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README.md

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-# Scribe: Gene regulation visualization, causal network inference for single-cell RNA-Seq experiments
+# **Scribe**: Towards inferring causal regulations with single cell dynamics-coupled measurements
 
-Cellular fate commitment is governed by hierarchical gene regulatory networks. Previous network inference approaches are inherently incapable of resolving complex causal relationships since they are designed for small-scale and static bulk measurements. Here we propose **Scribe**, a toolkit that employs **Direct Information** to reconstruct causal regulatory networks using single-cell RNA-seq data. **Scribe** detects pairs of genes that interact and determines **causality** through strength of **information transfer** from one to the other. Our technique exploits the fact that an upstream regulatory gene's expression changes before its downstream targets undergo coherent changes. To calibrate the expected time lag between upstream and downstream genes, Scribe analyzes single-cell expression kinetics as the cells progress through pseudotime along a cellular trajectory.
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-**Scribe** outperforms alternative approaches, including **Granger causality** and **cross-convergence mapping** (CCM), across systematic synthetic simulation datasets. Applying **Scribe** to several single-cell RNA-seq datasets spanning diverse biological processes including dendritic cells' response to LPS stimulation and cellular reprogramming, we find that **Scribe** reconstructs networks which are supported by either literature or ChIP-Seq/ATAC-seq datasets. Finally, we use **Scribe** to yield novel insights into the gene regulatory hierarchy of hematopoiesis. We anticipate that **Scribe** will enable single-cell biologists to reconstruct regulatory networks governing cell lineage differentiation for each of the many cell types in the human body. 
+Single-cell transcriptome sequencing now routinely samples thousands of cells, potentially providing enough data to reconstruct *causal gene regulatory networks* from observational data. Here, we developed **Scribe**, a toolkit for detecting and visualizing causal regulations, 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 by taking advantage of time-delays. We apply **Scribe** and other leading approaches for 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 to live imaging 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 therefore highlight an important shortcoming in experimental and computational methods for analyzing gene regulation at single-cell resolution and point the way towards overcoming it.