RT Journal Article
SR Electronic
T1 DCI: Learning Causal Differences between Gene Regulatory Networks
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.05.13.093765
DO 10.1101/2020.05.13.093765
A1 Anastasiya Belyaeva
A1 Chandler Squires
A1 Caroline Uhler
YR 2020
UL http://biorxiv.org/content/early/2020/05/15/2020.05.13.093765.abstract
AB Summary Designing interventions to control gene regulation necessitates modeling a gene regulatory network by a causal graph. Currently, large-scale expression datasets from different conditions, cell types, disease states and developmental time points are being collected. However, application of classical causal inference algorithms to infer gene regulatory networks based on such data is still challenging, requiring high sample sizes and computational resources. Here, we propose an algorithm that efficiently learns the differences in gene regulatory mechanisms between different conditions. Our difference causal inference (DCI) algorithm infers changes (i.e., edges that appeared, disappeared or changed weight) between two causal graphs given gene expression data from the two conditions. This algorithm is efficient in its use of samples and computation since it infers the differences between causal graphs directly without estimating each possibly large causal graph separately. We provide a user-friendly Python implementation of DCI and also enable the user to learn the most robust difference causal graph across different tuning parameters via stability selection. Finally, we show how to apply DCI to bulk and single-cell RNA-seq data from different conditions and cell states, and we also validate our algorithm by predicting the effects of interventions.Availability and implementation All algorithms are freely available as a Python package at http://uhlerlab.github.io/causaldag/dciContact cuhler{at}mit.eduCompeting Interest StatementThe authors have declared no competing interest.