DOI: 10.5281/zenodo.5044473
title: Capturing hidden regulation based on noise change of gene expression level from single cell RNA-seq in yeast

description: <strong>Abstract</strong> <strong>Recent progress in high throughput single cell RNA-seq (scRNA-seq) has activated the development of data-driven inferring methods of gene regulatory networks. Most network estimations assume that perturbations produce downstream effects. However, the effects of gene perturbations are sometimes compensated by a gene with redundant functionality (functional compensation). In order to avoid functional compensation, previous studies constructed double gene deletions, but its vast nature of gene combinations was not suitable for comprehensive network estimation. We hypothesized that functional compensation may emerge as a noise change without mean change (noise-only change) due to varying physical properties and strong compensation effects. Here, we show compensated interactions, which are not detected by mean change, are captured by noise-only change quantified from scRNA-seq. We investigated whether noise-only change genes caused by a single deletion of STP1 and STP2, which have strong functional compensation, are enriched in redundantly regulated genes. As a result, noise-only change genes are enriched in their redundantly regulated genes. Furthermore, novel downstream genes detected from noise change are enriched in “transport”, which is related to known downstream genes. Herein, we suggest the noise difference comparison has the potential to be applied as a new strategy for network estimation that capture even compensated interaction.</strong> <em><strong>SourceCode</strong></em> <strong><em>Main.R</em></strong><br> Please execute code along with the guide in this script <em><strong>CreateBASiCS.R</strong></em><br> Create BASiCS chain objects, which include mean and noise data, from the raw count matrix. This step takes ~48 hours. Use of HPC is recommended <em><strong>DifferenceTestAll.R</strong></em><br> Execute comparison with regard to the mean and noise of expression level quantified from all cells. Test enrichment to the redundantly regulated genes. <em><strong>DifferenceTestCluster.R</strong></em><br> Execute comparison with regard to the mean and noise of expression level quantified from cells belonging to the same cluster. Test enrichment to the redundantly regulated genes. <em><strong>STP12_detail_analysis.Rmd</strong></em><br> Output results mentioned in the discussion. Code is placed within the chunk. <em><strong>CreateSupFig2.R</strong></em><br> Generate supplement figure 2 <em><strong>CreateFigure4AndS6.R</strong></em><br> Generate figure 4 and supplement figure 6 <strong>YeastGeneNameList.csv</strong><br> Data that convert ORF id to the common name<br> This data includes 3 columns, Name, ORF, and SGD.<br> - Name: Common name (e.g., TSC3)<br> - ORF: ORF id with Yeastract format (e.g., YBR058C.A)<br> - SGD: ORF id with SGD format (e.g., YBR058C-A) <br> <strong>GeneticInteraction(ORF).csv</strong><br> Genetic interaction data downloaded from Yeastract. This data includes two-column, V1 and V2.<br> The interaction is described as two matrices such as V1 output to V2. <strong>NoiseChangeGeneList</strong><br> This folder contains data required for generating tables 1 and S2.