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

Kun Qian, Shiwei Fu, Hongwei Li, Wei Vivian Li

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1233-1236
Number of pages4
JournalJournal of Computational Biology
Volume29
Issue number11
DOIs
StatePublished - Nov 2022

All Science Journal Classification (ASJC) codes

  • Modeling and Simulation
  • Molecular Biology
  • Genetics
  • Computational Mathematics
  • Computational Theory and Mathematics

Keywords

  • clustering
  • data integration
  • non-negative matrix factorization
  • scRNA-seq

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