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# scMerge <br /> <img src="" align="right" width="200" /> [![R build status](]( [![Codecov test coverage](]( [![](]( [![](]( [![](]( [![](]( [![](]( <br /> `scMerge` is a R package for merging and normalising single-cell RNA-Seq datasets. ## Installation `scMerge` is available on Bioconductor (<>). 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: <>. 2. scMerge2: <>. ## 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 ## Contact us If you have any enquiries, especially about performing `scMerge` integration on your own data, then please contact <>. 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](