Inferring Causal Gene Regulatory Networks from Coupled Single-Cell Expression Dynamics Using Scribe.

Cell Systems
Xiaojie QiuSreeram Kannan

Abstract

Here, we present Scribe (https://github.com/aristoteleo/Scribe-py), a toolkit for detecting and visualizing causal regulatory interactions between genes and explore the potential for single-cell experiments to power network reconstruction. Scribe employs restricted directed information to determine causality by estimating the strength of information transferred from a potential regulator to its downstream target. We apply Scribe and other leading approaches for causal network reconstruction to several types of single-cell measurements and show that there is a dramatic drop in performance for "pseudotime"-ordered single-cell data compared with true time-series data. We demonstrate that performing causal inference requires temporal coupling between measurements. We show that methods such as "RNA velocity" restore some degree of coupling through an analysis of chromaffin cell fate commitment. These analyses highlight a shortcoming in experimental and computational methods for analyzing gene regulation at single-cell resolution and suggest ways of overcoming it.

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Citations

Jan 9, 2021·Cell Stem Cell·Patrick CahanChristine A Wells
Aug 18, 2020·Trends in Biotechnology·Antonio Del Sol, Sascha Jung
Jan 30, 2021·PLoS Computational Biology·Jonathan LuBarbara E Engelhardt
May 27, 2021·Communications Biology·Johan ReimegårdCaroline J Gallant
May 12, 2021·Bioinformatics·Guangzheng WengKyoung Jae Won
Aug 27, 2021·Molecular Systems Biology·Volker BergenFabian J Theis
Sep 28, 2021·Inflammation and Regeneration·Shunsuke NojiriShuji Terai

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