Meta-Analysis of Esophageal Cancer Transcriptomes Using Independent Component Analysis.

ABSTRACT: Independent Component Analysis is a matrix factorization method for data dimension reduction. ICA has been widely applied for the analysis of transcriptomic data for blind separation of biological, environmental, and technical factors affecting gene expression. The study aimed to analyze the publicly available esophageal cancer data using the ICA for identification and comprehensive analysis of reproducible signaling pathways and molecular signatures involved in this cancer type. In this study, four independent esophageal cancer transcriptomic datasets from GEO databases were used. A bioinformatics tool « BiODICA-Independent Component Analysis of Big Omics Data» was applied to compute independent components (ICs). Gene Set Enrichment Analysis (GSEA) and ToppGene uncovered the most signific

SUBMITTER: Ashenova A 

PROVIDER: S-EPMC8594933 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature