Analysis of Single-Cell RNA-Sequencing Data: A Step-by-Step Guide

Single-cell RNA-sequencing (scRNA-seq) technology provides an excellent platform for measuring the expression profiles of genes in heterogeneous cell populations. Multiple tools analysis scRNA-seq data have been developed over years. The require complicated commands and steps to analyze underlying data, which are not easy follow by genome researchers experimental biologists. Therefore, we describe a step-by-step workflow processing analyzing unique molecular identifier (UMI) from Human Lung Adenocarcinoma lines. We demonstrate basic analyses including quality check, mapping quantification transcript abundance through suitable real example obtain UMI count data. Further, performed statistical analyses, such as zero-inflation, differential clustering on obtained studied effects excess zero-inflation present downstream analyses. Our findings indicate that associated with had no or minimal role clustering, while it significant effect identifying differentially expressed genes. also provide insight into comparative based zero-inflated negative binomial models sensitivity enhanced our model-based tool did accurate efficient way This study set guidelines users handle more easily.