all_times <- list() # store the time for each chunk knitr::knit_hooks$set(time_it = local({ now <- NULL function(before, options) { if (before) { now <<- Sys.time() } else { res <- difftime(Sys.time(), now, units = "secs") all_times[[options$label]] <<- res } } })) knitr::opts_chunk$set( tidy = TRUE, tidy.opts = list(width.cutoff = 95), message = FALSE, warning = FALSE, time_it = TRUE )
scCustomize requires R version 4.0 or greater. Highly recommend installing R Studio.
scCustomize can be installed from CRAN for all platforms using standard installation methods:
# Base R install.packages("scCustomize") # Using pak pak::pkg_install("scCustomize")
A small number of functions in scCustomize rely on packages from Bioconductor and are not installed by default. To enable these functions make sure to install the following packages:
install.packages("BiocManager") BiocManager::install(c("ComplexHeatmap", "dittoSeq", "DropletUtils", "Nebulosa"))
There are a few other CRAN packages that are needed to enable some optional functionality of scCustomize. To enable these functions make sure to install the following packages:
install.packages(c("ggpubr", "hdf5r", "rliger"))
A full copy of the changes in each version can be found in the NEWS/ChangeLog.
I also maintain a separate development branch* that can be installed by supplying ref = "develop"
in the devtools or remotes installation command. Version scheme vX.X.X.9yyy.
devtools::install_github(repo = "samuel-marsh/scCustomize", ref = "develop") remotes::install_github(repo = "samuel-marsh/scCustomize", ref = "develop")
*Note: While this branch is typically mostly stable it may contain breaking issues/bugs.
I do try and keep development ChangeLog up to date so it's easier to follow changes than reading commit history.
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