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DCI: Learning Causal Differences between Gene Regulatory Networks

Anastasiya Belyaeva, Chandler Squires, Caroline Uhler
doi: https://doi.org/10.1101/2020.05.13.093765
Anastasiya Belyaeva
1Laboratory for Information & Decision Systems and Institute for Data, Systems and Society, Massachusetts Institute of Technology, Cambridge, 02139, USA
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Chandler Squires
1Laboratory for Information & Decision Systems and Institute for Data, Systems and Society, Massachusetts Institute of Technology, Cambridge, 02139, USA
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Caroline Uhler
1Laboratory for Information & Decision Systems and Institute for Data, Systems and Society, Massachusetts Institute of Technology, Cambridge, 02139, USA
2Department of Biosystems Science and Engineering, ETH Zurich, Basel, 4058, Switzerland
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  • For correspondence: cuhler@mit.edu
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Abstract

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/dci

Contact cuhler{at}mit.edu

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://uhlerlab.github.io/causaldag/dci

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted May 15, 2020.
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DCI: Learning Causal Differences between Gene Regulatory Networks
Anastasiya Belyaeva, Chandler Squires, Caroline Uhler
bioRxiv 2020.05.13.093765; doi: https://doi.org/10.1101/2020.05.13.093765
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DCI: Learning Causal Differences between Gene Regulatory Networks
Anastasiya Belyaeva, Chandler Squires, Caroline Uhler
bioRxiv 2020.05.13.093765; doi: https://doi.org/10.1101/2020.05.13.093765

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