recipe bioconductor-singlecelltk

Comprehensive and Interactive Analysis of Single Cell RNA-Seq Data






The Single Cell Toolkit (SCTK) in the singleCellTK package provides an interface to popular tools for importing, quality control, analysis, and visualization of single cell RNA-seq data. SCTK allows users to seamlessly integrate tools from various packages at different stages of the analysis workflow. A general "a la carte" workflow gives users the ability access to multiple methods for data importing, calculation of general QC metrics, doublet detection, ambient RNA estimation and removal, filtering, normalization, batch correction or integration, dimensionality reduction, 2-D embedding, clustering, marker detection, differential expression, cell type labeling, pathway analysis, and data exporting. Curated workflows can be used to run Seurat and Celda. Streamlined quality control can be performed on the command line using the SCTK-QC pipeline. Users can analyze their data using commands in the R console or by using an interactive Shiny Graphical User Interface (GUI). Specific analyses or entire workflows can be summarized and shared with comprehensive HTML reports generated by Rmarkdown. Additional documentation and vignettes can be found at

package bioconductor-singlecelltk

(downloads) docker_bioconductor-singlecelltk



depends bioconductor-annotationhub:


depends bioconductor-batchelor:


depends bioconductor-biobase:


depends bioconductor-biocparallel:


depends bioconductor-celda:


depends bioconductor-celldex:


depends bioconductor-complexheatmap:


depends bioconductor-delayedarray:


depends bioconductor-delayedmatrixstats:


depends bioconductor-deseq2:


depends bioconductor-dropletutils:


depends bioconductor-eds:


depends bioconductor-ensembldb:


depends bioconductor-experimenthub:


depends bioconductor-ggtree:


depends bioconductor-gseabase:


depends bioconductor-gsva:


depends bioconductor-gsvadata:


depends bioconductor-limma:


depends bioconductor-mast:


depends bioconductor-multtest:


depends bioconductor-s4vectors:


depends bioconductor-scater:


depends bioconductor-scdblfinder:


depends bioconductor-scds:


depends bioconductor-scmerge:


depends bioconductor-scran:


depends bioconductor-scrnaseq:


depends bioconductor-scuttle:


depends bioconductor-singlecellexperiment:


depends bioconductor-singler:


depends bioconductor-summarizedexperiment:


depends bioconductor-sva:


depends bioconductor-tenxpbmcdata:


depends bioconductor-trajectoryutils:


depends bioconductor-tscan:


depends bioconductor-tximport:


depends bioconductor-zellkonverter:


depends bioconductor-zinbwave:


depends r-anndata:

depends r-ape:

depends r-base:


depends r-circlize:

depends r-cluster:

depends r-colorspace:

depends r-colourpicker:

depends r-cowplot:

depends r-data.table:

depends r-dplyr:

depends r-dt:

depends r-enrichr:


depends r-fields:

depends r-ggplot2:

depends r-ggplotify:

depends r-ggrepel:

depends r-gridextra:

depends r-igraph:

depends r-kernsmooth:

depends r-magrittr:

depends r-matrix:


depends r-matrixstats:

depends r-metap:

depends r-msigdbr:

depends r-plotly:

depends r-plyr:

depends r-r.utils:

depends r-reshape2:

depends r-reticulate:


depends r-rlang:

depends r-rmarkdown:

depends r-rocr:

depends r-rtsne:

depends r-seurat:


depends r-shiny:

depends r-shinyalert:

depends r-shinycssloaders:

depends r-shinyjs:

depends r-soupx:

depends r-tibble:

depends r-vam:


depends r-withr:

depends r-yaml:



You need a conda-compatible package manager (currently either micromamba, mamba, or conda) and the Bioconda channel already activated (see set-up-channels).

While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.

Given that you already have a conda environment in which you want to have this package, install with:

   mamba install bioconductor-singlecelltk

and update with::

   mamba update bioconductor-singlecelltk

To create a new environment, run:

mamba create --name myenvname bioconductor-singlecelltk

with myenvname being a reasonable name for the environment (see e.g. the mamba docs for details and further options).

Alternatively, use the docker container:

   docker pull<tag>

(see `bioconductor-singlecelltk/tags`_ for valid values for ``<tag>``)

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