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 |
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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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