Identifying cancer pathway dysregulations using differential causal effects.

ABSTRACT:

Motivation

Signaling pathways control cellular behavior. Dysregulated pathways, for example, due to mutations that cause genes and proteins to be expressed abnormally, can lead to diseases, such as cancer.

Results

We introduce a novel computational approach, called Differential Causal Effects (dce), which compares normal to cancerous cells using the statistical framework of causality. The method allows to detect individual edges in a signaling pathway that are dysregulated in cancer cells, while accounting for confounding. Hence, technical artifacts have less influence on the results and dce is more likely to detect the true biological signals. We extend the approach to handle unobserved dense confounding, where each latent variable, such as, for example, batch effects or ce

SUBMITTER: Jablonski KP 

PROVIDER: S-EPMC8896597 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature