Details

Autor(en) / Beteiligte

• Qiu, Xiaojie
• Rahimzamani, Arman
• Wang, Li
• Ren, Bingcheng
• Mao, Qi
• Durham, Timothy
• McFaline-Figueroa, José L.
• Saunders, Lauren
• Trapnell, Cole
• Kannan, Sreeram

Titel

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

Ist Teil von

• Cell systems, 2020-03, Vol.10 (3), p.265-274.e11

Ort / Verlag

United States: Elsevier Inc

Erscheinungsjahr

2020

Quelle

MEDLINE

Beschreibungen/Notizen

• 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. [Display omitted] •Scribe detects causal regulatory networks between genes in diverse single-cell datasets•Scribe uses restricted directed information to identify regulators and their targets•Inferring causal regulatory networks requires temporal coupling between measurements•RNA velocity outperforms pseudotime, but neither perform as well as true time-series data Qiu et al. present Scribe (https://github.com/aristoteleo/Scribe-py), a toolkit for detecting and visualizing causal regulatory networks between genes in diverse single-cell datasets. They use Scribe to understand how causal network reconstruction depends on temporal coupling between measurements. They show that while pseudotime-ordered single-cell data fail to capture much of the information present in true temporal couplings, RNA velocity measurements restore much of this information.