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 language | English (US) |
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Pages (from-to) | 1233-1236 |
Number of pages | 4 |
Journal | Journal of Computational Biology |
Volume | 29 |
Issue number | 11 |
DOIs | |
State | Published - 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