Multi-Omics

Methods on multi-omics data

1 Background of Multi-Omics Data Integration

1.1 What is Multi-Omics?

Multi-omics involves studying multiple omics data from same samples at once (eg. epigenomics, proteomics, transcriptomics, genomics). More general term for this type of data analysis are:

  • multi-modal
  • multi-table
  • multi-way

The data can be described as multiple matrices/tables with same number of samples and varying number of features 1.

1.2 Public Data Sources

Abbreaviations: CNV, copy number variation; miRNA, microRNA; RPPA, reverse phase protein array; SNP, single-nucleotide polymorphism; SNV, single-nucleotide variant
Data Repository Link Disease Types of Multi-Omics data available
The Cancer Genome Atlas (TCGA) link Cancer RNA-seq, DNA-seq, miRNA-Seq, SNV, CNV, DNA methylation, RPPA
Clinical Proteomic Tumor Analysis Consortium(CPTAC) link Cancer Proteomics data corresponding to TCGA cohorts
International Cancer Genomics Consortium (ICGC) link Cancer Whole genome sequencing, genomic variations data (somatic and germline mutation)
Cancer Cell Line Encyclopedia (CCLE) link Cancer cell line Gene expression, copy number, and sequencing data; pharmalogical profiles of 24 anticancer drugs
TARGET link Pediatric cancers Gene expression, miRNA expression, copy number, and sequenciing data

1.3 Common analysis themes and examples

Placeholder

2 Methods for Vertical Multi-Omics Data Integration

Placeholder

References

“GitHub - Mikelove/Awesome-Multi-Omics: List of Software Packages for Multi-Omics Analysis — Github.com.” https://github.com/mikelove/awesome-multi-omics.

Footnotes

  1. “GitHub - Mikelove/Awesome-Multi-Omics: List of Software Packages for Multi-Omics Analysis — Github.com”↩︎