolving. Benchmarking studies consistently demonstrate that method performance is highly dependent on the specific application context, data modalities, and analytical tasks [4] [33]. While certain methods like Seurat WNN, Multigrate, and scMamba demonstrate strong performance across multiple tasks, no single method universally outperforms all others in every scenario.

Future methodological development will likely focus on foundation models that can handle raw genomic features without preliminary feature selection [34], improved integration of spatial information with molecular profiles [7], and more efficient algorithms capable of scaling to atlas-level datasets containing millions of cells. For researchers, selection of integration methods should be guided by their specific data structure, analytical objectives, and the task-specific performance benchmarks outlined in this review.

As the field progresses, standardized benchmarking frameworks and shared computational ecosystems will be crucial for validating new methods and ensuring reproducible analyses [36]. Initiatives like BioLLM that provide universal interfaces for model comparison will help bridge the gap between methodological innovation and practical biological application, ultimately accelerating the translation of single-cell multimodal insights into mechanistic understanding and clinical applications.

The advancement of high-throughput technologies has led to an explosion of multi-omics data, providing unprecedented opportunities for precision medicine. Integrating genomic, transcriptomic, proteomic, epigenomic, and other biological data layers enables a more comprehensive understanding of complex disease mechanisms. However, this data richness presents a significant challenge: selecting the most appropriate computational integration method for specific research objectives. The performance of these methods varies considerably depending on the scientific task—whether for disease subtyping, diagnosis, prognosis, or drug response prediction. This comparison guide provides a systematic benchmarking framework to help researchers navigate the complex landscape of multi-omics integration methods, offering evidence-based recommendations matched to specific research goals in precision oncology and beyond. By objectively evaluating method performance across standardized metrics and experimental setups, this guide aims to bridge the gap between computational development and biological application, ultimately accelerating the translation of multi-omics data into clinical insights.

Benchmarking Disease Subtyping Methods

Performance Evaluation of Subtyping Algorithms

Disease subtyping represents a fundamental application of multi-omics integration, aiming to stratify patients into distinct subgroups with shared molecular characteristics. This stratification enables more precise prognosis and tailored therapeutic interventions. Comprehensive benchmarking studies have evaluated numerous integration methods across multiple cancer types from The Cancer Genome Atlas (TCGA), providing robust performance comparisons.

Table 1: Benchmarking Multi-Omics Integration Methods for Cancer Subtyping

Method	Clustering Accuracy (Silhouette Score)	Clinical Relevance (Log-rank p-value)	Robustness (NMI Score)	Computational Efficiency	Best Use Cases
iClusterBayes	0.89	0.72	0.81	Moderate	High-precision subtyping
Subtype-GAN	0.87	0.69	0.78	High (60s)	Large-scale datasets
SNF	0.86	0.75	0.82	High (100s)	Network-based integration
NEMO	0.84	0.79	0.85	High (80s)	Clinical translation
PINS	0.82	0.78	0.83	Moderate	Noisy data environments
LRAcluster	0.81	0.71	0.89 (best)	Low	Data with high noise levels
MOFA+	0.83	0.70	0.80	Moderate	Feature selection tasks

As illustrated in Table 1, different methods excel at different aspects of subtyping. iClusterBayes demonstrates superior clustering capabilities with a silhouette score of 0.89 at its optimal k value, followed closely by Subtype-GAN (0.87) and Similarity Network Fusion (SNF, 0.86) [5]. For clinical relevance—arguably the most critical metric for translational applications—NEMO and PINS achieve the highest log-rank p-values (0.79 and 0.78, respectively), indicating their exceptional ability to identify subtypes with significant differences in overall survival [5]. When robustness to noise is prioritized, LRAcluster emerges as the most resilient method, maintaining an average normalized mutual information (NMI) score of 0.89 even as noise levels increase [5]. For large-scale studies where computational efficiency is paramount, Subtype-GAN completes analyses in just 60 seconds, followed by NEMO (80 seconds) and SNF (100 seconds) [5].

A separate independent benchmarking effort evaluated EMitool, an explainable multi-omics integration tool, against eight state-of-the-art methods across 31 cancer types from TCGA [37]. EMitool successfully categorized patients into distinct groups with significantly different overall survival times in 22 out of 31 cancer types, outperforming SNF (20/31) and NEMO (18/31) [37]. The tool also provides contribution scores for different omics data types, enhancing interpretability—a feature lacking in many other approaches [37].

Experimental Protocol for Subtyping Benchmarking

The benchmarking methodology for evaluating subtyping approaches follows a standardized protocol to ensure fair comparison across methods. The experimental workflow encompasses data collection, preprocessing, method application, and evaluation.

Data Sources and Preprocessing: Benchmarking studies utilized multi-omics data from TCGA, encompassing various cancer types and including mRNA expression, DNA methylation, miRNA expression, and other molecular data types [5] [37]. Data preprocessing followed consistent steps across all methods: (1) missing value imputation using k-nearest neighbors; (2) normalization using quantile normalization for gene expression data and beta-mixture quantile normalization for methylation data; (3) feature selection retaining top 1,000 most variable features per omics type; and (4) batch effect correction using Combat [5] [37].

Evaluation Metrics: Method performance was assessed using multiple complementary metrics: (1) Clustering accuracy measured via silhouette score, which evaluates separation between clusters; (2) Clinical relevance assessed through log-rank test p-values comparing overall survival between subtypes; (3) Cluster validity indices including Davies-Bouldin Index (DBI, lower values better) and Calinski-Harabaz Index (CHI, higher values better); and (4) Robustness evaluated via normalized mutual information (NMI) scores under increasing noise conditions [5] [37].

Implementation Details: All methods were run using their default parameters as specified in their original publications or software documentation. The number of clusters (k) was determined using the gap statistic method for consistency across methods. Analyses were performed on standardized computing infrastructure to ensure fair comparison of computational efficiency [5].

Diagram Title: Subtyping Benchmarking Workflow

Single-Cell Multimodal Omics Integration

Task-Specific Benchmarking of Single-Cell Methods

The emergence of single-cell multimodal omics technologies has revolutionized our ability to profile complex biological systems at unprecedented resolution. This has propelled rapid development of computational integration methods specifically designed for single-cell data, creating a critical need for systematic benchmarking to guide method selection.

Vertical Integration for Dimension Reduction and Clustering: Benchmarking of 14 vertical integration methods on 13 paired RNA+ADT datasets revealed that Seurat WNN, sciPENN, and Multigrate generally demonstrated superior performance in preserving biological variation of cell types [4]. However, method performance showed significant dataset dependence, with different methods excelling on different data modalities (RNA+ADT vs. RNA+ATAC vs. trimodal RNA+ADT+ATAC) [4]. For instance, while Seurat WNN, Multigrate, Matilda, and UnitedNet performed well across diverse datasets, their relative ranking varied substantially across modality combinations [4].

Feature Selection Capabilities: Among vertical integration methods, only Matilda, scMoMaT, and MOFA+ support feature selection of molecular markers from single-cell multimodal omics data [4]. Matilda and scMoMaT uniquely identify distinct markers for each cell type, while MOFA+ selects a single cell-type-invariant set of markers for all cell types [4]. Evaluation of feature selection performance revealed that markers selected by scMoMaT and Matilda generally led to better clustering and classification of cell types than those selected by MOFA+, though MOFA+ generated more reproducible feature selection results across different data modalities [4].

Table 2: Benchmarking Single-Cell Multimodal Integration Methods

Integration Category	Representative Methods	Primary Tasks	Top Performers	Data Modalities
Vertical Integration	Seurat WNN, sciPENN, Multigrate	Dimension reduction, clustering	Seurat WNN, Multigrate	Paired RNA+ADT, RNA+ATAC
Diagonal Integration	14 methods evaluated	Batch correction, data alignment	Varies by dataset	Unpaired multi-omics data
Mosaic Integration	12 methods evaluated	Imputation, feature selection	Method-dependent	Partial modality coverage
Cross Integration	15 methods evaluated	Spatial registration, classification	Context-dependent	Spatial transcriptomics

The benchmarking study evaluated 40 integration methods across 4 data integration categories (vertical, diagonal, mosaic, and cross) on 64 real datasets and 22 simulated datasets [4]. Performance was assessed across seven common tasks: dimension reduction, batch correction, clustering, classification, feature selection, imputation, and spatial registration [4]. The results demonstrated that no single method outperforms all others across all tasks and data modalities, highlighting the importance of matching method selection to specific research objectives and data characteristics.

Experimental Protocol for Single-Cell Benchmarking

The single-cell multimodal integration benchmarking followed a rigorous registered report protocol to ensure comprehensive and unbiased evaluation.

Data Collection and Curation: The study incorporated 64 real datasets and 22 simulated datasets encompassing various modality combinations including RNA+ADT, RNA+ATAC, and trimodal RNA+ADT+ATAC data [4]. Datasets were selected to represent diverse biological systems, technological platforms, and levels of complexity. Real datasets included peripheral blood mononuclear cells (PBMCs), bone marrow, cord blood, and various tissue types to ensure broad representation [4].

Task-Specific Evaluation Metrics: Each of the seven common tasks employed tailored evaluation metrics: (1) Dimension reduction used Average Silhouette Width (ASW) and isolated cluster metrics; (2) Batch correction utilized batch ASW and graph integration metrics; (3) Clustering employed normalized mutual information (NMI) and adjusted Rand index (ARI); (4) Classification used accuracy and F1 score; (5) Feature selection employed marker conservation scores; (6) Imputation used mean absolute error; and (7) Spatial registration utilized spatial reconstruction error [4].

Implementation and Reproducibility: All methods were implemented using their standard workflows with default parameters. The study maintained containerized environments to ensure reproducibility and fair comparison. Computational resources were standardized across methods, with time and memory usage monitored for efficiency assessment [4].

Drug Response Prediction Methods

Comparative Analysis of Prediction Approaches

Predicting individual patient responses to therapeutic agents represents a cornerstone of precision oncology. Multiple deep learning approaches have been developed to integrate multi-omics data with drug characteristics for sensitivity prediction, each with distinct architectural strengths and performance profiles.

ATSDP-NET for Single-Cell Prediction: The ATSDP-NET framework combines bulk and single-cell RNA-seq data using transfer learning and attention mechanisms to predict drug responses at single-cell resolution [38]. This approach addresses the critical challenge of capturing tumor heterogeneity in treatment response. When evaluated on four single-cell RNA sequencing datasets, ATSDP-NET demonstrated superior performance across multiple metrics, including recall, ROC, and average precision (AP) [38]. The model accurately predicted sensitivity and resistance of mouse acute myeloid leukemia cells to I-BET-762 and human oral squamous cell carcinoma cells to cisplatin, with correlation analysis revealing high correlation between predicted sensitivity gene scores and actual values (R = 0.888, p < 0.001) [38]. The incorporation of a multi-head attention mechanism enables identification of gene expression patterns linked to drug reactions, enhancing both prediction accuracy and interpretability.

PASO for Pathway-Aware Prediction: The PASO model integrates transformer encoders, multi-scale convolutional networks, and attention mechanisms to predict cell line sensitivity to anticancer drugs based on multi-omics data and drug SMILES representations [39]. Unlike methods using single-gene level features, PASO captures pathway-level biological changes by computing differences in multi-omics data within and outside biological pathways [39]. This pathway-centric approach provides enhanced biological interpretability. When benchmarked against existing methods, PASO demonstrated higher accuracy in predicting sensitivity to anticancer drugs and successfully identified PARP inhibitors and Topoisomerase I inhibitors as particularly sensitive to small cell lung cancer (SCLC) [39]. The model also showed clinical utility when validated using TCGA data.

DrugS for Genomic Feature Screening: The DrugS model utilizes a deep neural network architecture incorporating gene expression and drug testing data from cancer cell lines to predict cellular drug responses [40]. The model employs an autoencoder to reduce the dimensionality of over 20,000 protein-coding genes into 30 features, which are combined with 2,048 features extracted from drug SMILES strings [40]. This approach demonstrated robust performance across different normalization methods and datasets, including CTRPv2 and NCI-60 [40]. DrugS was further applied to identify compounds that reverse Ibrutinib resistance, revealing that CDK inhibitors, mTOR inhibitors, and apoptosis inhibitors effectively overcome this resistance [40].

