Integrative Metabolome and Transcriptome Analysis - Creative Proteomics Metabolomics

Q: What is the difference between transcriptomics, metabolomics, and integrated dual-omics analysis?

Transcriptomics measures gene expression (regulatory program). Metabolomics measures biochemical end-products (phenotype). Integrated dual-omics connects both layers, identifying which transcriptional changes propagate to the metabolic level and revealing gene-metabolite regulatory relationships invisible to either layer alone.

Q: Which correlation method is best for my dataset — Pearson, Spearman, or WGCNA?

Pearson for linear relationships in normalized data. Spearman for rank-based, robust to outliers. WGCNA for co-varying modules rather than individual pairs. We apply multiple methods and prioritize convergent findings.

Q: How many biological replicates do I need for dual-omics integration analysis?

Minimum 4–6 biological replicates per group. Fewer than 4 limits correlation power and prevents WGCNA. Paired samples — both omics from the same specimen — are essential for meaningful correlation.

Q: What is KEGG pathway co-mapping and how is it different from standard pathway enrichment?

Co-mapping overlays both DEGs and DAMs onto the same KEGG pathway diagram using Pathview, showing which enzyme-gene-metabolite triads are jointly perturbed — a level of mechanistic resolution that intersecting two enrichment lists cannot provide.

Q: Can I submit samples for both transcriptomics and metabolomics, or do I need to provide one dataset?

Both options available. Recommended: submit samples for both (we split at intake). Alternatively: metabolomics on your samples + integration with your existing RNA-seq. Or: standalone bioinformatics integration of both existing datasets.

Transcriptome and Metabolome Integration — Why Dual-Omics Reveals What Single-Omics Misses

Transcriptomics measures which genes are expressed — capturing the cell's regulatory potential. Metabolomics measures the biochemical end-products — the metabolites that actually mediate phenotype.

Neither layer alone tells the full story. Transcript levels do not always predict enzyme activity. Metabolite changes can arise from post-translational regulation invisible to RNA-seq. Integrating both layers filters transcriptional noise through metabolic evidence, identifying which gene expression changes actually propagate to the metabolic level — and which do not.

For researchers, the analytical challenge is integrating two fundamentally different data types — continuous transcriptomic matrices and semi-quantitative metabolomic feature tables — into a single coherent biological narrative. Our service resolves this by applying multiple complementary methods rather than relying on a single algorithm:

Pairwise correlation (Pearson and Spearman) identifies direct gene-metabolite relationships — for example, a positive correlation between PAL expression and cinnamic acid concentration confirms transcriptional regulation of phenylpropanoid flux.
WGCNA (Weighted Gene Co-Expression Network Analysis) groups genes and metabolites into co-varying modules, linking transcriptional programs to metabolic phenotypes at the network level rather than one pair at a time.
KEGG pathway co-mapping overlays differentially expressed genes and differentially accumulated metabolites onto shared biochemical pathways, identifying convergent regulation at the pathway level with enrichment statistics for each omics layer.

Why Choose Our Integrated Transcriptome and Metabolome Analysis?

Multi-Method Correlation, Transparent and Interpretable
We apply Pearson, Spearman, WGCNA, CCorA, and O2PLS as complementary tools — each answering a different question about your data. The final report synthesizes convergent findings across methods. You receive the full analytical rationale, not a black-box "integrated score."
Pathway-Level Integration with KEGG Pathview
Both differentially expressed genes and differentially abundant metabolites are mapped onto shared KEGG pathway diagrams, identifying which specific enzyme-gene-metabolite triads are jointly perturbed — a level of resolution that intersecting enrichment lists cannot provide.
Network Visualizations That Communicate Results
Annotated correlation networks with node size reflecting fold-change, edge color showing correlation direction, and node shape distinguishing transcripts from metabolites — designed for clear communication of regulatory architecture to collaborators, stakeholders, and reviewers.

Three Ways to Work With Us

Full-Service Dual-Omics

Send us your samples. We handle everything — RNA-seq library prep and sequencing, LC-MS/MS metabolomics acquisition, and multi-method integration analysis — in a single project workflow with one point of contact.

Metabolomics + Integration

Already have RNA-seq data? Send us your samples — we acquire LC-MS/MS metabolomics data and integrate with your existing transcriptomic dataset. No need to re-sequence.

Bioinformatics-Only Integration

You have both datasets ready. We perform multi-method correlation, WGCNA, KEGG pathway co-mapping, and deliver the complete analysis report — no wet-lab work, pure computational integration.

