multi-omics-pipeline

Multi-omics integration is performed by running MOFA2 and mofapy2 for multi-omic factor analysis, enabling you to identify shared latent factors across transcriptomics, proteomics, and methylation datasets.

ID mapping converts between Ensembl, UniProt, and HGNC identifiers using the mygene and biomaRt packages, preventing incorrect gene symbol joins and ensuring accurate cross-omics data alignment.

Batch correction removes unwanted technical variation by executing ComBat or ComBat-seq from the sva package, which standardizes data normalization before applying MOFA2 multi-omic factor analysis.

Yes, you can perform GSEA enrichment and consensus clustering using gseapy, fgsea, msigdbr, and ConsensusClusterPlus to identify biological pathways and distinct sample subgroups from your integrated multi-omics data.

Yes, multi-omics factor analysis and ID mapping require standard bioinformatics packages including mygene, biomaRt, sva, MOFA2, mofapy2, gseapy, and scikit-learn to ensure scientific accuracy and reproducibility.

Multi-omics data integration requires standardized code to prevent common errors like incorrect gene symbol joins and improper normalization, ensuring reproducible factor analysis and batch correction across diverse omics workflows.