Single cell classification via cell-type hierarchies based on ensemble learning and sample size estimation.
Install Bioconductor packages S4Vectors, hopach and limma packages using BiocManager:
# install.packages("BiocManager")
BiocManager::install(c("S4Vectors", "hopach", "limma"))Then install the latest scClassify using devtools (For R >= 4.0):
library(devtools)
devtools::install_github("SydneyBioX/scClassify")For R >= 3.6, install scClassify(v0.2.3) via
devtools::install_github("SydneyBioX/scClassify@085c72f")
You can find the vignette at this website (https://sydneybiox.github.io/scClassify/index.html):
- scClassify Model Building and Prediction: https://sydneybiox.github.io/scClassify/articles/scClassify.html
- Sample size calculation: https://sydneybiox.github.io/scClassify/articles/webOnly/sampleSizeCal.html
- Performing scClassify using pretrained models: https://sydneybiox.github.io/scClassify/articles/pretrainedModel.html
Also, you can find our interactive shiny application (beta) at this website: http://shiny.maths.usyd.edu.au/scClassify.
Currently available pre-trained scClassify models (in scClassifyTrainModel class)
If you have any enquiries, especially about performing scClassify to classify your cells or to build your own models, please contact yingxin.lin@sydney.edu.au or bioinformatics@maths.usyd.edu.au.
scClassify: sample size estimation and multiscale classification of cells using single and multiple reference
Yingxin Lin, Yue Cao, Hani Kim, Agus Salim, Terence Speed, David Lin, Pengyi Yang† & Jean Yang†. Molecular Systems Biology, 2020, 16, e9389. Full Text; BioC R package

