GO-Based Clustering for Learning Cluster-Level Causal Gene Regulatory Networks

arXiv (Cornell University)

Abstract

Discovery of causal relationships in high-dimensional Gene Regulatory Networks (GRN) is computationally challenging and often difficult to interpret due to dense connections. Therefore, grouping genes together into functional modules can improve tractability and biological interpretability. However, existing cluster level causal discovery methods assume access to a predefined admissible partitions, requiring the graph over clusters to be acyclic. Constructing such partitions is therefore challenging. In this work, we introduce GO-based Clustering for Causal Discovery (GO4CD), an algorithm that uses Gene Ontology (GO) to construct biologically meaningful gene partitions at multiple levels of granularity, while favoring those more likely to be admissible for causal discovery. GO4CD groups together genes participating in a shared biological process, and propagates gene annotations through the ontology hierarchy to achieve different granularity of partitions. Furthermore, we integrate GO4CD with Causal Learning over Clusters (CLOC) algorithm and evaluate recovery of true Markov equivalence class both with an oracle of conditional independencies and on simulated gene expression data using multivariate conditional independence tests. We evaluate GO4CD on multiple E.coli regulatory subnetworks and find that it is inadmissible in 18.1% of the cases, compared with 65.3-82.3% for the semantic-similarity baselines. Our results indicate that GO4CD is substantially better suited to learning causal GRNs defined over biologically meaningful gene clusters.

Authors 3

1. Azlaan Mustafa Samad
2. Wei Emma Zhang

   Leibniz University Hannover · L3S Research Center

   Affiliation as printed

   L3S Research Center , Leibniz University Hannover , Germany

3. Adèle H. Ribeiro Aachen Center for Computational Life Sciences

   RWTH Aachen University · Artificial Intelligence in Medicine (Canada)

   Affiliation as printed

   Center for Computational Life Sciences , RWTH Aachen University , Germany

   Institute for Causal and Explainable Artificial Intelligence in Life Sciences ,