Package Recipe 'bioconductor-singlecelltk'

recipe bioconductor-singlecelltk

Comprehensive and Interactive Analysis of Single Cell RNA-Seq Data

Homepage:: https://bioconductor.org/packages/3.20/bioc/html/singleCellTK.html
License:: MIT + file LICENSE
Recipe:: /bioconductor-singlecelltk/meta.yaml

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 camplab.net/sctk.

package bioconductor-singlecelltk¶

Versions:

2.16.0-0, 2.12.0-0, 2.10.0-0, 2.8.0-0, 2.4.0-0, 2.2.0-0, 2.0.0-1, 2.0.0-0, 1.8.0-0,

2.16.0-0, 2.12.0-0, 2.10.0-0, 2.8.0-0, 2.4.0-0, 2.2.0-0, 2.0.0-1, 2.0.0-0, 1.8.0-0, 1.6.0-1, 1.4.0-1, 1.2.3-0

Depends:

on bioconductor-annotationhub >=3.14.0,<3.15.0
on bioconductor-batchelor >=1.22.0,<1.23.0
on bioconductor-biobase >=2.66.0,<2.67.0
on bioconductor-biocparallel >=1.40.0,<1.41.0
on bioconductor-celda >=1.22.0,<1.23.0
on bioconductor-celldex >=1.16.0,<1.17.0
on bioconductor-complexheatmap >=2.22.0,<2.23.0
on bioconductor-delayedarray >=0.32.0,<0.33.0
on bioconductor-delayedmatrixstats >=1.28.0,<1.29.0
on bioconductor-deseq2 >=1.46.0,<1.47.0
on bioconductor-dropletutils >=1.26.0,<1.27.0
on bioconductor-eds >=1.8.0,<1.9.0
on bioconductor-ensembldb >=2.30.0,<2.31.0
on bioconductor-experimenthub >=2.14.0,<2.15.0
on bioconductor-ggtree >=3.14.0,<3.15.0
on bioconductor-gseabase >=1.68.0,<1.69.0
on bioconductor-gsva >=2.0.0,<2.1.0
on bioconductor-gsvadata >=1.42.0,<1.43.0
on bioconductor-limma >=3.62.0,<3.63.0
on bioconductor-mast >=1.32.0,<1.33.0
on bioconductor-multtest >=2.62.0,<2.63.0
on bioconductor-s4vectors >=0.44.0,<0.45.0
on bioconductor-scater >=1.34.1,<1.35.0
on bioconductor-scdblfinder >=1.20.0,<1.21.0
on bioconductor-scds >=1.22.0,<1.23.0
on bioconductor-scmerge >=1.22.0,<1.23.0
on bioconductor-scran >=1.34.0,<1.35.0
on bioconductor-scrnaseq >=2.20.0,<2.21.0
on bioconductor-scuttle >=1.16.0,<1.17.0
on bioconductor-singlecellexperiment >=1.28.0,<1.29.0
on bioconductor-singler >=2.8.0,<2.9.0
on bioconductor-summarizedexperiment >=1.36.0,<1.37.0
on bioconductor-sva >=3.54.0,<3.55.0
on bioconductor-tenxpbmcdata >=1.24.0,<1.25.0
on bioconductor-trajectoryutils >=1.14.0,<1.15.0
on bioconductor-tscan >=1.44.0,<1.45.0
on bioconductor-tximport >=1.34.0,<1.35.0
on bioconductor-zellkonverter >=1.16.0,<1.17.0
on bioconductor-zinbwave >=1.28.0,<1.29.0
on r-anndata
on r-ape
on r-base >=4.4,<4.5.0a0
on r-circlize
on r-cluster
on r-colorspace
on r-colourpicker
on r-cowplot
on r-data.table
on r-dplyr
on r-dt
on r-enrichr >=3.2
on r-fields
on r-ggplot2
on r-ggplotify
on r-ggrepel
on r-gridextra
on r-igraph
on r-kernsmooth
on r-magrittr
on r-matrix >=1.6-1
on r-matrixstats
on r-metap
on r-msigdbr
on r-plotly
on r-plyr
on r-r.utils
on r-reshape2
on r-reticulate >=1.14
on r-rlang
on r-rmarkdown
on r-rocr
on r-rtsne
on r-seurat >=3.1.3
on r-shiny
on r-shinyalert
on r-shinycssloaders
on r-shinyjs
on r-soupx
on r-stringr
on r-tibble
on r-tidyr
on r-vam >=0.5.3
on r-withr
on r-yaml

Additional platforms:

Installation¶

You need a conda-compatible package manager (currently either pixi, conda, or micromamba) and the Bioconda channel already activated (see Usage). Below, we show how to install with either pixi or conda (for micromamba and mamba, commands are essentially the same as with conda).

Pixi¶

With pixi installed and the Bioconda channel set up (see Usage), to install globally, run:

pixi global install bioconductor-singlecelltk

to add into an existing workspace instead, run:

pixi add bioconductor-singlecelltk

In the latter case, make sure to first add bioconda and conda-forge to the channels considered by the workspace:

pixi workspace channel add conda-forge
pixi workspace channel add bioconda

Conda¶

With conda installed and the Bioconda channel set up (see Usage), to install into an existing and activated environment, run:

conda install bioconductor-singlecelltk

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-singlecelltk

with envname being the name of the desired environment.

Container¶

Alternatively, every Bioconda package is available as a container image for usage with your preferred container runtime. For e.g. docker, run:

docker pull quay.io/biocontainers/bioconductor-singlecelltk:<tag>

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

Integrated deployment¶

Finally, note that many scientific workflow management systems directly integrate both conda and container based software deployment. Thus, workflow steps can be often directly annotated to use the package, leading to automatic deployment by the respective workflow management system, thereby improving reproducibility and transparency. Check the documentation of your workflow management system to find out about the integration.

Download stats¶

Link to this page¶

Render an badge with the following MarkDown:

[![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/bioconductor-singlecelltk/README.html)