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

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

doi:10.1016/j.cels.2020.02.003

Xiaojie Qiu, Sreeram Kannan + Show 8 more

Open Access

https://doi.org/10.1016/j.cels.2020.02.003

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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.

Highlights

Most biological processes, either in development or disease progression (Faith et al, 2007a; Friedman et al, 2000a; Langfelder and Horvath, 2008a; Margolin et al, 2006; Meyer et al, 30 2008a), are governed by complex gene regulatory networks
Built upon 90 Restricted Directed Information (RDI), we developed a toolkit, Scribe, that is designed for the analysis of timeseries datasets, and is especially tailored for single cellRNAseq (Supplementary Figure 1, Fig 1A)
Scribe aims to be agnostic to the particular measurement technology used in an experiment, requiring as input timeordered gene expression profiles for each cell as they progress along a time axis

Summary

Introduction

Either in development or disease progression (Faith et al, 2007a; Friedman et al, 2000a; Langfelder and Horvath, 2008a; Margolin et al, 2006; Meyer et al, 30 2008a), are governed by complex gene regulatory networks. In the past few decades, numerous algorithms for inferring networks from observational gene expression data Convergent Cross Mapping (CCM) (Sugihara et al, 2012), a more recent technique based on statespace reconstruction 45 (Takens, 1981) can detect pairwise nonlinear interactions This method is limited to deterministic systems, and may be poorly suited for many cellular processes (e.g. cell differentiation), which are inherently stochastic

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