Multi-omics RNA-seq differential expression
Multi-omics RNA-seq differential expression analysis combines transcript-level count data from RNA sequencing with one or more additional omics layers — such as proteomics, metabolomics, epigenomics, or genomic variant data — to identify genes, proteins, or metabolites that differ systematically between biological conditions. By integrating multiple molecular levels, the pipeline captures regulatory mechanisms that transcriptomics alone cannot resolve, enabling a more complete picture of the biological processes driving observed phenotypes.
Source record
Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.
- Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15(12), 550. · DOI 10.1186/s13059-014-0550-8
- Argelaguet, R., Velten, B., Arnol, D., Dietrich, S., Zenz, T., Marioni, J. C., Buettner, F., Huber, W., & Stegle, O. (2018). Multi-Omics Factor Analysis — a framework for unsupervised integration of multi-omics data sets. Molecular Systems Biology, 14(6), e8124. · DOI 10.15252/msb.20178124
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