/r_docker

Docker container for genomics analyses in R.

Primary LanguageRMIT LicenseMIT

R studio docker container for scRNA-seq analyses build on bioconductor_docker:RELEASE_3_18 and R v4.3.2.

The Docker image is build in three layers:

  1. Installation of R packages on top of the bioconductor image: docker build -f Dockerfile_R . -t jsschrepping/r_docker:jss_R432_bioc318
  2. Installation of pip packages on top: docker build -f Dockerfile_pip . -t jsschrepping/r_docker:jss_R432_bioc318_pip
  3. Installation of conda packages on top: docker build -f Dockerfile_conda . -t jsschrepping/r_docker:jss_R432_bioc318_pip_conda

Installed R packages include:

  • DESeq2

  • tximport

  • limma

  • edgeR

  • complexheatmap

  • EnhancedVolcano

  • clusterProfiler

  • gage

  • monocle & monocle3

  • slingshot

  • SingleCellExperiment

  • Seurat v5.0.3

  • SeuratDisc

  • SingleR

  • Rcistarget

  • harmony

  • symphony

  • SoupX

  • AUcell

  • Ucell

  • DittoSeq

  • DiffBind

  • ChipSeeker

  • Signac

  • ArchR

  • Gviz

  • ChromVar

  • CytoExploreR

  • CytoML

  • FlowWorkspace

  • ggcyto

  • openCyto

  • cydar

  • Human Annotation databases (org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg38.knownGene, EnsDb.Hsapiens.v86)

and many more...

In addition, following python packages are installed to be used via reticulate:

  • MACS3
  • Cytosig
  • scanpy
  • scvelo
  • cellrank
  • scrublet
  • CellphoneDB
  • rapids packages for gpu usage

Installed versions of packages are documented in /logs/log_install_R.txt, /logs/log_install_pip.txt and /logs/log_install_conda.txt.

For instructions on how to launch RStudio in docker please read: https://ropenscilabs.github.io/r-docker-tutorial/02-Launching-Docker.html.