Simultaneous gene clustering and regulatory program reconstruction reveals insight into the phenotypic plasticity of neural cancers

Abstract: Nervous system cancers contain a large spectrum of transcriptional cell states, reflecting processes active during normal development, injury response and growth. However, we lack a good understanding of these states' regulation and pharmacological importance. Here, we describe the integrated reconstruction of such cellular regulatory programs and their therapeutic targets from extensive collections of single-cell RNA sequencing data (scRNA-seq). Our approach called single-cell Regulatory-driven Clustering (scRegClust) performs simultaneous gene clustering and regulatory program reconstruction tasks to predict essential kinases and transcription factors. We formulate an apriori intractable partitioning problem that connects gene modules with linear regulator models. A greedy two-step procedure is constructed to iteratively update gene modules and associated regulatory programs and find an approximate solution. Penalized regression was used to replace a combinatorial selection problem in the construction of regulatory programs and predictive modelling was used during gene cluster allocation. The method is used to identify regulatory programs in tumor cell states from both adult and childhood brain cancers. Further analysis corroborated by experimental results leads to hypothesis generation on an underlying biological mechanism for drug combination therapy in adult glioblastoma.

machine learningprobabilitystatistics theory

Audience: researchers in the discipline

( paper )

Gothenburg statistics seminar

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