DCI: learning causal differences between gene regulatory networks
Abstract
Designing interventions to control gene regulation necessitates modeling a gene regulatory network by a causal graph. Currently, large-scale gene 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 describe 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 single-cell RNA-seq data from different conditions and cell states, and we also validate our algorithm by predicting the effects of interventions.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Mar 11, 2021
- Source ID
- 10.1093/bioinformatics/btab167
Entities
People
- Anastasiya Belyaeva
- Caroline Uhler
- Chandler Squires
Organizations
- Massachusetts Institute of Technology
- National Institutes of Health
- National Science Foundation
- Office of Naval Research