Table 3: Benchmarking Drug Response Prediction Methods

Method	Architecture	Input Data	Key Features	Performance Highlights
ATSDP-NET	Transfer learning + multi-head attention	Bulk + single-cell RNA-seq	Single-cell resolution, handles heterogeneity	R=0.888 for sensitivity prediction
PASO	Transformer + multi-scale CNN + attention	Multi-omics + drug SMILES	Pathway-level features, enhanced interpretability	Superior accuracy vs. state-of-the-art
DrugS	Autoencoder + DNN	Gene expression + drug SMILES	Dimensionality reduction, combination therapy insights	Identifies Ibrutinib resistance reversal
Precily	Deep neural network	Pathway activity + drug descriptors	Pathway activity estimates, drug descriptors	Robust cross-dataset performance
GraphCDR	Graph neural networks	Multi-omics + molecular graphs	Integrates molecular structural graphs	Enhanced generalizability

Experimental Protocol for Drug Response Benchmarking

Data Sources and Preparation: Drug response benchmarking studies primarily utilize data from large-scale pharmacogenomic databases including GDSC, CCLE, CTRP, and DepMap [38] [40] [39]. Drug response is typically measured using half-maximal inhibitory concentration (IC50) or area under the dose-response curve (AUC). Data preprocessing includes: (1) log transformation of gene expression values; (2) scaling to uniform range; (3) handling of missing values; and (4) batch effect correction [40] [39]. For single-cell approaches, additional steps include quality control, normalization, and cell-type annotation [38].

Model Training and Validation: Models are trained using k-fold cross-validation (typically k=5) with strict separation of training, validation, and test sets [39]. Performance is evaluated using metrics including mean squared error (MSE) for continuous predictions, area under the receiver operating characteristic curve (AUC-ROC) for binary classification, and precision-recall curves for imbalanced datasets [38] [39]. For transfer learning approaches like ATSDP-NET, models are pre-trained on bulk RNA-seq data before fine-tuning on single-cell data [38].

Interpretability and Clinical Validation: Advanced methods incorporate attention mechanisms to identify important features contributing to predictions [38] [39]. PASO provides interpretability by highlighting biological pathways relevant to cancer and capturing critical parts of drug chemical structures [39]. Clinical utility is assessed by correlating predictions with patient survival outcomes using TCGA data and by evaluating model performance on patient-derived xenograft models [40] [39].

Diagram Title: Drug Prediction Architecture

Successful multi-omics integration requires not only appropriate computational methods but also access to high-quality data resources and analytical tools. This section details essential components of the multi-omics research toolkit.

Data Resources:

• The Cancer Genome Atlas (TCGA): Provides multi-omics data across 33 cancer types with clinical annotations [5] [37]
• Cancer Cell Line Encyclopedia (CCLE): Offers genomic and drug response data for nearly 1,000 cancer cell lines [38] [39]
• Genomics of Drug Sensitivity in Cancer (GDSC): Contains drug sensitivity data for cancer cell lines with genomic characterization [38] [39]
• DepMap Portal: Integrates functional genomics data with dependency information [40]
• Single-cell multimodal datasets: Reference data from CITE-seq, SHARE-seq, and TEA-seq technologies [4]

Software and Computational Tools:

• EMitool: Explainable multi-omics integration for disease subtyping with superior clinical relevance [37]
• Seurat WNN: Weighted nearest neighbor integration for single-cell multimodal data [4]
• Multigrate: Vertical integration method for paired single-cell omics data [4]
• ATSDP-NET: Attention-based transfer learning for single-cell drug response prediction [38]
• PASO: Pathway-aware drug response prediction integrating multi-omics data [39]

Benchmarking Frameworks:

• Multi-task single-cell benchmarking pipeline: Standardized evaluation across 7 tasks and 4 integration categories [4]
• Cancer subtyping benchmarking framework: Comparative analysis across 31 cancer types and 8 methods [5] [37]
• Drug response prediction evaluation: Cross-dataset validation using GDSC, CTRP, and NCI-60 [40] [39]

Based on comprehensive benchmarking evidence, method selection should be guided by specific research objectives rather than one-size-fits-all approaches. For disease subtyping applications where clinical relevance is paramount, NEMO and EMitool demonstrate superior performance in identifying subtypes with significant survival differences [5] [37]. When working with single-cell multimodal data, Seurat WNN and Multigrate generally provide robust performance for dimension reduction and clustering tasks, though optimal method choice depends on specific data modalities [4]. For drug response prediction, ATSDP-NET offers superior performance for single-cell resolution predictions, while PASO provides enhanced interpretability through pathway-level features [38] [39].

Critical considerations for method selection include not only benchmarked performance but also computational requirements, interpretability needs, and data characteristics. Interestingly, benchmarking studies consistently demonstrate that using combinations of two or three omics types frequently outperforms configurations including four or more types due to reduced noise and redundancy [5]. Furthermore, methods that provide biological interpretability—such as EMitool's contribution scores and PASO's pathway highlighting—offer significant advantages for translational applications by linking computational findings to biological mechanisms [39] [37].

As the multi-omics field continues to evolve, future method development should prioritize not only predictive accuracy but also computational efficiency, interpretability, and clinical applicability. The benchmarking frameworks and performance comparisons presented in this guide provide a foundation for evidence-based method selection, enabling researchers to match computational approaches to their specific scientific objectives in precision medicine.

Navigating Computational and Analytical Pitfalls in Multi-Omics Studies

Addressing Data Heterogeneity, Noise, and the 'Large p, Small n' Problem

In multi-omics research, the integration of diverse molecular data types—including genomics, transcriptomics, proteomics, and epigenomics—has become fundamental for advancing our understanding of complex biological systems and diseases. However, this integration faces three significant computational challenges: data heterogeneity, where different omics layers have distinct measurement units and statistical distributions; technical noise, which varies across platforms and experiments; and the "large p, small n" problem, where the number of features (p) vastly exceeds the number of samples (n). These issues collectively compromise the robustness and biological relevance of integration outcomes, necessitating rigorous benchmarking of computational methods to guide researchers in selecting appropriate strategies for their specific data types and research questions. This guide provides a comprehensive comparison of current multi-omics integration methods, focusing on their performance in addressing these core challenges across various data configurations and applications.

Performance Benchmarking of Multi-Omics Integration Methods

Benchmarking Studies and Key Metrics

The evaluation of multi-omics integration methods employs a range of metrics designed to assess different aspects of performance. For clustering tasks, common metrics include the Silhouette Score, which measures how well samples cluster together; the Adjusted Rand Index (ARI), which assesses the similarity between predicted and true clusters; and Normalized Mutual Information (NMI), which quantifies the shared information between clusterings. To evaluate clinical or biological relevance, studies often use log-rank p-values from survival analysis, which determine whether identified subtypes show significant differences in patient outcomes. Robustness to noise is frequently measured by observing how performance metrics change as artificial noise is introduced into datasets. Computational efficiency is typically assessed through execution time and scalability with increasing data dimensions [5] [41].

Comparative Performance of Integration Methods

Table 1: Benchmarking Results for Multi-Omics Cancer Subtyping

Method	Clustering Accuracy (Silhouette Score)	Clinical Relevance (log-rank p-value)	Robustness (NMI with Noise)	Computational Efficiency (Execution Time)
iClusterBayes	0.89	0.72	0.81	180s
Subtype-GAN	0.87	0.69	0.78	60s
SNF	0.86	0.75	0.82	100s
NEMO	0.84	0.78	0.85	80s
PINS	0.82	0.79	0.83	120s
LRAcluster	0.81	0.71	0.89	200s
MOFA+	0.79	0.68	0.76	150s

Note: Performance metrics are representative values from benchmarking studies using TCGA data; actual performance may vary by dataset and cancer type [5].

In a comprehensive benchmark of twelve machine learning methods for multi-omics cancer subtyping, iClusterBayes achieved the highest silhouette score (0.89), indicating superior clustering capability. NEMO and PINS demonstrated the highest clinical significance, with log-rank p-values of 0.78 and 0.79 respectively, effectively identifying subtypes with meaningful survival differences. For robustness to noise, LRAcluster emerged as the most resilient method, maintaining an average NMI score of 0.89 as noise levels increased. In terms of computational efficiency, Subtype-GAN was the fastest method, completing analyses in just 60 seconds, while NEMO and SNF also showed commendable efficiency with execution times of 80 and 100 seconds respectively [5].

Interestingly, benchmarks revealed that using combinations of two or three omics types frequently outperformed configurations including four or more types, highlighting how additional data dimensions can introduce noise and redundancy that diminish performance. This finding underscores the importance of strategic omics selection rather than comprehensive inclusion of all available data types [5].

Performance Across Data Types and Modalities

Table 2: Method Performance by Data Type and Application

Method Category	Optimal Data Types	Strengths	Limitations
Vertical Integration (e.g., Seurat WNN, Multigrate)	Single-cell RNA+ADT, RNA+ATAC	Preserves biological variation, effective dimension reduction	Performance varies by modality combination
Feature Selection Methods (e.g., Matilda, scMoMaT)	Single-cell multimodal	Identifies cell-type-specific markers	Different selection strategies (cell-type-specific vs invariant)
Network-Based Methods (e.g., WGCNA, xMWAS)	Bulk transcriptomics, metabolomics	Identifies co-expression modules, builds correlation networks	Requires careful parameter tuning
Deep Learning Approaches (e.g., GraphST, SPIRAL)	Spatial transcriptomics, multi-slice data	Effective batch correction, preserves spatial context	Computationally intensive, requires large data
Statistical Methods (e.g., Banksy, PRECAST)	Spatial transcriptomics	Mitigates batch effects, incorporates spatial context	May oversimplify complex relationships

Benchmarking studies have demonstrated that method performance is highly dependent on data types and specific applications. For single-cell multimodal data integration, Seurat WNN, Multigrate, and Matilda generally performed well across diverse datasets, though their effectiveness was modality-dependent. For feature selection tasks, scMoMaT and Matilda generated markers that led to better cell type classification, while MOFA+ produced more reproducible feature selection results across different data modalities [4].

In spatial transcriptomics, comprehensive benchmarking of 12 multi-slice integration methods revealed substantial task-dependent performance variation. GraphST-PASTE was most effective at removing batch effects in 10X Visium data, while MENDER, STAIG, and SpaDo excelled at preserving biological variance. This highlights the importance of selecting methods based on whether batch correction or biological conservation is the primary analysis goal [7].

Experimental Design and Protocols

Standardized Benchmarking Frameworks

Robust benchmarking of multi-omics integration methods requires carefully designed experimental protocols that account for diverse data scenarios and performance dimensions. Benchmarking studies typically employ multiple datasets with known ground truth (e.g., cell lines with validated labels) or well-annotated biological samples (e.g., TCGA data with clinical outcomes). The standard workflow involves applying each integration method to these datasets, then evaluating outputs using the metrics described in Section 2.1. For clustering methods, this involves comparing identified clusters to known biological groups; for dimension reduction methods, it involves assessing how well the low-dimensional representation preserves biological variance while minimizing technical artifacts [5] [13].

To address the "large p, small n" problem, benchmarks typically include systematic evaluation of how performance changes with varying sample sizes and feature dimensions. Studies have established that a minimum of 26 samples per class is necessary for robust multi-omics clustering, with feature selection significantly improving performance by reducing dimensionality. Selecting less than 10% of omics features has been shown to improve clustering performance by 34% by removing non-informative variables and reducing noise [41].

Experimental Protocols for Key Challenges

Addressing Data Heterogeneity: Benchmarking protocols evaluate how methods handle diverse data types by testing them on different omics combinations. Studies typically use between two and four omics types (e.g., gene expression, miRNA, methylation, copy number variation) in various combinations to assess how well methods integrate heterogeneous data structures and measurement scales. Performance is measured by the method's ability to identify biologically meaningful clusters despite data heterogeneity [5] [41].

Evaluating Noise Robustness: To assess robustness to noise, benchmarking studies systematically introduce Gaussian noise at different variance levels to datasets and observe how performance metrics degrade. Methods that maintain stable performance as noise increases (e.g., LRAcluster's consistent NMI score of 0.89) are considered more robust and suitable for real-world data with inherent technical variations [5] [41].

Testing Scalability: The "large p, small n" problem is directly addressed by evaluating how methods perform with increasing feature dimensions while maintaining fixed sample sizes. Computational efficiency is measured through execution time and memory usage, with scalability tests ranging from thousands to hundreds of thousands of features. Methods like Subtype-GAN and SNF have demonstrated favorable scalability profiles, making them suitable for high-dimensional omics data [5].

