<!-- README.md is generated from README.Rmd. Please edit that file -->
# scMerge
<br />
<img src="https://github.com/SydneyBioX/scMerge/raw/master/inst/logo.png" align="right" width="200" />
[](https://github.com/SydneyBioX/scMerge/actions)
[](https://codecov.io/gh/SydneyBioX/scMerge?branch=master)
[](https://doi.org/10.1073/pnas.1820006116)
[](https://github.com/SydneyBioX/scMerge)
[](https://bioconductor.org/packages/stats/bioc/scMerge)
[](https://github.com/SydneyBioX/scMerge/commits/master)
[](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:
<https://sydneybiox.github.io/scMerge/index.html>.
## Case studies
You can find a list of case studies here:
<https://sydneybiox.github.io/scMerge/articles/>.
## 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
<bioinformatics@maths.usyd.edu.au>. You can also [open an
issue](https://github.com/SydneyBioX/scMerge/issues) on GitHub.
## Reference
**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
Our manuscript published at PNAS can be found
[here](http://www.pnas.org/lookup/doi/10.1073/pnas.1820006116).
