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.

在这里，我们推出了 Scribe (https://github.com/aristoteleo/Scribe-py)，这是一个工具包，用于检测和可视化基因之间的因果调控相互作用，并探索单细胞实验推动网络重建的潜力。 Scribe 采用受限定向信息，通过估计从潜在监管者传输到其下游目标的信息强度来确定因果关系。我们将 Scribe 和其他领先的因果网络重建方法应用于几种类型的单细胞测量，结果表明，与真实时间序列数据相比，“伪时间”有序单细胞数据的性能急剧下降。我们证明执行因果推理需要测量之间的时间耦合。我们通过对嗜铬细胞命运承诺的分析表明，“RNA速度”等方法可以恢复一定程度的耦合。这些分析强调了在单细胞分辨率下分析基因调控的实验和计算方法的缺陷，并提出了克服它的方法。