scMerge is a R package for merging and normalising single-cell RNA-Seq datasets.
scMerge is available on Bioconductor (https://bioconductor.org/packages/scMerge). You can install it using:
## Install scMerge from Bioconductor, requires R 3.6.0 or above
BiocManager::install("scMerge")
## You can also try to install the Bioconductor devel version of scMerge:
BiocManager::install("scMerge", version = "devel")
You can find the vignette at our website:
Stably expressed genes identified from this study can be extracted by
library(scMerge)
data(segList)
segList$human$human_scSEG # human SEG
segList$mouse$mouse_scSEG # mouse SEG
Or download csv files here (human SEG: link; mouse SEG: link)
For more detailed information and evaluation about SEG, please see our publication https://doi.org/10.1093/gigascience/giz106.
If you have any enquiries, especially about performing scMerge integration on your own data, then please contact yingxin.lin@sydney.edu.au. You can also open an issue on GitHub.
scMerge: scMerge leverages factor analysis, stable expression, and pseudoreplication to merge multiple single-cell RNA-seq datasets. Yingxin Lin, Shila Ghazanfar, Kevin Y.X. Wang, Johann A. Gagnon-Bartsch, Kitty K. Lo, Xianbin Su, Ze-Guang Han, John T. Ormerod, Terence P. Speed, Pengyi Yang, Jean Y. H. Yang. (2019). Our manuscript published at PNAS can be found here.
scMerge2: Atlas-scale single-cell multi-sample multi-condition data integration using scMerge2. Yingxin Lin, Yue Cao, Elijah Willie, Ellis Patrick, Jean Y.H. Yang. (2023). Our manuscript published in Nature Communications can be found here.
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