# censcyt [](http://bioconductor.org/checkResults/devel/bioc-LATEST/censcyt) [](http://bioconductor.org/checkResults/release/bioc-LATEST/censcyt) [](https://github.com/retogerber/censcyt/actions) [](https://codecov.io/gh/retogerber/censcyt) ## Summary `censcyt` is an R package extending the [diffcyt](https://github.com/lmweber/diffcyt) (differential discovery in high-dimensional cytometry via high-resolution clustering) pipeline. `censcyt` (**Cens**ored diff**cyt**) includes methods for differential abundance analysis in the presence of a covariate subject to right censoring. It uses the *reversed* association testing approach (like `diffcyt`) meaning the censored variable (e.g. survival time) is modeled as a predictor. Classical survival analysis methods on the other hand model the censored variable as the response. See also [here](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-021-04125-4). ## Vignettes The main workflow can be found in the Bioconductor [package vignette of diffcyt](http://bioconductor.org/packages/release/bioc/vignettes/diffcyt/inst/doc/diffcyt_workflow.html). An example use of the `censcyt` methods for differential abundance analysis with a covariate subject to right censoring is available in the [package vignette](http://bioconductor.org/packages/devel/bioc/vignettes/censcyt/inst/doc/censored_covariate.html) on bioconductor. ## Install To install from bioconductor: ```{r} if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("censcyt") ``` ### Development version To install directly from GitHub run the following code: ```{r} # First install 'devtools' package from CRAN install.packages("devtools") # Then install 'censcyt' package from GitHub devtools::install_github("retogerber/censcyt") ```