Single-cell gene regulatory network prediction by explainable AI

  • Philipp Keyl
  • Philip Bischoff
  • Gabriel Dernbach
  • Michael Bockmayr
  • Rebecca Fritz
  • David Horst
  • Nils Blüthgen
  • Grégoire Montavon
  • Klaus-Robert Müller
  • Frederick Klauschen

Abstract

The molecular heterogeneity of cancer cells contributes to the often partial response to targeted therapies and relapse of disease due to the escape of resistant cell populations. While single-cell sequencing has started to improve our understanding of this heterogeneity, it offers a mostly descriptive view on cellular types and states. To obtain more functional insights, we propose scGeneRAI, an explainable deep learning approach that uses layer-wise relevance propagation (LRP) to infer gene regulatory networks from static single-cell RNA sequencing data for individual cells. We benchmark our method with synthetic data and apply it to single-cell RNA sequencing data of a cohort of human lung cancers. From the predicted single-cell networks our approach reveals characteristic network patterns for tumor cells and normal epithelial cells and identifies subnetworks that are observed only in (subgroups of) tumor cells of certain patients. While current state-of-the-art methods are limited by their ability to only predict average networks for cell populations, our approach facilitates the reconstruction of networks down to the level of single cells which can be utilized to characterize the heterogeneity of gene regulation within and across tumors.

Bibliografische Daten

OriginalspracheEnglisch
ISSN0305-1048
DOIs
StatusVeröffentlicht - 28.02.2023

Anmerkungen des Dekanats

© The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.

PubMed 36629274