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Iterative random forests to discover predictive and stable high-order interactions.

By Sumanta Basu, Karl Kumbier, James B Brown and Bin Yu

Abstract

Genomics has revolutionized biology, enabling the interrogation of whole transcriptomes, genome-wide binding sites for proteins, and many other molecular processes. However, individual genomic assays measure elements that interact in vivo as components of larger molecular machines. Understanding how these high-order interactions drive gene expression presents a substantial statistical challenge. Building on random forests (RFs) and random intersection trees (RITs) and through extensive, biologically inspired simulations, we developed the iterative random forest algorithm (iRF). iRF trains a feature-weighted ensemble of decision trees to detect stable, high-order interactions with the same order of computational cost as the RF. We demonstrate the utility of iRF for high-order interaction discovery in two prediction problems: enhancer activity in the early Drosophila embryo and alternative splicing of primary transcripts in human-derived cell lines. In Drosophila, among the 20 pairwise transcription factor interactions iRF identifies as stable (returned in more than half of bootstrap replicates), 80% have been previously reported as physical interactions. Moreover, third-order interactions, e.g., between Zelda (Zld), Giant (Gt), and Twist (Twi), suggest high-order relationships that are candidates for follow-up experiments. In human-derived cells, iRF rediscovered a central role of H3K36me3 in chromatin-mediated splicing regulation and identified interesting fifth- and sixth-order interactions, indicative of multivalent nucleosomes with specific roles in splicing regulation. By decoupling the order of interactions from the computational cost of identification, iRF opens additional avenues of inquiry into the molecular mechanisms underlying genome biology

Topics: Animals, Drosophila, Computational Biology, Gene Expression Regulation, Developmental, Alternative Splicing, Algorithms, Models, Genetic, Gene Regulatory Networks, Genome-Wide Association Study, genomics, high-order interaction, interpretable machine learning, random forests, stability, Gene Expression Regulation, Developmental, Models, Genetic, Human Genome, Genetics, Biotechnology, 1.1 Normal biological development and functioning, Generic Health Relevance
Publisher: eScholarship, University of California
Year: 2018
OAI identifier: oai:escholarship.org/ark:/13030/qt4wf5x25z
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