The scINSIGHT Package for Integrating Single-Cell RNA-Seq Data from Different Biological Conditions

Journal of computational biology: A journal of computational molecular cell biology

article

Kun Qian¹  Shiwei Fu²  Hongwei Li¹  Wei Vivian Li² 

[1] School of Mathematics and Physics, China University of Geosciences;Department of Biostatistics and Epidemiology, Rutgers School of Public Health, Rutgers, The State University of New Jersey;Department of Statistics, University of California

关键词: clustering;    data integration;    non-negative matrix factorization;    scRNA-seq;   

DOI  :  10.1089/cmb.2022.0244

学科分类：生物科学（综合）

来源: Mary Ann Liebert, Inc. Publishers

【 摘 要 】

Data integration is a critical step in the analysis of multiple single-cell RNA sequencing samples to account for heterogeneity due to both biological and technical variability. scINSIGHT is a new integration method for single-cell gene expression data, and can effectively use the information of biological condition to improve the integration of multiple single-cell samples. scINSIGHT is based on a novel non-negative matrix factorization model that learns common and condition-specific gene modules in samples from different biological or experimental conditions. Using these gene modules, scINSIGHT can further identify cellular identities and active biological processes in different cell types or conditions. Here we introduce the installation and main functionality of the scINSIGHT R package, including how to preprocess the data, apply the scINSIGHT algorithm, and analyze the output.

【 授权许可】

Unknown