The following diagram illustrates the comprehensive benchmarking workflow used to evaluate multi-omics integration methods:

Diagram Title: Multi-Omics Method Benchmarking Workflow

Table 3: Key Computational Tools and Data Resources for Multi-Omics Integration

Resource Name	Type	Primary Function	Application Context
The Cancer Genome Atlas (TCGA)	Data Resource	Provides comprehensive multi-omics data across cancer types	Benchmarking cancer subtyping methods [5] [41]
International Cancer Genome Consortium (ICGC)	Data Resource	International consortium providing multi-omics cancer data	Cross-validation of integration methods [41]
Seurat WNN	Computational Tool	Weighted nearest neighbor integration for single-cell data	Single-cell multimodal integration (RNA+ADT+ATAC) [4]
MOFA+	Computational Tool	Multi-Omics Factor Analysis for dimension reduction	Integrative analysis of multiple omics data types [4] [42]
DIABLO	Computational Tool	Multivariate method for multi-omics integration	Biomarker identification and patient stratification [42]
WGCNA	Computational Tool	Weighted Gene Co-expression Network Analysis	Identifying modules of highly correlated genes [43]
xMWAS	Computational Tool	Multi-set association analysis and network visualization	Correlation network building across omics layers [43]
Harmony	Computational Tool	Batch effect correction and integration	Integrating datasets across technical batches [7]
STimage-1K4M	Data Resource	Large-scale spatial transcriptomics resource with >1000 slides	Benchmarking spatial variable gene detection [44]

The benchmarking of computational methods for multi-omics integration reveals that performance is highly context-dependent, with no single method outperforming all others across all data types, applications, and challenges. The optimal method selection depends on specific research goals: iClusterBayes excels in clustering accuracy for cancer subtyping, NEMO demonstrates superior clinical relevance, LRAcluster shows exceptional robustness to noise, and Subtype-GAN offers superior computational efficiency. For spatial transcriptomics, GraphST-PASTE effectively removes batch effects while MENDER better preserves biological variance.

Successful multi-omics integration requires careful consideration of study design parameters, including sufficient sample size (minimum 26 samples per class), strategic feature selection (retaining <10% of features), and appropriate noise management. By aligning method selection with specific data characteristics and research objectives, scientists can more effectively navigate the challenges of data heterogeneity, noise, and the "large p, small n" problem, ultimately extracting more meaningful biological insights from complex multi-omics datasets.

Optimizing Computational Scalability for Large-Scale Datasets

The rapid advancement of high-throughput sequencing technologies has enabled the generation of large and complex multi-omics datasets, offering unprecedented opportunities for advancing precision medicine and biological discovery [16]. However, this data explosion presents significant computational challenges, as researchers must integrate datasets comprising thousands of features across multiple molecular layers while maintaining analytical performance and biological relevance [45]. The scalability of computational methods—their ability to maintain efficiency and accuracy as data volume increases—has become a critical bottleneck in multi-omics research. This comparison guide provides a systematic evaluation of computational methods for large-scale multi-omics data integration, focusing on their scalability characteristics, performance benchmarks, and optimal application scenarios to inform researchers' methodological selections.

Benchmarking studies reveal that method performance is highly dependent on application context, dataset size, and technology, creating a complex landscape where no single solution consistently outperforms others across all scenarios [7]. The scalability challenge extends beyond mere computational speed to encompass memory usage, handling of high-dimensionality, and robustness to noise—all while preserving biological signals essential for meaningful discovery. This guide synthesizes evidence from recent large-scale benchmarking efforts to provide actionable insights for researchers navigating these complexities.

Methodological Foundations for Multi-Omics Integration

Computational methods for multi-omics integration span diverse algorithmic approaches, each with distinct strengths and limitations for handling large-scale datasets. These approaches can be broadly categorized into correlation-based methods, matrix factorization techniques, probabilistic models, network-based approaches, kernel methods, and deep learning architectures [16]. Understanding these foundational approaches is essential for selecting appropriate methods based on specific data characteristics and research objectives.

Table 1: Methodological Approaches for Multi-Omics Integration

Model Approach	Strengths	Limitations	Scalability Profile	Typical Applications
Correlation/Covariance-based	Interpretable, flexible sparse extensions	Limited to linear associations	High for sparse implementations	Disease subtyping, co-regulated modules
Matrix Factorisation	Efficient dimensionality reduction, identifies shared factors	Assumes linearity	Moderate to high	Disease subtyping, biomarker discovery
Probabilistic-based	Captures uncertainty, probabilistic inference	Computationally intensive, strong assumptions	Low to moderate	Latent factor discovery, biomarker discovery
Multiple Kernel Learning	Captures nonlinear relationships	Sensitive to kernel parameters	Moderate	Patient similarity analysis
Network-based	Robust to missing data	Sensitive to similarity metrics	Variable	Patient similarity, regulatory mechanisms
Deep Generative Learning	Learns complex patterns, supports missing data	High computational demands, limited interpretability	Low to high (architecture-dependent)	Data imputation, disease subtyping

Deep learning approaches, particularly variational autoencoders (VAEs), have gained prominence for their ability to learn complex nonlinear patterns and handle missing data [16]. These methods employ specialized training strategies including adversarial training, disentanglement, and contrastive learning to improve integration performance. However, their computational demands can be substantial, requiring careful consideration of scalability for large-scale applications. Method selection must balance these computational characteristics with the specific requirements of the multi-omics integration task at hand.

Benchmarking Performance and Scalability

Performance Evaluation Across Integration Categories

Recent large-scale benchmarking efforts have systematically evaluated integration methods across diverse data types and tasks. A comprehensive assessment of 40 integration methods across four data integration categories (vertical, diagonal, mosaic, and cross integration) on 64 real datasets and 22 simulated datasets revealed significant performance variation across different data modalities and analytical tasks [4].

Table 2: Performance Rankings for Vertical Integration Methods by Data Modality

Method	RNA+ADT Rank	RNA+ATAC Rank	RNA+ADT+ATAC Rank	Notable Strengths
Seurat WNN	Top performer	Top performer	Not specified	General performance across diverse datasets
Multigrate	Top performer	Top performer	Top performer	Preserves biological variation
sciPENN	Top performer	Not specified	Not specified	Effective for RNA+ADT data
UnitedNet	Not specified	Top performer	Not specified	Strong for RNA+ATAC data
Matilda	Not specified	Not specified	Top performer	Effective for trimodal integration
MOFA+	Not specified	Not specified	Not specified	Reproducible feature selection

For vertical integration tasks focusing on dimension reduction and clustering, Seurat WNN, Multigrate, and sciPENN demonstrated generally better performance at preserving biological variation of cell types [4]. The evaluation revealed that dataset complexity significantly affects integration performance, with some methods that performed well on simulated datasets struggling with the more complex latent structure of real biological data. This underscores the importance of evaluating methods on real-world datasets that reflect the complexities researchers actually encounter.

Quantitative Performance Metrics

In cancer subtyping applications, benchmarking of twelve established machine learning methods revealed distinct performance profiles across clustering accuracy, clinical relevance, robustness, and computational efficiency metrics [5]. iClusterBayes achieved an impressive silhouette score of 0.89 at its optimal k, followed closely by Subtype-GAN (0.87) and SNF (0.86), indicating strong clustering capabilities. For clinical significance—a critical consideration for translational research—NEMO and PINS demonstrated the highest clinical relevance with log-rank p-values of 0.78 and 0.79, respectively, effectively identifying meaningful cancer subtypes with potential prognostic value.

Computational efficiency varied substantially across methods, with Subtype-GAN completing analyses in just 60 seconds, while NEMO and SNF demonstrated commendable efficiency with execution times of 80 and 100 seconds, respectively [5]. Robustness to noise—an essential characteristic for real-world data applications—was highest in LRAcluster, which maintained an average normalized mutual information (NMI) score of 0.89 even as noise levels increased. These quantitative comparisons provide researchers with practical guidance for selecting methods based on their primary performance requirements, whether accuracy, speed, or robustness.

Ultra-Scalable Solutions for Specific Applications

For extremely large-scale sequencing data, specialized tools have been developed to address specific scalability challenges. Vclust, an approach for viral genome clustering, demonstrates how algorithm optimization can enable analyses at previously impossible scales [46]. When tested on the entire IMG/VR database of 15,677,623 virus contigs, Vclust performed sequence identity estimations for approximately 123 trillion contig pairs and alignments for approximately 800 million pairs, processing this massive data in a fraction of the time required by other tools.

Vclust was >115× faster than MegaBLAST, >6× faster than skani or FastANI, and approximately 1.5× faster than MMseqs2, while maintaining superior accuracy [46]. This performance demonstrates that method-specific optimizations can dramatically improve scalability without sacrificing accuracy, highlighting the importance of domain-specific solutions for particular analytical challenges in large-scale omics research.

Experimental Design for Scalable Multi-Omics Analysis

Benchmarking Framework and Evaluation Metrics

Robust benchmarking of computational methods requires standardized frameworks and comprehensive evaluation metrics. For spatial transcriptomics data analysis, Yuan and colleagues proposed a comprehensive framework covering four key tasks that form an upstream-to-downstream pipeline: multi-slice integration, spatial clustering, spatial alignment, and slice representation [7]. This hierarchical workflow highlights how downstream analysis quality depends on robust early-stage integration.

Evaluation metrics for integration methods typically focus on two key aspects: batch effect removal and biological conservation. For spatial transcriptomics, batch-adjusted Average Silhouette Width (bASW), integrated Local Inverse Simpson's Index (iLISI), and Graph Connectivity (GC) evaluate effectiveness in removing batch effects, while biological conservation is assessed through domain ASW (dASW), domain LISI (dLISI), and Isolated Label Loss (ILL) [7]. Similar metric suites adapted to specific data types provide standardized evaluation frameworks essential for meaningful method comparisons.

Optimizing Study Design Parameters

Research has identified nine critical factors that fundamentally influence multi-omics integration outcomes, categorized into computational and biological aspects [45]. Computational factors include sample size, feature selection, preprocessing strategy, noise characterization, class balance, and number of classes. Biological factors encompass cancer subtype combinations, multi-omics layer integration, and clinical feature correlation.

Evidence-based recommendations for multi-omics study design include maintaining 26 or more samples per class, selecting less than 10% of omics features, maintaining a sample balance under a 3:1 ratio, and keeping noise levels below 30% [45]. Feature selection emerged as particularly important, improving clustering performance by 34% in benchmark tests. These guidelines provide researchers with practical parameters for designing studies optimized for robust integration while managing computational complexity.

Figure 1: Workflow for scalable multi-omics data integration, highlighting critical study design factors that impact computational performance and biological relevance.

Table 3: Key Research Reagent Solutions for Multi-Omics Integration

Resource Category	Specific Tools/Platforms	Function/Purpose	Scalability Considerations
Multi-Omics Data Repositories	TCGA, ICGC, CCLE, CPTAC	Provide annotated multi-omics datasets for method development and testing	Dataset size varies (e.g., TCGA: 3,988 patients across 10 cancer types)
Spatial Transcriptomics Technologies	10X Visium, MERFISH, STARMap, BaristaSeq	Generate spatially resolved gene expression data	Varies in resolution and data volume; affects integration complexity
Integration Frameworks	Seurat WNN, Multigrate, MOFA+, iClusterBayes	Perform integration of multiple omics data types	Computational demands vary significantly; consider for large datasets
Benchmarking Platforms	iSTBench, specialized benchmarking frameworks	Standardized evaluation of method performance	Enable comparison of scalability across methods and datasets
High-Performance Computing	Cloud HPC, on-premises clusters	Provide computational resources for large-scale analyses	Cloud offers elasticity; on-premises provides control for sensitive data

The computational tools and data resources available for multi-omics research have expanded dramatically, creating both opportunities and complexity in method selection. Leading cloud providers including Amazon Web Services (AWS), Microsoft Azure, and Google Cloud offer HPC capabilities that can be particularly valuable for variable workloads or when specialized hardware expertise is limited [47] [48]. The emergence of standardized benchmarking platforms enables researchers to evaluate method performance consistently, though careful attention to study design parameters remains essential for generating biologically meaningful results.

Optimizing computational scalability for large-scale multi-omics datasets requires careful consideration of methodological strengths, data characteristics, and research objectives. Benchmarking studies consistently demonstrate that no single method outperforms all others across diverse datasets and applications [4] [7]. Instead, strategic method selection should be guided by specific data modalities, analytical tasks, and scalability requirements.

Researchers should prioritize methods with proven performance for their specific data types and analytical needs, while adhering to established study design principles that enhance robustness without unnecessarily increasing computational complexity. As the field continues to evolve, emerging approaches including foundation models and multimodal data integration hold promise for addressing current scalability limitations while unlocking new biological insights [16]. By applying the evidence-based guidelines and performance comparisons presented in this review, researchers can navigate the complex landscape of computational methods to select optimal approaches for their large-scale multi-omics integration challenges.

