scMerge.data
contains an illustrative data to demonstrate the utility
of the scMerge
package. For more details, please see here:
https://github.com/SydneyBioX/scMerge.
This package is a convenient way to load the data stored here.
scMerge
is available on Bioconductor
(https://bioconductor.org/packages/scMerge). You can install it using:
# install.packages(c("BiocManager", "devtools"))
## Install scMerge from Bioconductor, requires R 3.6.0 or above
BiocManager::install("scMerge")
devtools::install_github("SydneyBioX/scMerge.data")
You can find the vignette at our website: https://sydneybiox.github.io/scMerge/index.html.
You can find a list of case studies here: https://sydneybiox.github.io/scMerge/articles/.
If you have any enquries, especially about performing scMerge
integration on your own data, then please contact
[email protected]. You can also open an
issue on GitHub.
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.