Large-scale causal discovery using interventional data sheds light on the regulatory network architecture of blood traits

Brown, B. C.; Morris, J. A.; Lappalainen, T.; Knowles, D. A.

doi:10.1101/2023.10.13.562293

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bioRxiv · 10.1101/2023.10.13.562293

Large-scale causal discovery using interventional data sheds light on the regulatory network architecture of blood traits

Brown, B. C.·Morris, J. A.·Lappalainen, T.·Knowles, D. A.

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Abstract

Inference of directed biological networks is an important but notoriously challenging problem. We introduce inverse sparse regression (inspre), an approach to learning causal networks that leverages large-scale intervention-response data. Applied to 788 genes from the genome-wide perturb-seq dataset, inspre helps elucidate the network architecture of blood traits.

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Brown, B. C., Morris, J. A., Lappalainen, T., Knowles, D. A.. 2023-10-17. Large-scale causal discovery using interventional data sheds light on the regulatory network architecture of blood traits. https://doi.org/10.1101/2023.10.13.562293

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