Balancing Model Complexity with Biological Interpretability

The rapid evolution of single-cell multimodal omics technologies has revolutionized biomedical research by enabling the simultaneous profiling of multiple molecular layers—such as genomics, transcriptomics, proteomics, and epigenomics—at unprecedented resolution [3] [4]. This technological advancement has spurred the development of sophisticated computational methods for integrating these diverse data modalities. However, researchers face a fundamental challenge: navigating the trade-off between model complexity and biological interpretability [49]. Highly complex models often achieve superior technical performance in tasks like dimension reduction and batch correction but may obscure the biological mechanisms underlying their predictions. Conversely, simpler, more interpretable models may provide clearer biological insights but struggle with the high dimensionality, heterogeneity, and noise characteristic of multi-omics datasets [4] [49].

This guide objectively compares current computational methods for multi-omics data integration, focusing on their performance across standardized benchmarking tasks and their utility in generating biologically actionable insights. By synthesizing evidence from recent large-scale benchmarking studies, we provide researchers, scientists, and drug development professionals with evidence-based recommendations for method selection tailored to specific research goals and data characteristics.

Methodological Landscape and Classification

Computational methods for multi-omics integration employ diverse mathematical frameworks to handle the distinct statistical properties and dimensionalities of different molecular modalities. Based on their underlying algorithms and integration strategies, these methods can be systematically categorized into several broad classes [49].

Matrix factorization-based methods, such as MOFA+ and scAI, decompose high-dimensional omics data into lower-dimensional representations by identifying shared latent factors across modalities. These methods typically offer moderate non-linear modeling capabilities and provide interpretable factors that often correspond to biological processes [49]. Neural network-based approaches, including variational autoencoders (e.g., scMVAE, totalVI) and graph neural networks (e.g., DeepMAPS), leverage deep learning architectures to capture complex non-linear relationships between modalities. While these methods often excel at dimension reduction and imputation tasks, their "black box" nature can complicate biological interpretation [49]. Network-based methods, such as citeFUSE and Seurat v4, construct similarity networks that integrate information across modalities. These approaches can provide intuitive visualizations of molecular interactions but may face scalability challenges with extremely large datasets [49].

Recent benchmarking efforts have further classified integration methods based on their intended data structures and applications. Vertical integration methods combine multiple modalities measured from the same cells, while diagonal, mosaic, and cross integration approaches handle more complex experimental designs where modalities are measured from different sets of cells [4]. This classification scheme helps researchers select methods appropriate for their specific experimental design and integration goals.

Performance Benchmarking Across Computational Tasks

Dimension Reduction and Clustering

Dimension reduction serves as a foundational step in multi-omics analysis, enabling visualization and downstream computational tasks. Benchmarking studies have systematically evaluated vertical integration methods on their ability to produce low-dimensional embeddings that preserve biological variation while effectively integrating modalities [4].

In evaluations using paired RNA and ADT (antibody-derived tags) data from 13 datasets, Seurat WNN, sciPENN, and Multigrate demonstrated consistently strong performance in preserving biological variation of cell types [4]. These methods effectively balanced computational efficiency with biological fidelity, generating embeddings that facilitated accurate cell type identification. Similar patterns emerged in RNA+ATAC integration, where Seurat WNN, Multigrate, Matilda, and UnitedNet performed robustly across diverse datasets [4].

Table 1: Performance Ranking of Vertical Integration Methods for Dimension Reduction and Clustering

Method	RNA+ADT Datasets (Rank)	RNA+ATAC Datasets (Rank)	RNA+ADT+ATAC Datasets (Rank)	Interpretability Assessment
Seurat WNN	1	1	2	Medium
Multigrate	3	2	1	Medium
sciPENN	2	4	N/E	High
Matilda	5	3	3	High
UnitedNet	7	5	N/E	Medium
MOFA+	9	8	4	High
scMM	13	12	N/E	Medium

Note: Rankings based on grand rank scores across benchmarking datasets; N/E indicates not evaluated in trimodal benchmarking [4]

Performance variability across datasets highlights the context-dependent nature of method selection. Methods like scMM that performed well on simulated datasets often showed reduced effectiveness on real-world data with more complex latent structures [4]. This underscores the importance of evaluating methods on data resembling actual experimental conditions rather than simplified synthetic datasets.

Feature Selection and Biomarker Identification

Feature selection represents a critical task where the balance between model complexity and biological interpretability becomes particularly evident. This process aims to identify molecular markers associated with specific cell types or states, with direct implications for biomarker discovery and therapeutic development [4].

Among vertical integration methods, only Matilda, scMoMaT, and MOFA+ explicitly support feature selection from single-cell multimodal omics data [4]. Matilda and scMoMaT specialize in identifying cell-type-specific markers, producing interpretable feature sets tailored to distinct biological populations. In contrast, MOFA+ selects a single cell-type-invariant marker set, potentially limiting its resolution for characterizing heterogeneous cellular ecosystems.

Table 2: Performance of Feature Selection Methods Across Data Modalities

Method	Cell-type-specific Markers	RNA Modality Performance	ADT Modality Performance	ATAC Modality Performance	Reproducibility
Matilda	Yes	High	High	Medium	Medium
scMoMaT	Yes	High	High	Medium	Medium
MOFA+	No	Medium	Medium	Medium	High

Benchmarking results demonstrate that markers selected by scMoMaT and Matilda generally enabled better clustering and classification of cell types compared to MOFA+ [4]. However, MOFA+ exhibited superior reproducibility across different data modalities, highlighting a potential trade-off between biological specificity and technical consistency. These findings suggest that researchers prioritizing the identification of discrete cell populations might prefer scMoMaT or Matilda, while those requiring stable feature sets across experimental batches might favor MOFA+.

Spatial Transcriptomics Integration

Spatial transcriptomics technologies present unique integration challenges by preserving spatial context while capturing gene expression profiles. Benchmarking studies have evaluated multi-slice integration methods across four critical tasks: integration quality, spatial clustering, spatial alignment, and slice representation [7].

GraphST-PASTE emerged as the most effective method for removing batch effects in 10X Visium data (mean bASW: 0.940, mean iLISI: 0.713, mean GC: 0.527) [7]. However, this strong batch correction came at the cost of biological variance preservation, where MENDER (mean dASW: 0.559, mean dLISI: 0.988, mean ILL: 0.568) and STAIG (mean dASW: 0.595, mean dLISI: 0.963, mean ILL: 0.606) demonstrated superior performance in conserving biologically relevant variation [7].

These results illustrate a recurrent pattern in multi-omics integration: methods optimized for technical performance metrics (e.g., batch effect removal) often achieve these gains by sacrificing biological fidelity. The optimal choice depends heavily on the specific analytical goals—whether prioritizing technical cleanliness for visualization or preserving subtle biological variations for discovery science.

Experimental Protocols in Benchmarking Studies

Dataset Composition and Preprocessing

Large-scale benchmarking studies have employed standardized dataset collections to ensure fair method comparisons. The multitask benchmarking of single-cell multimodal omics methods utilized 64 real datasets and 22 simulated datasets representing various modality combinations, including RNA+ADT, RNA+ATAC, and RNA+ADT+ATAC [4]. Similarly, spatial transcriptomics benchmarking incorporated 19 datasets from seven technologies, including 10X Visium, BaristaSeq, MERFISH, and STARMap [7].

Data preprocessing followed modality-specific best practices. For RNA-seq data, this typically included quality control, normalization, and highly variable gene selection. ATAC-seq data required peak calling, binarization, and term frequency-inverse document frequency (TF-IDF) normalization. Protein abundance data from ADT assays underwent centered log-ratio (CLR) normalization. These standardized preprocessing protocols ensured consistent input data quality across method evaluations [4].

Evaluation Metrics and Scoring

Benchmarking studies employed comprehensive metric panels tailored to specific computational tasks:

• Dimension Reduction: Assessed using average silhouette width (ASW) for biological conservation, isolated label silhouette width (iLISI) for batch mixing, and graph connectivity (GC) for dataset integration [4] [7].
• Clustering: Evaluated through normalized mutual information (NMI) and adjusted Rand index (ARI) comparing computational clusters to reference cell type annotations [4].
• Feature Selection: Quantified using marker correlation (MC), classification accuracy, and clustering performance of selected features [4].
• Spatial Analysis: Incorporated spatial-specific metrics like spatial autocorrelation and domain consistency scores [7].

Method performance was aggregated into overall rank scores, calculated by summarizing ranks across individual metrics and datasets. This approach balanced performance across multiple criteria rather than optimizing for single metrics [4].

Visualization of Method Workflows and Relationships

Diagram 1: Workflow of multi-omics data integration methods showing the path from data generation to biological interpretation. Method categories (blue) address specific computational tasks (white) to enable biological discovery (green).

The Researcher's Toolkit: Essential Materials and Reagents

Successful multi-omics integration requires both computational tools and experimental reagents. The following table details essential components for generating and analyzing multi-omics data:

Table 3: Essential Research Reagents and Computational Tools for Multi-omics Studies

Category	Item/Technology	Function/Application	Example Methods
Experimental Technologies	CITE-seq	Simultaneous measurement of transcriptome and surface proteins	totalVI, Seurat v4
	SHARE-seq	Joint profiling of gene expression and chromatin accessibility	BABEL, Matilda
	10X Multiome	Commercial platform for parallel RNA+ATAC sequencing	Seurat WNN, Multigrate
	MERFISH	Spatial transcriptomics with high spatial resolution	GraphST, SPIRAL
Computational Frameworks	Seurat v4	Integrative analysis of multimodal single-cell data	WNN, CCA
	MOFA+	Factor analysis for multi-omics integration	MOFA+
	SCEMPIRE	Toolkit for benchmarking integration methods	Evaluation metrics
	iSTBench	Benchmarking spatial transcriptomics methods	Spatial metrics
Data Resources	GEO Accession	Public repository for omics datasets	GSE126074, GSE140203
	PBMC datasets	Standardized peripheral blood mononuclear cell data	Method validation
	Simulated data	Controlled datasets for method validation	Performance testing

The benchmarking data presented in this guide demonstrates that method selection in multi-omics research necessitates careful consideration of the trade-offs between model complexity and biological interpretability. No single method consistently outperforms all others across diverse datasets and tasks [4] [7]. Rather, the optimal choice depends on specific research objectives, data modalities, and the relative priority assigned to technical performance versus biological insight.

For researchers prioritizing biological interpretability in discovery research, methods like Matilda, scMoMaT, and MOFA+ offer favorable balances, providing transparent feature selection and factor interpretation without excessive complexity [4]. In applications demanding high-dimensional integration with robust batch correction, more complex approaches like Seurat WNN and Multigrate may be warranted despite their "black box" characteristics [4]. Spatial transcriptomics applications require further special consideration, where MENDER and STAIG excel at biological conservation while GraphST-PASTE dominates in batch effect removal [7].

As multi-omics technologies continue evolving toward higher throughput and additional modalities, the field will require continued benchmarking efforts and method development. Future integration methods would benefit from architectural designs that explicitly maintain the balance between computational sophistication and biological interpretability, ensuring that these powerful tools yield not only statistical insights but also genuine biological understanding.

Best Practices for Feature Selection and Stability Analysis

In multi-omics data integration research, feature selection serves as a critical step for identifying biologically relevant molecular markers from high-dimensional datasets. However, the "small-sample, high-dimensional" nature of this data—where the number of features (e.g., genes, proteins, metabolites) far exceeds the number of observations—poses significant challenges, making feature selection inherently prone to instability [50] [51]. Stability, defined as the ability of a feature selection algorithm to produce consistent feature subsets under slight perturbations in the training data, is essential for ensuring reproducible findings and reliable biomarker discovery [50] [51]. This guide provides a comparative analysis of feature selection methods and stability assessment protocols, offering a structured framework for researchers and practitioners in precision medicine and drug development.

Comparative Analysis of Feature Selection Methods and Their Stability

Feature selection methods can be broadly categorized into filter methods (which select features based on statistical properties), wrapper methods (which use a predictive model's performance to guide selection), and embedded methods (which perform feature selection as part of the model training process) [51]. Recent advances have emphasized ensemble feature selection, which integrates multiple feature subsets to enhance robustness, and is subdivided into homogeneous (using data perturbation with a single base selector) and heterogeneous (combining different selector types) ensembles [50].

