NetMap is a BMBF-funded research project run by FAU (Biomedical Network Science Lab), UHH (Institute for Computational Systems Biology), and TUM (Chair of Animal Physiology and Immunology).

Motivation

Thanks to rapid advances in single-cell RNA sequencing (scRNA-seq) technologies, it is now possible to quantify genome-wide gene expression profiles at single-cell resolution at affordable costs. scRNA-seq data hold enormous potential to decipher molecular mechanisms that modulate progression and treatment response in complex diseases such as cancer and to inform personalized treatment regimens. Since scRNA-seq data are extremely sparse and high-dimensional, reducing their dimensionality is a necessary preprocessing step for all analyses. Yet, when applied to scRNA-seq data, standard dimensionality reduction techniques have been shown to yield highly non-canonical and unstable results with unforeseeable effects on downstream analyses.

Mission

In NetMap, we will therefore integrate dimensionality reduction for scRNA-seq data with a central task in the detection of molecular disease mechanisms: the identification of key gene regulatory networks and transcription factors that drive cell differentiation. The new computational methods will be applied on scRNA-seq data for exhausted and non-exhausted CD4 T cells, where we will aim at deriving hypotheses on gene regulatory programmes driving T cell exhaustion. The most promising hypotheses will then be tested in pre-clinical in vivo studies.