Dimensionality reduction for molecular data based on explanatory power of differential regulatory networks (NetMap)
Third Party Funds Group - Overall project
Acronym: NetMap
Start date : 01.03.2023
End date : 28.02.2026
Website: https://www.netmap.ai/
Project details
Scientific Abstract
Rapid advances in single-cell RNA sequencing (scRNA-seq) technology are leading to ever-increasing dimensions of the generated molecular data, which complicates data analyses. In NetMap, new scalable and robust dimensionality reduction approaches for scRNA-seq data will be developed. To this end, dimensionality reduction will be integrated into a central task of the systems medicine analysis of scRNA-seq data: inference of gene regulatory networks (GRNs) and driver transcription factors based on cell expression profiles. Each resulting dimension will correspond to a driver GRN, and the coordinate of a cell in this low-dimensional representation will quantify the extent to which the particular driver GRN explains the cell's gene expression profile. These new methods will be implemented as a user-friendly software platform for exploratory expert-in-the-loop analysis and in silico prediction of drug repurposing candidates.
As a case study, we will investigate CD4 helper T cell exhaustion, a potential limiting factor in immunotherapy. NetMap's strategy consists of (1) analyzing phenotypic heterogeneity of depleted CD4 T cells, (2) identifying transcriptional mechanisms that control this heterogeneity, (3) amplifying/eliminating specific subsets and testing their functional impact. This will allow the development of an atlas of the gene regulatory landscape of depleted CD4 T cells, while the in vivo testing of key regulatory transcription factors will help demonstrate the power of the developed methods and allow evaluation and improvement of predictions.
