Pan-cancer deconvolution of omics data using Independent Component Analysis

The main idea - of the project is focused on extraction of hidden biological signals from multilevel data types (microarray / RNA-seq / ChIP-seq / scRNA-seq) obtained for tumor tissues of various types and different localizations for comprehensive analysis of tumor heterogeneity, as well as integration of biological processes ongoing in individual cancer cells and in tumor tissue.

Aim of the project:
Pan-cancer deconvolution of “multi-omics” data from high-throughput omics platforms using Independent Component Analysis for comprehensive meta-analysis of hidden signals and pathways.

Tasks of the project:

• Creation of database with cancer datasets from “multi-omics” platforms (microarrays, RNA-seq, Chip-Seq, scRNA-seq);
• Microarrays datasets processing and normalization as well as mapping and alignment of sequencing reads using bioinformatics methods;
• Independent Component Analysis application for each prepared cancer dataset and calculation of optimal number of independent components;
• Identification and visualization of reproducible gene interaction pathways and meta-genes specific or common for pan-cancer “multi-omics” datasets;
• Development of user-friendly interface and software with developed and implemented methodology of ICA application for cancer “multi-omics” datasets;
• Formation of atlas of reproducible meta-genes and interaction pathways based on the results of the project and analysis of cancer “multi-omics” datasets.