Which Integration Method Fits Your Research Question?

Different biological questions require different integration strategies. The table below summarizes the key methods in our analytical framework and the questions each method answers.

Method	Question Answered	Output	Best For
Pearson / Spearman Correlation	Which individual genes and metabolites are quantitatively linked?	Correlation matrix, ranked gene-metabolite pairs, significance-filtered edge lists	Hypothesis-driven studies with predefined gene or metabolite candidates
WGCNA	Which groups of genes and metabolites co-vary as modules?	Co-expression/co-abundance modules, module-trait associations, hub gene/metabolite identification	Exploratory studies, complex traits with unknown molecular drivers
CCorA (Canonical Correlation)	What is the overall strength of association between the transcriptome and metabolome datasets?	Canonical correlation coefficients, significance tests, canonical variate loadings	Global assessment of transcriptome-metabolome concordance
O2PLS (Two-Way Orthogonal PLS)	Which components of the transcriptomic and metabolomic data are jointly driven vs. unique to each layer?	Joint and orthogonal component scores, loadings, variance decomposition	Multi-omics data fusion, separating shared from layer-specific variation
KEGG Pathway Co-Mapping	Which biochemical pathways are convergently regulated at both the gene and metabolite level?	Pathview diagrams with overlaid gene expression and metabolite abundance, enrichment p-values per layer	Pathway-centric mechanism studies, drug target identification
Nine-Quadrant Analysis	What is the concordance of fold-change direction between transcripts and metabolites?	Nine-quadrant scatter plot, concordance/discordance statistics by quadrant	Assessing global agreement in omics-level regulation

Not every project requires all methods. During study design, we recommend the analysis strategy that matches your biological question, sample size, and data structure. A typical project applies 3–4 complementary methods and synthesizes convergent findings.

Instrumentation — RNA-seq and LC-MS/MS Platforms for Dual-Omics Data Acquisition

Transcriptomics (RNA-seq)

Sequencing Platform: Illumina NovaSeq 6000 / HiSeq 4000

Library Preparation: Strand-specific mRNA-seq (polyA enrichment) or rRNA-depleted total RNA-seq; TruSeq or NEBNext kits

Read Depth: 20–50 million paired-end reads per sample (150 bp PE); customizable for species with larger genomes

Data Processing: FastQC → Trimmomatic → STAR/HISAT2 alignment → featureCounts quantification → DESeq2/edgeR differential expression

Quality Metrics: Q30 ≥ 85%, alignment rate ≥ 80% (model organisms), rRNA contamination < 5%

Metabolomics (LC-MS/MS)

Mass Spectrometer: SCIEX QTRAP 6500+ or Thermo Q Exactive HF-X (HRAM)

LC System: Waters ACQUITY UPLC with HILIC (polar metabolites) and C18 (lipids, non-polar) columns

Acquisition: Full-scan MS1 (untargeted, 70–1,050 m/z) + data-dependent MS/MS (Top 10); polarity switching

Data Processing: XCMS/MZmine 3 peak detection → CAMERA annotation → MetaboAnalyst / in-house pipeline → KEGG/HMDB/METLIN identification (Level 1–2)

Quality Metrics: Pooled QC RSD ≤ 20% for features retained; internal standard recovery 80–120%; blank feature removal

Integration Software and Computational Environment

Correlation and Network Analysis: R (Hmisc, corrplot, igraph), Python (NumPy, SciPy, NetworkX); WGCNA package; mixOmics (CCA, PLS, sPLS-DA); OmicsPLS (O2PLS)
Pathway Mapping: KEGG pathview, KEGG mapper, clusterProfiler (GO/KEGG over-representation), MetaboAnalyst 6.0, xOmicsShiny
Visualization: ggplot2, ComplexHeatmap, Cytoscape (network visualization), Pathview (pathway overlay figures)

Dual-Omics Integration Workflow — From Paired Samples to Biological Insight

Study Design and Sample Preparation

We design a paired sampling strategy — RNA and metabolites extracted from the same biological sample whenever possible (e.g., tissue split at collection, one portion into RNA later, one portion snap-frozen for metabolomics). This eliminates inter-sample variability as a confounding factor in correlation analysis. Sample size, biological replicates, and batch design are configured for statistical power in both omics layers.

Dual-Omics Data Acquisition

RNA-seq: library preparation, quality control, and sequencing on Illumina NovaSeq 6000 (20–50M PE reads/sample). Metabolomics: untargeted LC-MS/MS acquisition in positive and negative ion modes with pooled QC injections every 8 samples. Both datasets are acquired with the same sample identifiers and metadata structure for seamless downstream integration.

Single-Omics Differential Analysis

Transcriptomics: DESeq2/edgeR differential expression (|log2FC| ≥ 1, FDR < 0.05). Metabolomics: peak detection, alignment, gap-filling, and statistical testing (t-test/ANOVA with FDR correction). Each layer is independently quality-controlled before integration — we do not integrate noise.

Multi-Method Integration Analysis

Pearson/Spearman correlation for direct gene-metabolite pairs. WGCNA for co-varying modules. CCorA for global transcriptome-metabolome concordance. O2PLS for joint vs. layer-specific variation decomposition. KEGG pathway co-mapping with Pathview overlay. Nine-quadrant analysis for fold-change direction concordance. Results are cross-referenced across methods — convergent findings are prioritized.

Data Delivery and Final Report

Complete data package: differential expression and abundance tables, correlation matrices and ranked gene-metabolite pairs, WGCNA module assignments with hub genes, KEGG pathview diagrams with overlaid omics data, annotated correlation networks in Cytoscape-compatible format, nine-quadrant concordance plots, and a comprehensive analysis report with fully documented methods.

Integrated Transcriptome and Metabolome Analysis Workflow

Sample Requirements for Transcriptomics and Metabolomics — Paired Collection Guidelines

Sample Type	Amount (Transcriptomics)	Amount (Metabolomics)	Collection Notes
Tissue (animal/plant)	≥ 30 mg (RNAlater or snap-frozen)	≥ 30 mg (snap-frozen, separate aliquot)	Split tissue immediately at collection: one piece into RNAlater (4°C overnight, then −80°C), one piece snap-frozen in liquid N₂ for metabolomics. Do not freeze-thaw.
Cell Pellets	≥ 1 × 10⁶ cells (TRIzol or RNAlater)	≥ 2 × 10⁶ cells (snap-frozen pellet)	Harvest and split from the same culture flask; wash twice with cold PBS before freezing. Include a medium blank for metabolomics.
Blood / Plasma	PAXgene tube (2.5 mL) or Tempus tube	≥ 150 μL plasma (EDTA, fasting)	For transcriptomics: PAXgene or Tempus tube, invert 8–10 times, room temperature for 2 h, then −80°C. For metabolomics: separate plasma within 30 min of collection.
Plant Leaf / Root	≥ 100 mg (RNAlater or snap-frozen)	≥ 100 mg (snap-frozen, separate aliquot)	Harvest at consistent time of day. For transcriptomics: RNAlater within 30 s of excision. For metabolomics: snap-freeze in liquid N₂ within 30 s.
Microbial Culture	≥ 1 × 10⁸ cells (RNAprotect)	≥ 5 × 10⁷ cells (snap-frozen, quenched)	Quench metabolism rapidly (cold methanol or liquid N₂) for metabolomics. For transcriptomics: RNAprotect Bacteria Reagent immediately after harvest.

Critical Notes for Dual-Omics Sample Collection:

The paired-sample problem: RNA requires RNAlater or immediate lysis to stabilize transcripts. Metabolites require cold methanol quenching and organic solvent extraction to arrest metabolism. These conditions are incompatible — you cannot extract both from the same piece of tissue. Our workflow resolves this by splitting tissue at collection: one portion into RNAlater for transcriptomics, one portion snap-frozen for metabolomics. Both come from the same specimen, preserving the paired structure essential for correlation analysis.
Consistent collection timing is critical. Diurnal/circadian variation in both the transcriptome and metabolome can exceed treatment effects. Collect all samples within a narrow time window.
Minimum of 4–6 biological replicates per group for robust correlation statistics. Fewer than 4 replicates severely limits the power of Pearson/Spearman correlation and prevents WGCNA.

Deliverables — Correlation Matrices, Pathway Maps, and Analysis-Ready Figures

Single-Omics Results
Differential expression table (DEGs, .xlsx) with log2FC, FDR, and annotation. Differential metabolite table (DAMs, .xlsx) with fold-change, p-value, FDR, and KEGG/HMDB identifiers. PCA plots, volcano plots, and heatmaps for each omics layer.

Correlation Analysis
Ranked gene-metabolite correlation pairs (.xlsx) with r/rho values and FDR. Correlation heatmaps. WGCNA module eigengene profiles and module-metabolite association statistics.