Table 1: Comparative Performance of Feature Selection Methods and Stability Metrics

Method Category	Specific Methods	Reported Stability (Index/Metric)	Key Strengths	Key Limitations
Homogeneous Ensemble	MVFS-SHAP [50]	Extended Kuncheva Index: 0.50-0.75 on challenging datasets, >0.80 on 80% of results, >0.90 on Exo/Endo datasets [50]	High stability & predictive performance; robust in high-dimensional, small-sample settings [50]	High computational cost; requires careful hyperparameter tuning [50]
Embedded Methods	Lasso (L1 SVM, Logistic Regression) [51]	Nogueira Stability: Higher stability with stronger regularization (fewer features) [51]	Induces sparsity; interpretable; integrated into model training [51]	Stability decreases with lower regularization; sensitive to data perturbations [51]
Vertical Integration Methods	Matilda, scMoMaT [4]	Feature selection evaluated via clustering, classification, and reproducibility metrics [4]	Identifies cell-type-specific markers; effective for clustering/classification [4]	Selection stability can vary [4]
Vertical Integration Methods	MOFA+ [4]	High reproducibility in feature selection across modalities [4]	High reproducibility of selected features [4]	Selects cell-type-invariant markers; may lack specificity [4]
Tree-Based Models	Conditional Inference Forest (CIF) [52]	Coefficient of Variation (CoV) of R²: 0.12 (most stable among tested algorithms) [52]	High stability and accuracy [52]	--
Tree-Based Models	RF, XGB, BRT [52]	CoV of R²: 0.13-0.15 [52]	High predictive accuracy [52]	Moderate stability compared to CIF [52]

Impact of Data Characteristics and Scenarios on Stability

Stability is profoundly influenced by data characteristics. Analyses of multi-omics cancer data from TCGA reveal that feature stability differs across omics layers; for instance, the mirna layer consistently demonstrates high stability, whereas mutation and rna layers are generally less stable [51]. Furthermore, increasing proportions of missing data consistently lead to evident declines in stability across methods, with different techniques exhibiting varying sensitivity to this issue [53]. Finally, strong correlations among features can produce multiple equally optimal signatures, thereby reducing confidence in the selected features [51].

Experimental Protocols for Stability Assessment

The MVFS-SHAP Workflow for Metabolomics Data

The MVFS-SHAP framework is designed to enhance stability in high-dimensional, small-sample metabolomics data. Its protocol involves a structured, multi-stage process, visualized in the workflow below.

Protocol Steps:

• Data Resampling: Generate multiple sampled datasets from the original high-dimensional data using 5-fold cross-validation and bootstrap sampling techniques [50].
• Base Feature Selection: Apply the same base feature selection method (e.g., Ridge regression) to each resampled dataset to generate corresponding feature subsets [50].
• Ensemble Integration: Integrate these feature subsets using a majority voting strategy to obtain a consolidated list of features [50].
• SHAP-based Re-ranking: Compute feature importance scores using Ridge regression and Linear SHAP. Re-rank the features according to their average SHAP values to form the final representative feature subset [50].
• Validation: Construct a predictive model (e.g., Partial Least Squares regression) using the final feature subset. Evaluate the stability of the selection process through an extended Kuncheva index [50].

General Framework for Multi-Omics Data

For a broader assessment of feature selection stability in multi-omics studies, a generalizable framework is essential.

Table 2: Core Components of a General Stability Assessment Framework

Component	Description	Example Techniques & Metrics
Data Input & Perturbation	Introduces controlled variations to the training data to test selector robustness.	- k-fold Cross-Validation [51]- Bootstrap Sampling [50]
Feature Selection Algorithm	The method(s) under evaluation.	- Embedded (Lasso, SVM-RFE) [51]- Ensemble (MVFS-SHAP) [50]
Stability Metric	Quantifies the similarity between feature subsets from different perturbations.	- Nogueira's Stability Index [51]- Extended Kuncheva Index [50]
Evaluation & Interpretation	Assesses the trade-offs between stability, prediction performance, and biological relevance.	- Predictive Accuracy (RMSE, R²) [50] [52]- Clinical/Biological Validation [5]

Essential Research Reagent Solutions

This section details key computational tools and resources that form the foundational "reagents" for conducting rigorous feature selection and stability analysis in multi-omics research.

Table 3: Key Research Reagent Solutions for Feature Selection & Stability Analysis

Category & Item	Primary Function	Relevance to Feature Selection & Stability
Data Repositories
The Cancer Genome Atlas (TCGA)	Provides comprehensive, multi-layered omics datasets from large cancer cohorts.	A primary public source for high-dimensional, multi-omics data used to benchmark feature selection stability and predictive accuracy [51] [5].
International Cancer Genome Consortium (ICGC)	Offers a global catalog of genomic abnormalities in various tumor types.	Complements TCGA, providing additional datasets for validating the generalizability of feature selection methods [16].
Benchmarking Tools & Metrics
Nogueira's Stability Index	A stability measure that accounts for feature selection by chance, allowing for confidence intervals.	Enables rigorous statistical comparison of the stability between different feature selection algorithms [51].
Extended Kuncheva Index	An adaptation of the Kuncheva index designed for stability assessment.	Used in specialized frameworks like MVFS-SHAP to evaluate the consistency of selected features under data perturbation [50].
Computational Methods
Ensemble Feature Selection Platforms (e.g., MVFS-SHAP)	Implements homogeneous ensemble strategies to aggregate feature subsets from resampled data.	Specifically designed to enhance the stability and reproducibility of feature selection in high-dimensional, small-sample settings [50].
Multi-Omics Integration Tools with FS (e.g., Matilda, MOFA+)	Methods that integrate multiple omics layers and often include built-in feature selection capabilities.	Allow for the identification of features (markers) that are consistent across different data modalities, contributing to stable, biologically relevant discovery [4].
SHAP (SHapley Additive exPlanations)	A game-theoretic approach to explain the output of any machine learning model, providing feature importance scores.	Used within frameworks like MVFS-SHAP to re-rank features based on their consistent contribution across models, improving reliability [50].

Stability Analysis and Downstream Task Correlations

The stability of feature selection is not an isolated goal but is intrinsically linked to the performance of downstream analytical tasks. Research in spatial transcriptomics has demonstrated that the quality of upstream integration and feature selection strongly influences downstream outcomes such as spatial clustering and spatial alignment [7]. This relationship underscores that robust, stable feature selection is a critical prerequisite for obtaining reliable and interpretable biological results in subsequent analyses. Therefore, evaluating stability should be part of a holistic assessment pipeline.

Rigorous Benchmarking Frameworks: Evaluating Method Performance Across Tasks and Modalities

The rapid development of computational methods for multi-omics data integration has created an urgent need for robust, standardized benchmarking studies. These studies enable researchers to objectively compare the performance of different algorithms, guide method selection for specific biological questions, and foster innovation by identifying current limitations and opportunities for improvement. Benchmarking provides the critical evidence base needed to translate computational advances into reliable biological insights and clinical applications, particularly in precision oncology where accurate molecular classification directly impacts treatment decisions [8].

Robust benchmarking requires careful consideration of three interconnected components: appropriate datasets that represent biological and technical diversity, tailored evaluation metrics that reflect real-world analytical tasks, and validation protocols that ensure reproducible and interpretable results. The field has matured from isolated method development to community-driven systematic evaluations, with recent large-scale benchmarks providing key insights into the relative strengths of different integration strategies across various data modalities and analytical tasks [4] [7]. This guide synthesizes current best practices and experimental frameworks for designing comprehensive benchmarking studies that yield actionable recommendations for the research community.

Foundational Components of Benchmarking Studies

Dataset Selection and Curation

The foundation of any robust benchmarking study is the selection of appropriate datasets that represent the biological complexity and technical challenges of real-world data. Different dataset types serve distinct purposes in benchmarking, from evaluating basic functionality to assessing performance on realistic biological problems with known ground truth.

Table 1: Dataset Types for Benchmarking Multi-Omics Integration Methods

Dataset Type	Primary Purpose	Key Characteristics	Example Sources
Real biological datasets	Evaluate performance on realistic biological problems; validate biological relevance	Heterogeneous quality; known biological variation (e.g., cell types); technical artifacts	TCGA, CITE-seq, SHARE-seq, TEA-seq, 10X Multiome [4] [54] [45]
Semi-simulated datasets	Controlled evaluation of specific capabilities (e.g., batch correction)	Real biological structure with introduced technical effects; known ground truth	Modified real datasets with simulated batch effects [4]
Fully simulated datasets	Method validation under ideal conditions; stress-testing specific features	Complete control over parameters; known ground truth; may lack real biological complexity	Simulated single-cell multimodal omics data [4]

Comprehensive benchmarking requires datasets spanning multiple technologies and modalities. For single-cell multimodal omics, this includes popular combinations like RNA+ADT (CITE-seq), RNA+ATAC (SHARE-seq), and three-modal RNA+ADT+ATAC (TEA-seq) [4]. For bulk tissue analysis, The Cancer Genome Atlas (TCGA) provides extensive multi-omics data including gene expression, miRNA, methylation, and copy number variation across multiple cancer types [45] [8]. Spatial transcriptomics benchmarks require datasets from multiple tissue sections with spatial coordinates and domain annotations [7].

Dataset quality parameters significantly impact benchmarking outcomes. Studies recommend including ≥26 samples per class, selecting <10% of omics features through careful feature selection, maintaining sample balance under 3:1 ratio between classes, and ensuring noise levels below 30% for robust performance [45] [41]. These parameters should be explicitly reported in benchmarking studies to enable proper interpretation of results.

Evaluation Metrics for Multi-Omics Integration Tasks

Evaluation metrics must be carefully selected to match the specific analytical tasks that multi-omics integration methods are designed to address. Different metrics capture distinct aspects of performance, and comprehensive benchmarking requires multiple metrics to provide a complete picture of method capabilities.

Table 2: Evaluation Metrics for Multi-Omics Integration Tasks

Analytical Task	Evaluation Metrics	What It Measures	Interpretation Guidelines
Dimension Reduction	iLISI, dLISI	Batch mixing (iLISI) and biological conservation (dLISI)	Higher iLISI indicates better batch correction; higher dLISI indicates better biological preservation [4] [7]
Clustering	Adjusted Rand Index (ARI), Normalized Mutual Information (NMI), Average Silhouette Width (ASW)	Agreement with reference labels; cluster compactness and separation	Higher values indicate better alignment with known biological groups [4] [7]
Batch Correction	Batch ASW (bASW), Graph Connectivity (GC)	Removal of technical batch effects while preserving biology	Lower bASW indicates better batch correction; higher GC indicates better connectivity within batches [4] [7]
Feature Selection	Marker Correlation (MC), Classification Accuracy	Relevance of selected features to biological labels	Higher MC indicates better marker identification; higher classification accuracy indicates more informative features [4]
Spatial Alignment	Alignment Accuracy, Regional Boundary Preservation	Accuracy of spatial coordinate correction between slices	Higher alignment accuracy indicates better spatial reconstruction [7]

Metric selection should align with benchmarking goals. For example, spatial transcriptomics benchmarking employs bASW (batch ASW) to evaluate batch effect removal, iLISI (integration Local Inverse Simpson's Index) to assess batch mixing, and dASW (dataset ASW) to measure biological conservation [7]. Single-cell multimodal benchmarks use iF1 (integration F1 score) for clustering accuracy and NMI_cellType (Normalized Mutual Information) for cell type identification [4].

The inherent trade-offs between metrics must be acknowledged. Methods that excel at batch correction (high iLISI, low bASW) may simultaneously degrade biological signal (low dLISI, low dASW) [7]. Comprehensive benchmarking should report multiple complementary metrics to capture these trade-offs and help users select methods appropriate for their specific analytical priorities.

Experimental Design and Workflow

Benchmarking Framework Architecture

A robust benchmarking framework incorporates multiple analytical tasks arranged in a logical workflow, where upstream outputs feed into downstream applications. This approach captures the interconnected nature of real-world bioinformatics pipelines and evaluates how performance at early stages influences final results.

This workflow illustrates the hierarchical relationship between tasks in spatial transcriptomics benchmarking, where multi-slice integration serves as the foundational step [7]. Similar workflows exist for single-cell multimodal omics, beginning with data integration and proceeding through dimension reduction, clustering, and biological interpretation tasks [4]. The framework emphasizes that downstream performance (e.g., spatial clustering accuracy) depends heavily on upstream integration quality, highlighting the importance of evaluating method performance across multiple connected tasks rather than in isolation.

Validation Protocols and Statistical Rigor

Robust validation requires appropriate data splitting strategies, statistical testing, and sensitivity analyses. For supervised tasks, standardized training/validation/test splits with strict separation between partitions prevent data leakage and overoptimistic performance estimates. The 70/30 split used in survival modeling benchmarks provides a reasonable balance between training data quantity and unbiased evaluation [8]. For unsupervised tasks, evaluation against known biological labels (e.g., cell types, spatial domains) with multiple random initializations assesses method stability.

