Like all gene expression data, single-cell data suffers from batch effects and other unwanted variations that makes accurate biological interpretations difficult. The scMerge method leverages factor analysis, stably expressed genes (SEGs) and (pseudo-) replicates to remove unwanted variations and merge multiple single-cell data. This package contains all the necessary functions in the scMerge pipeline, including the identification of SEGs, replication-identification methods, and merging of single-cell data.
| Package details | |
|---|---|
| Bioconductor views | BatchEffect GeneExpression Normalization RNASeq Sequencing SingleCell Software Transcriptomics | 
| Maintainer | |
| License | GPL-3 | 
| Version | 1.19.0 | 
| URL | https://github.com/SydneyBioX/scMerge | 
| Package repository | View on GitHub | 
| Installation | Install the latest version of this package by entering the following in R:  | 
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