KEGG Pathway Co-Mapping
Pathview diagrams with overlaid transcript and metabolite fold-changes. Pathway enrichment tables per omics layer and convergently enriched pathways with joint statistics.

Network and Concordance Figures
Annotated correlation networks (Cytoscape-compatible .cys and .png). Nine-quadrant concordance plot. O2PLS joint/orthogonal score plots.

Methods and Data Appendix
Complete analysis methods documentation. Raw data files (.fastq for RNA-seq, .mzML for metabolomics). All processed data tables in .xlsx and .csv formats.

KEGG pathway co-mapping diagram showing differentially expressed genes and metabolites mapped onto phenylpropanoid biosynthesis pathway

KEGG pathway co-mapping: differentially expressed genes and differential metabolites jointly mapped onto the phenylpropanoid biosynthesis pathway, with Pathview overlay showing fold-change direction for each molecular species.

Correlation network diagram with gene nodes squares and metabolite nodes circles connected by edges colored by correlation direction

Gene-metabolite correlation network: square nodes represent transcripts, circles represent metabolites. Node size reflects fold-change magnitude. Red edges indicate positive correlation, blue edges negative correlation (|r| > 0.6, FDR < 0.05).

Applications of Integrated Transcriptome-Metabolome Analysis

Our dual-omics integration service supports researchers across disciplines where connecting gene regulation to metabolic phenotype drives discovery:

Cancer Metabolism and Biomarker Discovery

Identify metabolic vulnerabilities driven by oncogenic transcriptional programs; discover gene-metabolite biomarker pairs with dual-omics validation for improved specificity

Plant Secondary Metabolism and Crop Improvement

Map transcriptional regulation of specialized metabolite pathways (flavonoids, alkaloids, terpenoids); identify transcription factor-metabolite regulatory pairs for metabolic engineering

Drug Mechanism of Action and Toxicology

Connect drug-induced transcriptional changes to metabolic consequences; distinguish on-target pharmacological effects from off-target toxicity using dual-omics concordance

Case Study: Transcriptome-Metabolome Integration Reveals Metabolic Quiescence in Oral Cancer Stem Cells

Metabolomics, Transcriptome and Single-Cell RNA Sequencing Analysis of the Metabolic Heterogeneity between Oral Cancer Stem Cells and Differentiated Cancer Cells

Miao, Y., Wang, P., Huang, J., Qi, X., Liang, Y., Zhao, W., Wang, H., Lyu, J., and Zhu, H. | Cancers (MDPI), 2024, 16(2), 237

DOI: 10.3390/cancers16020237 · Open Access (CC BY 4.0)

Background

Cancer stem cells (CSCs) drive tumor recurrence and therapy resistance, but their metabolic state — whether they are more or less metabolically active than differentiated cancer cells — has been debated. Bulk RNA-seq and metabolomics average out CSC-specific signals, while single-omics approaches miss the regulatory linkages between gene expression and metabolic phenotype. Integrating transcriptomics, metabolomics, and single-cell RNA-seq on the same tumor model resolves this question.

Challenge: Determine whether oral squamous cell carcinoma CSCs are metabolically more or less active than their differentiated counterparts, using multi-omics integration of RNA-seq, quasi-targeted metabolomics (2,200-compound panel), and single-cell RNA-seq from patient tumors.

Key Findings

Metric	Finding
Transcriptomic changes (HSC3 MCTS vs. control)	4,743 upregulated and 4,412 downregulated genes (\|log2FC\| > 1, FDR < 0.05)
Metabolomic changes	368 DAMs in CAL27, 314 DAMs in HSC3; 19 metabolites commonly upregulated, 109 commonly downregulated in both cell lines
CSC metabolic state	Metabolically less active than differentiated cells — TCA cycle metabolites (cis-aconitate, succinate) significantly decreased; glutathione metabolism downregulated; fatty acid β-oxidation reduced (CPT1A down)
Single-cell validation	25,531 cells from 6 patient tumors; CD44+/SOX2+ CSC cluster (cluster 7) confirmed reduced metabolic gene expression
Prognostic marker	ENO2 (enolase 2) overexpressed in CSCs; high ENO2 associated with significantly shorter overall survival (median 50.1 vs. 65.7 months)

