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README.md
# scMerge <br /> <img src="https://github.com/SydneyBioX/scMerge/raw/master/inst/logo.png" align="right" width="200" /> [![R build status](https://github.com/SydneyBioX/scMerge/workflows/R-CMD-check/badge.svg)](https://github.com/SydneyBioX/scMerge/actions) [![Codecov test coverage](https://codecov.io/gh/SydneyBioX/scMerge/branch/master/graph/badge.svg)](https://codecov.io/gh/SydneyBioX/scMerge?branch=master) [![](https://img.shields.io/badge/doi-10.1073/pnas.1820006116-blue.svg)](https://doi.org/10.1073/pnas.1820006116) [![](https://img.shields.io/badge/devel%20version-1.5.0-blue.svg)](https://github.com/SydneyBioX/scMerge) [![](https://img.shields.io/badge/download-1155/total-green.svg)](https://bioconductor.org/packages/stats/bioc/scMerge) [![](https://img.shields.io/github/last-commit/SydneyBioX/scMerge.svg)](https://github.com/SydneyBioX/scMerge/commits/master) [![](https://img.shields.io/badge/Docker%20image-available-blue.svg)](https://hub.docker.com/repository/docker/kevinwang09/scmerge) <br /> `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: ``` r ## 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](https://www.maths.usyd.edu.au/u/yingxinl/wwwnb/SEG/human_scSEG.csv); mouse SEG: [link](https://www.maths.usyd.edu.au/u/yingxinl/wwwnb/SEG/mouse_scSEG.csv)) 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 <yingxin.lin@sydney.edu.au>. You can also [open an issue](https://github.com/SydneyBioX/scMerge/issues) 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](http://www.pnas.org/lookup/doi/10.1073/pnas.1820006116). 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. (2022). Our manuscript in biorxiv can be found [here](https://www.biorxiv.org/content/10.1101/2022.12.08.519588v1).