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README.md
In scmap: A tool for unsupervised projection of single cell RNA-seq data
scmap - A tool for unsupervised projection of single cell RNA-seq data
Single-cell RNA-seq (scRNA-seq) is widely used to investigate the composition of complex tissues since the technology allows researchers to define cell-types using unsupervised clustering of the transcriptome. However, due to differences in experimental methods and computational analyses, it is often challenging to directly compare the cells identified in two different experiments. Here, we present scmap, a method (source available at https://github.com/hemberg-lab/scmap and the application can be run from http://www.hemberg-lab.cloud/scmap) for projecting cells from a scRNA-seq experiment on to the cell-types identified in a different experiment.
Cloud-based scmap
A Cloud implementation of scmap can be used for free without any restriction here. Instructions on how to set it up on your own Cloud are available here.
Questions
Q: How to install/run scmap?
A: Please follow instruction on Bioconductor page. If there are any problems you can install scmap from GitHub:
# run this in your R session
install.packages("devtools")
devtools::install_github("hemberg-lab/scmap")
Q: Where can I report bugs, comments, issues or suggestions?
A: Please use this page.
Q: Where can I ask questions about scmap?
A: Please use this page.
Q: Is scmap published?
A: Yes, it is published in Nature Methods.
Q: What is scmap licence?
A: GPL-3
Try the scmap package in your browser
Any scripts or data that you put into this service are public.
scmap documentation built on Nov. 8, 2020, 8:07 p.m.
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
scmap - A tool for unsupervised projection of single cell RNA-seq data
Single-cell RNA-seq (scRNA-seq) is widely used to investigate the composition of complex tissues since the technology allows researchers to define cell-types using unsupervised clustering of the transcriptome. However, due to differences in experimental methods and computational analyses, it is often challenging to directly compare the cells identified in two different experiments. Here, we present scmap, a method (source available at https://github.com/hemberg-lab/scmap and the application can be run from http://www.hemberg-lab.cloud/scmap) for projecting cells from a scRNA-seq experiment on to the cell-types identified in a different experiment.
Cloud-based scmap
A Cloud implementation of scmap can be used for free without any restriction here. Instructions on how to set it up on your own Cloud are available here.
Questions
Q: How to install/run scmap? A: Please follow instruction on Bioconductor page. If there are any problems you can install scmap from GitHub:
# run this in your R session
install.packages("devtools")
devtools::install_github("hemberg-lab/scmap")
Q: Where can I report bugs, comments, issues or suggestions? A: Please use this page.
Q: Where can I ask questions about scmap? A: Please use this page.
Q: Is scmap published? A: Yes, it is published in Nature Methods.
Q: What is scmap licence? A: GPL-3
Try the scmap package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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