What This Means for Your Dual-Omics Research

Multi-omics resolves functional states that single-omics cannot. Transcriptomics alone would have flagged thousands of DEGs without clarifying whether the CSC was metabolically active or quiescent. Only the metabolomics layer confirmed the suppressed metabolic state, and only the integration revealed that the transcriptional program was actively maintaining quiescence — not passively shutting down.
Single-cell data provides context that bulk omics cannot. The scRNA-seq data from 25,531 cells confirmed that the metabolic signature identified in bulk was genuinely attributable to the CSC subpopulation — not a contamination from stromal or immune cells. This multi-resolution approach (bulk + single-cell) is the direction our integration service supports.
Integrated analysis produces clinically actionable biomarkers. ENO2 was identified as both differentially expressed (transcriptomics) and a strong prognostic marker (survival analysis). Our pipeline is designed to identify exactly this type of dual-validated candidate — where omics-level significance is corroborated by clinical or phenotypic data.

Conclusion

This study demonstrates that integrated transcriptome-metabolome analysis — augmented with scRNA-seq — can resolve fundamental biological questions (CSC metabolic state) that neither omics layer answers alone. Our dual-omics integration service applies the same logic: multi-method correlation, KEGG pathway co-mapping, and network visualization — enabling you to move from two parallel data tables to one integrated biological narrative.

Read the full paper: Miao et al., Cancers, 2024 — Open Access (CC BY 4.0)

What is the difference between transcriptomics, metabolomics, and integrated dual-omics analysis?

Transcriptomics measures which genes are being expressed, capturing the cell's regulatory program. Metabolomics measures the biochemical end-products — the metabolites that actually mediate phenotype. Integrated dual-omics analysis connects these two layers, identifying which transcriptional changes propagate to the metabolic level and which do not. This filters transcriptional noise through metabolic evidence and reveals gene-metabolite regulatory relationships invisible to either layer alone.

Which correlation method is best for my dataset — Pearson, Spearman, or WGCNA?

Pearson correlation assumes linear relationships and normally distributed data — appropriate for log-transformed, well-normalized datasets. Spearman correlation is rank-based and robust to outliers and non-linearity — better for smaller datasets or when the relationship may be monotonic but not linear. WGCNA identifies co-varying modules of genes and metabolites rather than individual pairs — ideal for exploratory studies with complex traits. Our standard workflow applies multiple methods and prioritizes convergent findings; during study design, we recommend the strategy that matches your data structure.

How many biological replicates do I need for dual-omics integration analysis?

A minimum of 4–6 biological replicates per group is required for meaningful correlation statistics. Fewer than 4 replicates severely limits the power of Pearson/Spearman correlation (increasing false negatives) and prevents WGCNA (which requires sufficient sample size for module detection). For CCorA and O2PLS, 6–8 replicates per group is recommended for stable decomposition. Paired samples — transcriptomics and metabolomics from the same biological specimen — are essential for correlation analysis.

Can you integrate transcriptomics and metabolomics data that were generated separately?

Yes, we routinely integrate data from separate experiments — for example, RNA-seq data generated in your lab combined with metabolomics data acquired by us. However, the strongest correlation analysis requires paired samples (both omics from the same biological specimen) and consistent experimental conditions. If the two datasets come from different batches, animals, or time points, inter-sample variability can overwhelm the biological signal. We assess data compatibility during study design and advise on whether integration is statistically appropriate.

What is KEGG pathway co-mapping and how is it different from standard pathway enrichment?

Standard pathway enrichment tests whether a list of differentially expressed genes is statistically over-represented in a given KEGG pathway. Co-mapping goes further: it overlays both differentially expressed genes AND differentially abundant metabolites onto the same pathway diagram (using KEGG Pathview), showing which specific enzyme-gene-metabolite triads are jointly perturbed. This reveals whether transcriptional changes at a given enzymatic step are accompanied by corresponding metabolite changes — a level of mechanistic resolution that intersecting two separate enrichment lists cannot provide.

Can I submit samples for both transcriptomics and metabolomics, or do I need to provide one dataset?

Both options are available. The recommended approach is to submit samples for both omics layers — we split tissue at intake, process one portion for RNA-seq and the other for LC-MS/MS metabolomics. This ensures paired data from the same biological specimen. Alternatively, if you have already generated RNA-seq data, we can perform metabolomics on your samples and integrate the datasets. If both datasets already exist, we provide standalone integration analysis as a bioinformatics-only service.

How do you handle the different data scales between transcriptomics and metabolomics?

