scMerge

scMerge is a R package for merging and normalising single-cell RNA-Seq datasets.

Installation

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")

Vignette

You can find the vignette at our website:

1. scMerge: https://sydneybiox.github.io/scMerge/articles/scMerge.html.
2. scMerge2: https://sydneybiox.github.io/scMerge/articles/scMerge2.html.

Stably Expressed Genes

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.

Contact us

If you have any enquiries, especially about performing scMerge integration on your own data, then please contact [email protected]. You can also open an issue on GitHub.

Reference

1. 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.

2. 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.