Statistical significance testing must account for multiple comparisons when evaluating numerous methods across multiple datasets. Non-parametric tests like Wilcoxon signed-rank tests compare method rankings across diverse datasets, acknowledging that absolute performance varies substantially between data types and technologies [4] [7]. Reporting effect sizes alongside p-values helps distinguish statistical significance from practical importance.

Sensitivity analyses evaluate how performance depends on key parameters like feature selection thresholds, dimensionality, and noise levels. Systematic benchmarks demonstrate that feature selection improves clustering performance by 34% on average, with optimal results achieved when selecting <10% of omics features [45] [41]. Similarly, maintaining noise levels below 30% and sample balance under 3:1 ratio produces more reliable and reproducible results [45].

Implementation Considerations

Table 3: Essential Research Reagent Solutions for Multi-Omics Benchmarking

Resource Category	Specific Tools/Platforms	Primary Function	Application Context
Data Archives	TCGA, ICGC, CCLE, CPTAC, TCIA	Provide standardized multi-omics datasets for benchmarking	Bulk tissue analysis; radiogenomics; clinical correlation [45] [55] [8]
Integration Methods	Seurat WNN, Multigrate, scECDA, Flexynesis, GraphST, MENDER	Representative algorithms for different integration categories	Single-cell multimodal omics; spatial transcriptomics; bulk data integration [4] [54] [7]
Evaluation Frameworks	iSTBench, Multi-task Benchmarking Framework	Standardized pipelines for performance assessment	Spatial transcriptomics; single-cell multimodal omics [4] [7]
Visualization Tools	Uniform Manifold Approximation (UMAP), t-SNE	Visual assessment of integration quality	All data types for qualitative evaluation [4]

Successful benchmarking studies combine computational tools with standardized biological resources. Public data repositories like The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA) provide extensively characterized datasets with clinical annotations that enable benchmarking against biologically meaningful endpoints [55] [8]. Method selection should represent major algorithmic categories—deep learning-based (GraphST, SPIRAL), statistical (Banksy, PRECAST), and hybrid approaches (CellCharter, STAligner) for spatial transcriptomics [7]; vertical, diagonal, mosaic, and cross-integration methods for single-cell multimodal data [4].

Specialized benchmarking frameworks like iSTBench for spatial transcriptomics provide standardized evaluation pipelines that ensure fair comparison and reproducibility [7]. Similarly, the multi-task benchmarking framework for single-cell multimodal omics offers tailored evaluation metrics for seven common analytical tasks [4]. These frameworks abstract implementation details, allowing researchers to focus on experimental design and interpretation.

Method Categorization and Selection

Systematic method categorization enables representative sampling of different algorithmic approaches. Single-cell multimodal integration methods are typically classified into four prototypical categories based on input data structure and modality combination: vertical (paired multi-omics data from the same cells), diagonal (overlapping but not identical features), mosaic (partially overlapping cells and features), and cross integration (unpaired modalities) [4]. Each category addresses distinct biological scenarios and requires appropriate evaluation datasets.

Spatial transcriptomics methods fall into three broad categories: deep learning-based (using VAEs or GNNs), statistical (leveraging cellular microenvironments or abundance data), and hybrid approaches (combining deep learning with spatial context) [7]. Bulk multi-omics integration employs early (feature-level), intermediate (representation-level), or late (decision-level) fusion strategies, each with distinct advantages and challenges [56].

Method selection should prioritize diversity in algorithmic approaches rather than simply including the largest number of methods. Comprehensive benchmarks typically evaluate 12-40 methods representing all major categories [4] [7]. Including both established widely-used methods and recent innovative approaches ensures benchmarks remain relevant to current research practices while capturing state-of-the-art advancements.

Case Studies and Applications

Single-Cell Multimodal Omics Benchmark

A landmark registered report in Nature Methods exemplifies comprehensive benchmarking, evaluating 40 integration methods across 64 real datasets and 22 simulated datasets [4]. The study defined four data integration categories (vertical, diagonal, mosaic, cross) and seven common analytical tasks (dimension reduction, batch correction, clustering, classification, feature selection, imputation, spatial registration).

Key findings revealed that method performance is highly dataset-dependent and modality-specific. For RNA+ADT data, Seurat WNN, sciPENN and Multigrate demonstrated strong performance, while UnitedNet and Matilda excelled with RNA+ATAC data [4]. The benchmark highlighted inherent trade-offs—methods that performed well on simulated datasets (e.g., scMM) often struggled with real data complexity, underscoring the importance of diverse dataset inclusion.

This benchmark provided actionable recommendations through overall grand rank scores, enabling researchers to select methods based on their specific data modalities and analytical tasks. The study established that no single method outperforms all others across all scenarios, emphasizing the need for task-specific method selection.

Spatial Transcriptomics Integration Benchmark

A comprehensive evaluation of 12 multi-slice integration methods across 19 datasets revealed substantial performance variation based on application context, dataset size, and technology [7]. The benchmark covered four key tasks: multi-slice integration, spatial clustering, spatial alignment, and slice representation.

Results demonstrated that deep learning-based method GraphST-PASTE excelled at batch effect removal (mean bASW: 0.940) but struggled with biological conservation, while statistical method MENDER and deep learning method STAIG excelled at preserving biological variance (mean dASW: 0.559 and 0.595 respectively) [7]. The study identified strong interdependencies between upstream integration quality and downstream application performance, highlighting that spatial clustering accuracy directly depends on integration quality.

This benchmark provided technology-specific recommendations, noting that method performance varies significantly across platforms like 10X Visium, MERFISH, and STARmap due to differences in resolution and data distribution [7]. The authors made their complete benchmarking workflow publicly available (https://github.com/bm2-lab/iSTBench), enabling researchers to reproduce results or apply the framework to new datasets.

Emerging Challenges and Future Directions

Despite significant advances, multi-omics benchmarking faces several ongoing challenges. Method scalability remains a critical concern as dataset sizes continue to grow, with many algorithms struggling with the computational demands of millions of cells or high-dimensional spatial data [7]. Standardization of evaluation metrics across studies would enhance comparability, though appropriate metrics necessarily vary by data type and analytical task.

Interpretability and biological relevance present additional challenges. While quantitative metrics efficiently capture technical performance, evaluating the biological plausibility of integrated representations requires domain expertise and functional validation [4] [8]. Incorporating pathway analysis and network biology approaches may help bridge this gap between statistical performance and biological meaning.

Future benchmarking efforts should increasingly focus on multi-task evaluation, recognizing that methods are often applied to multiple analytical tasks in real research scenarios [8]. Similarly, as multimodal data become more prevalent, benchmarks must expand to include integration across more than two modalities, such as simultaneous analysis of RNA, ATAC, ADT, and spatial information [4].

The development of more sophisticated simulated benchmarks that better capture the complexity of real biological systems would enable more rigorous method validation. Current benchmarks note that methods performing well on simulated data often struggle with real datasets, indicating a gap in our ability to simulate biological complexity [4]. Closing this gap will produce more reliable benchmarks that better predict real-world performance.

Finally, increased emphasis on reproducibility and accessibility through containerization, workflow systems, and cloud-based implementations will make benchmarking frameworks more accessible to the broader research community [7] [8]. As the field matures, standardized benchmarking will play an increasingly vital role in translating computational innovation into biological discovery and clinical application.

Benchmarking computational methods is a critical step in advancing multi-omics data integration research. As high-throughput technologies generate increasingly complex and voluminous biological data, researchers require robust, scalable, and accurate computational tools to extract meaningful biological insights. This guide provides an objective comparison of method performance across three fundamental computational tasks in omics data analysis: dimensionality reduction, batch correction, and cell type classification. By synthesizing evidence from recent large-scale benchmarking studies, we aim to offer researchers, scientists, and drug development professionals evidence-based recommendations for method selection, along with detailed experimental protocols and practical resources for implementation.

Performance Comparison of Dimensionality Reduction Methods

Dimensionality reduction (DR) techniques are essential for analyzing high-dimensional transcriptomic data, enabling visualization, clustering, and interpretation of complex biological patterns. A comprehensive benchmark evaluated 30 DR methods using the Connectivity Map (CMap) dataset, which contains drug-induced transcriptomic profiles [57]. Performance was assessed under four experimental conditions: different cell lines treated with the same compound, a single cell line treated with multiple compounds, a single cell line treated with compounds targeting distinct molecular mechanisms of action (MOAs), and a single cell line treated with varying dosages of the same compound [57].

Table 1: Performance of Top Dimensionality Reduction Methods on Transcriptomic Data

Method	Local Structure Preservation	Global Structure Preservation	Dose-Response Sensitivity	Key Strengths
t-SNE	Excellent	Good	Strong	Preserves local neighborhoods effectively; good for cluster separation
UMAP	Excellent	Very Good	Moderate	Balances local and global structure; fast computation
PaCMAP	Very Good	Very Good	Moderate	Optimized for both local and global structure preservation
TRIMAP	Very Good	Good	Moderate	Uses triplet constraints to balance local and global relationships
PHATE	Good	Good	Strong	Models data trajectories; captures continuous transitions
Spectral	Moderate	Good	Strong	Effective for detecting subtle, continuous patterns
PCA	Poor	Good	Poor	Preserves global variance but obscures local structure

The benchmark employed three internal cluster validation metrics to assess how well each method preserved biological structure: Davies-Bouldin Index (DBI), Silhouette score, and Variance Ratio Criterion (VRC) [57]. The ranking of DR methods showed high concordance across these metrics (Kendall's W=0.91-0.94, P<0.0001), indicating consistent performance evaluation [57]. The study revealed that method performance varied significantly across different biological contexts, highlighting the importance of selecting DR techniques based on specific analytical goals.

Benchmarking Multi-Omics Integration and Batch Correction

Multimodal Omics Integration Performance

Single-cell multimodal omics technologies have revolutionized biological research by enabling simultaneous measurement of multiple molecular layers in individual cells. A comprehensive Registered Report in Nature Methods systematically categorized and benchmarked 40 integration methods across four data integration categories: vertical (same cells, multiple modalities), diagonal (overlapping but not identical cells), mosaic (different sets of cells and modalities), and cross integration (different modalities across different sets of cells) [4].

Table 2: Top-Performing Multi-Omics Integration Methods Across Tasks

Method	Vertical Integration	Diagonal Integration	Feature Selection	Key Capabilities
Seurat WNN	Top performer	Variable	Limited	Weighted nearest neighbors for multimodal clustering
Multigrate	Top performer	Good	Good	Joint generative modeling of multiple modalities
Matilda	Good	Moderate	Excellent	Cell-type-specific marker identification
sciPENN	Top performer (RNA+ADT)	Moderate	Limited	Deep learning for paired RNA and protein data
scMoMaT	Moderate	Good	Excellent	Matrix factorization; cell-type-specific features
MOFA+	Moderate	Good	Good	Identifies shared factors across modalities

The benchmarking evaluated methods on seven common computational tasks: dimension reduction, batch correction, clustering, classification, feature selection, imputation, and spatial registration [4]. For vertical integration of paired RNA and ADT data, Seurat WNN, sciPENN, and Multigrate demonstrated generally better performance in preserving biological variation of cell types [4]. For RNA+ATAC integration, UnitedNet and Multigrate performed well, while for trimodal data (RNA+ADT+ATAC), Multigrate and Matilda showed robust performance [4].

Batch Correction in Single-Cell Data

Batch effects remain a significant challenge in single-cell genomics, particularly when integrating data across different experiments, studies, and platforms. A benchmark of deep learning approaches for single-cell data integration evaluated 16 methods within a unified variational autoencoder framework, incorporating different loss functions for batch correction and biological conservation [58].

The study implemented a multi-level strategy for single-cell data integration. Level-1 methods focused on batch effect removal using batch labels with techniques including Generative Adversarial Networks (GAN), Hilbert-Schmidt Independence Criterion (HSIC), Orthogonal Projection Loss, Mutual Information Minimization, Reverse Backpropagation, and Reverse Cross-Entropy [58]. Level-2 methods incorporated known cell-type labels as biological conservation constraints using approaches such as supervised contrastive learning, Invariant Risk Minimization, and domain meta-learning [58]. Level-3 integrated both batch labels and cell-type labels simultaneously, combining loss functions from levels 1 and 2 and introducing Domain Class Triplet loss [58].

The benchmarking revealed limitations in existing evaluation metrics, particularly in capturing intra-cell-type biological variation, leading to the development of refined metrics (scIB-E) that better assess biological conservation after integration [58].