Transcriptomic data (counts or normalized expression) and metabolomic data (peak areas or concentrations) operate on fundamentally different scales. We apply layer-specific normalization — variance-stabilizing transformation or regularized log transformation for RNA-seq counts, and log-transformation with Pareto scaling for metabolomics. For methods that require a common scale (O2PLS, CCorA), data are mean-centered and unit-variance scaled per variable. The choice of transformation and scaling is documented in the methods appendix for full analytical transparency.

What file formats do you accept and deliver?

We accept RNA-seq data as .fastq (raw reads) or gene count matrices (.csv/.tsv). Metabolomics data can be submitted as raw vendor files (.wiff, .d, .raw) or processed feature tables (.csv). Deliverables include .xlsx tables (DEGs, DAMs, correlation pairs, module assignments), .csv feature matrices, KEGG Pathview diagrams (.png), correlation networks (.cys for Cytoscape, .png), and a comprehensive analysis report (.pdf). Raw sequencing data (.fastq) and metabolomics raw files are provided on request.

Can the analysis be customized for non-model organisms without KEGG pathway annotations?

Yes. For non-model organisms, we use homology-based annotation — BLAST against KEGG Orthology, UniProt, or EggNOG databases — to assign functional annotations to transcripts. For metabolites, we use accurate mass matching against HMDB, METLIN, and PubChem. Pathway mapping is performed against reference pathways from the closest annotated species or using KEGG Orthology-level mapping (KO numbers rather than species-specific gene IDs). Correlation and network analyses are species-independent and work for any organism.

What is the typical project timeline?

Project timelines depend on scope: RNA-seq library preparation and sequencing, metabolomics data acquisition, and integration analysis each scale with sample number and method complexity. A complete dual-omics project from sample submission to final report is project-dependent. Contact us during study design for scheduling and phased delivery options.

Metabolomics, Transcriptome and Single-Cell RNA Sequencing Analysis of the Metabolic Heterogeneity between Oral Cancer Stem Cells and Differentiated Cancer Cells

Miao, Y., Wang, P., Huang, J., Qi, X., Liang, Y., Zhao, W., Wang, H., Lyu, J., and Zhu, H.

Journal: Cancers (MDPI), 2024, 16(2), 237

Case study reference. Integrated RNA-seq, quasi-targeted metabolomics, and scRNA-seq in oral cancer stem cells. CSC-enriched MCTS model showed metabolic quiescence; ENO2 identified as prognostic marker.

MetaboAnalyst 6.0: Towards a Unified Platform for Metabolomics Data Processing, Analysis and Interpretation

Pang, Z., Lu, Y., Zhou, G., Hui, F., Xu, L., Viau, C., Spigelman, A.F., MacDonald, P.E., Wishart, D.S., Li, S., and Xia, J.

Journal: Nucleic Acids Research, 2024, 52(W1), W398–W406

Platform reference for MetaboAnalyst 6.0, supporting joint KEGG pathway enrichment, correlation network analysis, and multi-omics factor analysis across ~130 species.

MOFA+: A Statistical Framework for Comprehensive Integration of Multi-Modal Single-Cell Data

Argelaguet, R., Arnol, D., Bredikhin, D., Deloro, Y., Velten, B., Marioni, J.C., and Stegle, O.

Journal: Genome Biology, 2020, 21, 111

Method reference for MOFA+ used in our integration pipeline. Reconstructs a low-dimensional representation of multi-omics data using variational inference, identifying latent factors driving coordinated variation across transcriptome and metabolome layers.

Multi-Omics Approaches to Disease

Hasin, Y., Seldin, M., and Lusis, A.

Journal: Genome Biology, 2017, 18, 83

Foundational review of multi-omics integration strategies. Covers transcriptomics, proteomics, and metabolomics integration for understanding the flow of biological information underlying disease.

Web-Based Multi-Omics Integration Using the Analyst Software Suite

Ewald, J., Zhou, G., Lu, Y., Kolic, J., Ellis, C., Johnson, J.D., MacDonald, P.E., and Xia, J.

Journal: Nature Protocols, 2024, 19, 1467–1497

Protocol for web-based multi-omics integration covering transcriptomics, proteomics, and metabolomics data. Describes step-by-step workflows for correlation analysis, pathway enrichment, and network visualization.

xOmicsShiny: An R Shiny Application for Cross-Omics Data Analysis and Pathway Mapping

Journal: bioRxiv (preprint), 2025

Tool reference for the xOmicsShiny platform supporting cross-omics data merging, KEGG pathway co-mapping, and WGCNA module analysis. Illustrates the modular analysis framework applied in our multi-method integration pipeline.