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Distance covariance entropy reveals primed states and bifurcation dynamics in single-cell RNA-Seq data

Distance covariance entropy reveals primed states and bifurcation dynamics in single-cell RNA-Seq data
Author Luo, Q; Maity, AK; Teschendorff, AE
Journal ISCIENCE
Pub Year 2022
Type Article
Abstract Cell-fate transitions are fundamental to development and differentiation. Studying them with single-cell omic data is important to advance our understanding of the cell-fate commitment process, yet this remains challenging. Here we present a computational method called DICE, which analyzes the entropy of expression covariation patterns and which is applicable to static and dynamically changing cell populations. Using only single-cell RNA-Seq data, DICE is able to predict multipotent primed states and their regulatory factors, which we subsequently validate with single-cell epigenomic data. DICE reveals that primed states are often defined by epigenetic regulators or pioneer factors alongside lineage-specific transcription factors. In developmental time course single-cell RNA-Seq datasets, DICE can pinpoint the timing of bifurcations more precisely than lineage-trajectory inference algorithms or competing variance-based methods. In summary, by studying the dynamic changes of expression covariation entropy, DICE can help elucidate primed states and bifurcation dynamics without the need for single-cell epigenomic data.
Issue 25
Volume 25