Cell Type Classification and Regulatory Element Prediction

Accurate cell type identification is crucial for interpreting single-cell data and understanding biological systems. While conventional methods rely on gene expression patterns, emerging approaches are leveraging transcription factor binding motifs to predict cell-type-specific regulatory elements with high accuracy.

The Bag-of-Motifs (BOM) framework represents a minimalist yet powerful approach for predicting cell-type-specific cis-regulatory elements [59]. BOM represents distal regulatory sequences as unordered counts of transcription factor motifs and uses gradient-boosted trees for classification, achieving 93% accuracy in assigning enhancers to their correct cell type of origin in mouse embryonic data [59].

When benchmarked against other sequence-based classifiers, BOM significantly outperformed established methods. It achieved a mean area under the precision-recall curve of 0.99 and Matthews correlation coefficient of 0.93, surpassing LS-GKM by 17.2% in auPR, DNABERT by 55.1%, and Enformer by 10.3% [59]. This performance demonstrates that a simplified motif-based representation can capture essential regulatory codes governing cell identity while offering direct interpretability.

Experimental Protocols for Benchmarking Studies

Dimensionality Reduction Benchmarking Protocol

The benchmarking of dimensionality reduction methods followed a standardized protocol [57]:

• Data Collection: 2,166 drug-induced transcriptomic change profiles from nine cell lines (A549, HT29, PC3, A375, MCF7, HA1E, HCC515, HEPG2, and NPC) from the Connectivity Map dataset
• Data Representation: Each profile represented as z-scores for 12,328 genes
• Evaluation Framework:
  • Internal validation: Davies-Bouldin Index, Silhouette score, Variance Ratio Criterion
  • External validation: Normalized Mutual Information and Adjusted Rand Index after clustering
  • Visual inspection of 2D embeddings
• Experimental Conditions:
  • Different cell lines treated with the same compound
  • Single cell line treated with multiple compounds
  • Single cell line treated with compounds targeting distinct MOAs
  • Dose-response relationships

Multi-Omics Integration Benchmarking Protocol

The multi-omics integration benchmarking followed a rigorous, preregistered protocol [4]:

• Dataset Curation: 64 real datasets and 22 simulated datasets covering various modality combinations
• Method Categorization: Classification into vertical, diagonal, mosaic, and cross integration
• Task-Specific Evaluation:
  • Dimension reduction: Visualization quality, biological preservation
  • Batch correction: Batch mixing, biological conservation
  • Clustering: Alignment with known cell type labels
  • Feature selection: Identification of informative markers
  • Classification: Transfer learning accuracy
• Metric Selection: Tailor-made metrics for each task, including ASWcellType, iF1, NMIcellType for clustering

Workflow Diagram for Method Benchmarking

Table 3: Key Computational Tools and Data Resources for Multi-Omics Benchmarking

Resource	Type	Primary Function	Application Context
Connectivity Map (CMap)	Dataset	Drug-induced transcriptomic profiles	Evaluating DR method performance on pharmacological data [57]
Seurat WNN	Software	Weighted nearest neighbor integration	Multimodal data integration and clustering [4]
Multigrate	Software	Joint generative modeling	Vertical integration of multiple modalities [4]
scVI/scANVI	Software	Variational autoencoder framework	Single-cell data integration with batch correction [58]
BOM Framework	Software	Bag-of-motifs classifier	Cell-type-specific regulatory element prediction [59]
SynOmics	Software	Graph convolutional networks	Multi-omics integration via feature interaction networks [60]
Harmony	Software	Batch integration algorithm	Removing technical effects while preserving biology [58]
Single-cell Integration Benchmarking (scIB)	Framework	Performance metrics	Standardized evaluation of integration methods [58]

This comparative guide synthesizes evidence from recent large-scale benchmarking studies to evaluate computational methods for dimensionality reduction, batch correction, and cell type classification. The evidence reveals that method performance is highly context-dependent, varying based on data modalities, biological questions, and analytical tasks. For dimensionality reduction, t-SNE, UMAP, and PaCMAP excel at capturing drug response patterns in transcriptomic data. For multi-omics integration, Seurat WNN, Multigrate, and Matilda demonstrate robust performance across multiple tasks. For batch correction, deep learning approaches within variational autoencoder frameworks show particular promise when appropriately regularized. Researchers should select methods based on their specific data characteristics and analytical goals, considering the trade-offs between computational efficiency, interpretability, and biological fidelity highlighted in this guide. As the field evolves, continued benchmarking efforts will be essential for guiding method selection and development in multi-omics research.

In the rapidly evolving field of computational biology, benchmarking serves as the cornerstone for evaluating the performance of analytical methods. For multi-omics data integration research—where scientists combine genomic, transcriptomic, proteomic, and other molecular data—benchmarking provides an essential framework for navigating complex algorithmic choices. As noted in Nature Biomedical Engineering, benchmarking is crucial for biomedical advancement, distinguishing incremental improvements from genuine breakthroughs and providing the comparative data needed to validate performance claims [61]. The exponential growth in computational tools, with over 1,500 tools for single-cell RNA-sequencing analysis alone, has created both unprecedented opportunities and significant challenges for researchers seeking to select appropriate methods for their specific biological questions [62].

This guide examines the fundamental trade-offs between three critical performance dimensions—accuracy, stability, and reproducibility—that researchers must navigate when interpreting benchmarking results for multi-omics integration methods. Through systematic analysis of current benchmarking studies and performance data, we provide a structured framework for evaluating these competing priorities and making informed decisions about method selection.

Performance Landscape of Multi-Omics Integration Methods

Quantitative Performance Across Integration Categories

Systematic benchmarking of computational methods requires evaluating performance across multiple tasks and data modalities. A comprehensive 2025 Registered Report in Nature Methods evaluated 40 integration methods across 4 data integration categories and 7 common tasks using 64 real datasets and 22 simulated datasets [4]. The performance landscape reveals significant trade-offs between accuracy, stability, and reproducibility across different methodological approaches.

Table 1: Overall Performance Rankings of Vertical Integration Methods by Data Modality

Method	RNA+ADT Rank	RNA+ATAC Rank	RNA+ADT+ATAC Rank	Accuracy	Stability	Reproducibility
Seurat WNN	1	1	2	High	Medium	High
Multigrate	2	3	1	High	Medium	Medium
sciPENN	3	5	4	Medium	High	High
Matilda	4	2	3	High	Low	Medium
UnitedNet	5	4	5	Medium	High	Medium
MOFA+	8	7	6	Low	High	High

The data reveals that method performance is highly dataset-dependent and modality-dependent [4]. For instance, Seurat WNN and Multigrate demonstrated generally better performance on RNA+ADT datasets, effectively preserving biological variation of cell types [4]. However, performance rankings shifted significantly when these same methods were applied to RNA+ATAC or trimodal (RNA+ADT+ATAC) datasets, highlighting the critical importance of context in method evaluation.

Performance Across Computational Tasks

Different methods excel at specific computational tasks, creating another dimension of trade-offs in method selection. The Nature Methods benchmarking evaluated methods across seven common tasks: dimension reduction, batch correction, clustering, classification, feature selection, imputation, and spatial registration [4].

Table 2: Task-Specific Performance of Selected Methods

Method	Dimension Reduction	Clustering	Batch Correction	Feature Selection	Computation Speed
Seurat WNN	High	High	Medium	Not Supported	Fast
Multigrate	High	High	High	Not Supported	Medium
Matilda	Medium	Medium	Low	High	Slow
scMoMaT	Medium	Medium	Medium	High	Slow
MOFA+	Low	Low	High	Medium	Fast

For feature selection tasks, only Matilda, scMoMaT, and MOFA+ supported identifying molecular markers from single-cell multimodal omics data [4]. Notably, Matilda and scMoMaT identified distinct markers for each cell type, while MOFA+ selected a single cell-type-invariant set of markers for all cell types, representing different approaches with distinct trade-offs between specificity and generality [4].

Experimental Protocols in Benchmarking Studies

Standardized Benchmarking Frameworks

Robust benchmarking requires standardized experimental protocols to ensure fair comparisons. The benchmarking study published in Nature Methods established a comprehensive protocol that can serve as a template for future evaluations [4]. Their methodology included:

• Dataset Curation: 64 real datasets and 22 simulated datasets spanning different technology platforms (CITE-seq, SHARE-seq, TEA-seq) and modality combinations (RNA+ADT, RNA+ATAC, RNA+ADT+ATAC) [4].
• Task Definition: Seven common computational tasks relevant to biological discovery: dimension reduction, batch correction, clustering, classification, feature selection, imputation, and spatial registration [4].
• Evaluation Metrics: Tailor-made metrics for each task, including accuracy measures (iF1, NMIcellType), stability assessments (ASWcellType, iASW), and reproducibility indicators [4].
• Statistical Analysis: Calculation of overall rank scores summarized across evaluation metrics and datasets, with visualization of performance variation across individual datasets [4].

Realistic Data Simulation Strategies

For spatial transcriptomics benchmarking, researchers have developed advanced simulation strategies to overcome the lack of established ground truth in real-world tissues. A 2025 study in Genome Biology employed the scDesign3 framework to generate biologically realistic data by modeling gene expression as a function of spatial locations with a Gaussian Process model [63]. This approach represented a significant advancement over previous simulations that relied on pre-defined spatial clusters or limited spatial patterns, better capturing the rich diversity of patterns observed in real biological systems [63].

The following workflow diagram illustrates the key stages in a comprehensive benchmarking protocol for multi-omics integration methods:

Diagram 1: Comprehensive benchmarking workflow for multi-omics methods, showing key stages from scope definition to final recommendations.

The Accuracy-Stability-Reproducibility Trade-off

Fundamental Tensions in Method Performance

The interpretation of benchmarking results requires careful consideration of the inherent tensions between accuracy, stability, and reproducibility. These trade-offs represent fundamental challenges in computational method development and selection:

• Accuracy vs. Stability: Methods that achieve high accuracy on specific datasets may demonstrate variable performance across different data types or technological platforms. For example, while Matilda showed high accuracy for feature selection in RNA+ADT data, its performance was less stable across different modality combinations compared to MOFA+, which provided more consistent but less accurate results [4].

• Accuracy vs. Reproducibility: Highly accurate methods often incorporate dataset-specific optimizations that limit their reproducibility across studies. The 2025 landscape analysis of single-cell benchmarking studies highlighted that reproducibility remains a significant challenge, with limited cross-validation of results across different research groups [62].

• Stability vs. Computational Efficiency: Methods that maintain stable performance across diverse datasets often achieve this stability through computational approaches that sacrifice efficiency. For instance, in spatial transcriptomics benchmarking, SPARK-X achieved the best overall performance across metrics, while SOMDE demonstrated superior scalability across memory usage and running time [63].

Context-Dependent Performance Variations

The trade-offs between accuracy, stability, and reproducibility are highly context-dependent, varying based on specific data characteristics and analytical goals. The Nature Methods benchmarking revealed that dataset complexity significantly affects integration method performance, with some methods that performed well on simulated datasets demonstrating poor performance on real datasets with more complex latent structures [4].

The following diagram illustrates the interconnected relationships and trade-offs between the three core performance dimensions:

Diagram 2: Interconnected relationships and trade-offs between accuracy, stability, and reproducibility in benchmarking results, showing how these are influenced by contextual factors.

Successful benchmarking and method evaluation require access to standardized computational frameworks, reference datasets, and evaluation metrics. The following table details key resources for researchers conducting benchmarking studies in multi-omics integration:

Table 3: Essential Research Reagents and Computational Resources for Multi-Omics Benchmarking

Resource Category	Specific Tool/Resource	Function/Purpose	Access Information
Benchmarking Platforms	Open Problems in Single-Cell Analysis	Living platform for hosting single-cell analysis tasks	https://openproblems.bio
	IOHprofiler	Performance analysis and visualization	https://iohprofiler.github.io/IOHanalyzer/
Data Simulation Tools	scDesign3	Realistic simulation of single-cell and spatial data	R/Bioconductor package
	BBOB Test Suite	Synthetic benchmark functions	https://coco-platform.org/testsuites/bbob/
Evaluation Metrics	iF1, NMI_cellType	Clustering accuracy assessment	Custom implementation
	ASW_cellType, iASW	Stability and integration assessment	Custom implementation
Method Collections	SCRNA-tools.org	Catalog of single-cell RNA-seq tools	https://www.scrna-tools.org
Color Accessibility Tools	ColorBrewer	Color-blind safe palettes	https://colorbrewer2.org
	Coblis	Color blindness simulator	https://www.color-blindness.com/coblis-color-blindness-simulator/

Experimental Design Considerations

Beyond specific software tools, effective benchmarking requires careful experimental design to ensure meaningful results. Key considerations include:

• Dataset Diversity: Inclusion of both real and simulated datasets spanning multiple technology platforms and biological contexts [4] [63].
• Metric Selection: Employing multiple complementary metrics to capture different performance dimensions (accuracy, stability, reproducibility) [4].
• Statistical Robustness: Accounting for algorithmic bias and stochasticity through multiple runs and appropriate statistical testing [64].
• Accessibility Reporting: Ensuring visualizations are interpretable by all researchers, including those with color vision deficiencies [65] [66].

Interpreting benchmarking results for multi-omics integration methods requires careful consideration of the fundamental trade-offs between accuracy, stability, and reproducibility. As the comprehensive Nature Methods study demonstrated, method performance is highly context-dependent, varying by data modality, computational task, and dataset characteristics [4]. There is no universally superior method—the optimal choice depends on the specific research context, analytical priorities, and available computational resources.

Researchers should select methods based on their specific needs: prioritizing accuracy when studying well-characterized systems with standardized data types, emphasizing stability when working across diverse datasets or technological platforms, and valuing reproducibility when building analytical pipelines for long-term research programs. As benchmarking practices continue to evolve, the development of more realistic simulation frameworks, standardized evaluation metrics, and community-maintained performance databases will further enhance our ability to navigate these critical trade-offs and select the most appropriate methods for advancing multi-omics research.

Single-cell multimodal omics technologies have revolutionized biology by enabling researchers to simultaneously profile multiple molecular layers—such as the transcriptome, epitope-based proteome, and chromatin accessibility—within individual cells [4]. The rapid development of these technologies has spurred the creation of numerous computational methods designed to integrate these diverse data types. However, this innovation has created a significant challenge for researchers: navigating and selecting the most appropriate integration approach among the growing number of available options [4].

The performance of these integration methods is highly contingent on both the specific analytical tasks researchers aim to perform and the particular combination of modalities present in their data [4]. To address this challenge, several large-scale benchmarking studies have recently been conducted to provide evidence-based guidelines for method selection. This case study synthesizes insights from these comprehensive evaluations, focusing on their experimental designs, key findings, and practical recommendations for the scientific community.

Methodological Frameworks for Benchmarking Studies

Categorization of Integration Problems

To systematically evaluate computational methods, benchmarking studies first established clear categorizations of single-cell multimodal omics integration problems. One widely adopted framework defines four prototypical integration categories based on input data structure and modality combination [4]:

• Vertical integration: Analyzing data from the same cells profiled for multiple modalities (e.g., paired RNA and protein measurements from CITE-seq)
• Diagonal integration: Integrating datasets profiling different modalities collected from different cells
• Mosaic integration: Combining datasets where some cells have multiple modalities measured while others have only single modalities
• Cross integration: Transferring information across modalities or predicting one modality from another

Benchmarking Design and Evaluation Metrics

Recent benchmarking studies employed rigorous experimental designs to evaluate method performance across diverse scenarios. The largest such study assessed 40 integration methods across 64 real datasets and 22 simulated datasets, creating an extensive framework for comparison [4]. Evaluations spanned seven common computational tasks: dimension reduction, batch correction, clustering, classification, feature selection, imputation, and spatial registration [4].

These studies employed task-specific evaluation metrics to ensure comprehensive assessment. For dimension reduction and clustering, metrics included Adjusted Rand Index (ARI), Normalized Mutual Information (NMI), Average Silhouette Width (ASW), and their integrated counterparts (iF1, iASW) [4] [67]. For imputation tasks, researchers used Pearson correlation coefficient (PCC) and root mean square error (RMSE) to quantify accuracy [68].

Table 1: Overview of Key Benchmarking Studies

Study	Number of Methods	Datasets	Key Tasks Evaluated	Primary Metrics
Liu et al. [4]	40 integration methods	64 real, 22 simulated	Dimension reduction, batch correction, clustering, classification, feature selection, imputation, spatial registration	ARI, NMI, ASW, iLISI, PCC, RMSE
Protein Imputation Benchmark [68]	12 imputation methods	11 CITE-seq/REAP-seq	Protein prediction accuracy, robustness, scalability	PCC, RMSE, ARS, RCS
Hu et al. [6]	14 prediction, 18 integration methods	47 multi-omics datasets	Protein/accessibility prediction, data integration	Multiple task-specific metrics
Clustering Benchmark [67]	28 clustering algorithms	10 paired transcriptomic/proteomic	Clustering performance, resource usage	ARI, NMI, running time, memory

Experimental Protocols for Key Evaluations

Evaluation of Vertical Integration

The evaluation of vertical integration methods focused on their performance for dimension reduction and clustering tasks. For RNA+ADT data, 14 methods were systematically benchmarked on 13 datasets, while 14 methods were tested on 12 RNA+ATAC datasets, and 5 methods on 4 trimodal datasets (RNA+ADT+ATAC) [4].

The experimental protocol involved running each method on each dataset using standardized preprocessing steps. Performance was quantified using multiple metrics that captured different aspects of performance. For instance, methods like Seurat WNN, sciPENN, and Multigrate demonstrated strong performance on a representative PBMC dataset with RNA+ADT data, effectively preserving biological variation of cell types [4]. Interestingly, method performance showed significant dataset and modality dependence, with no single method dominating across all scenarios.

Benchmarking Cross-Omics Imputation

The benchmarking of cross-omics imputation methods employed a particularly comprehensive design to evaluate 12 state-of-the-art methods across six distinct scenarios [68]:

• Random holdout: Dataset randomly divided into training and test sets
• Different training data sizes: Evaluating performance with varying training data sizes
• Different samples: Training and test from different biological samples
• Different tissues: Generalizability across tissue types
• Different clinical states: Transferring across biological conditions
• Different protocols: Performance across sequencing technologies

This multi-scenario approach provided insights into method robustness and generalizability—critical considerations for real-world applications. For each experiment, the training dataset contained paired transcriptomic and proteomic data, while the test dataset had masked proteomic data to simulate scRNA-seq-only data [68].

Assessment of Clustering Algorithms

A separate benchmarking study specifically evaluated 28 clustering algorithms on 10 paired transcriptomic and proteomic datasets [67]. This study employed six evaluation metrics (ARI, NMI, Clustering Accuracy, Purity, Peak Memory, and Running Time) to provide a comprehensive assessment of clustering performance and computational efficiency [67].

The experimental design included not only evaluation on real datasets but also robustness assessment using 30 simulated datasets with varying noise levels and dataset sizes. Additionally, the study investigated the impact of highly variable genes (HVGs) and cell type granularity on clustering performance [67].

Key Findings and Performance Rankings

Performance Leaders Across Integration Categories

Benchmarking studies revealed that performance is highly context-dependent, with different methods excelling in specific scenarios:

Table 2: Top-Performing Methods Across Different Tasks and Modalities

Integration Category	Top-Performing Methods	Key Strengths	Data Modalities
Vertical integration	Seurat WNN, Multigrate, Matilda, UnitedNet	Effective dimension reduction, clustering, and feature selection	RNA+ADT, RNA+ATAC, RNA+ADT+ATAC
Protein imputation	Seurat v4 (PCA), Seurat v3 (PCA), TotalVI, scArches	High prediction accuracy, robustness to technical/biological variations	RNA to ADT
Chromatin accessibility prediction	LS_Lab	Top performance in most cases	RNA to ATAC
Multi-omics clustering	scAIDE, scDCC, FlowSOM	High ARI and NMI across modalities	Integrated transcriptomic and proteomic

For vertical integration, the top methods varied by modality combination. For RNA+ADT data, Seurat WNN, sciPENN, and Multigrate demonstrated strong performance, while for RNA+ATAC data, Seurat WNN, Multigrate, Matilda, and UnitedNet generally performed well across diverse datasets [4]. For trimodal data (RNA+ADT+ATAC), a smaller set of methods including Seurat WNN, Multigrate, and Matilda showed robust performance [4].

For cross-omics imputation, Seurat v4 (PCA) and Seurat v3 (PCA) demonstrated exceptional performance across multiple evaluation scenarios, offering promising avenues for further research in single-cell omics [68]. These methods showed high accuracy, robustness across experiments, and relative insensitivity to training data size.

Specialized Method Performance

Feature Selection Capabilities

For the specialized task of feature selection—identifying molecular markers associated with specific cell types—only a subset of vertical integration methods offered this functionality. Among them, Matilda and scMoMaT demonstrated capability in identifying distinct markers for each cell type in a dataset, while MOFA+ selected a single cell-type-invariant set of markers for all cell types [4].

Evaluation of the selected markers revealed that features identified by scMoMaT and Matilda generally led to better clustering and classification of cell types than those by MOFA+ [4]. However, MOFA+ generated more reproducible feature selection results across different data modalities.

Clustering Performance Across Modalities

The benchmarking of clustering algorithms revealed that top-performing methods for transcriptomic data (scDCC, scAIDE, and FlowSOM) also excelled for proteomic data, though in a slightly different order: scAIDE ranked first, followed by scDCC and FlowSOM [67]. This cross-modal performance is particularly valuable for researchers working with multiple data types.

Research Reagent Solutions

The following table details key experimental and computational reagents essential for conducting single-cell multimodal omics studies and implementing the integration methods discussed in this review.

Table 3: Essential Research Reagents and Computational Tools for Single-Cell Multimodal Omics

Reagent/Tool	Type	Primary Function	Example Applications
CITE-seq	Wet-lab protocol	Simultaneous profiling of transcriptomes and surface proteins	Generating paired RNA+protein data for vertical integration [4] [68]
SHARE-seq	Wet-lab protocol	Concurrent measurement of gene expression and chromatin accessibility	Producing RNA+ATAC data for diagonal integration [4]
TEA-seq	Wet-lab protocol	Integrated profiling of transcriptome, epitopes, and chromatin accessibility	Creating trimodal data for complex integration tasks [4]
Seurat Suite	Computational tool	Multimodal data integration, imputation, and analysis	Vertical integration, protein imputation, cross-modality prediction [4] [68]
TotalVI	Computational method	Joint probabilistic modeling of transcriptome and proteome	Protein abundance prediction, multi-omics integration [68] [6]
MOFA+	Computational framework	Factor analysis for multi-omics data integration	Identifying latent factors driving variation across modalities [4]
Multigrate	Integration method	Deep learning-based multimodal integration	Vertical integration of transcriptomic and proteomic data [4]

Workflow and Method Selection Guidance

Based on the comprehensive benchmarking results, we propose the following workflow diagram to guide researchers in selecting appropriate methods for single-cell multimodal omics analysis. The diagram outlines key decision points and recommends methods based on data characteristics and analysis goals.

Large-scale multi-task benchmarking studies have provided invaluable insights into the rapidly evolving landscape of single-cell multimodal omics integration methods. The evidence consistently shows that method performance is highly context-dependent, varying significantly across data modalities, analytical tasks, and dataset characteristics.

Based on the comprehensive evaluations conducted across hundreds of datasets and scenarios, researchers can now make informed decisions when selecting integration approaches. For vertical integration tasks, Seurat WNN, Multigrate, and Matilda demonstrate robust performance across multiple modalities. For cross-omics imputation, particularly protein prediction from RNA data, Seurat v4 (PCA) and Seurat v3 (PCA) offer superior accuracy and robustness. For clustering integrated data, scAIDE, scDCC, and FlowSOM consistently achieve high performance.

These benchmarking efforts not only guide current method selection but also highlight areas needing further development, such as improved scalability, better handling of complex biological variations, and more effective integration of spatial multi-omics data. As single-cell technologies continue to evolve, ongoing benchmarking will remain essential for navigating this complex methodological landscape and maximizing the biological insights derived from single-cell multimodal omics studies.

Conclusion

Systematic benchmarking is paramount for advancing the application of multi-omics data integration in biomedical research. This synthesis reveals that no single method universally outperforms others; instead, optimal selection is highly contingent on the specific data modalities, scientific objectives, and computational tasks at hand. Network-based and ensemble machine learning methods have demonstrated particular promise in drug discovery contexts, such as target identification and response prediction, by effectively capturing complex biological interactions. Future progress hinges on developing more interpretable models, incorporating temporal and spatial dynamics, and establishing community-wide standardized evaluation frameworks. As the field evolves, robust benchmarking will continue to guide researchers toward more reliable, reproducible, and biologically insightful analyses, ultimately accelerating the translation of multi-omics data into clinical and therapeutic breakthroughs.

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