recipe bioconductor-sincell

R package for the statistical assessment of cell state hierarchies from single-cell RNA-seq data

Homepage:

https://bioconductor.org/packages/3.18/bioc/html/sincell.html

License:

GPL (>= 2)

Recipe:

/bioconductor-sincell/meta.yaml

Links:

biotools: sincell

Cell differentiation processes are achieved through a continuum of hierarchical intermediate cell-states that might be captured by single-cell RNA seq. Existing computational approaches for the assessment of cell-state hierarchies from single-cell data might be formalized under a general workflow composed of i) a metric to assess cell-to-cell similarities (combined or not with a dimensionality reduction step), and ii) a graph-building algorithm (optionally making use of a cells-clustering step). Sincell R package implements a methodological toolbox allowing flexible workflows under such framework. Furthermore, Sincell contributes new algorithms to provide cell-state hierarchies with statistical support while accounting for stochastic factors in single-cell RNA seq. Graphical representations and functional association tests are provided to interpret hierarchies.

package bioconductor-sincell

versions:

1.34.0-0,  1.32.0-0,  1.30.0-1,  1.30.0-0,  1.26.0-2,  1.26.0-1,  1.26.0-0,  1.24.0-0,  1.22.0-1, 

1.34.0-0,  1.32.0-0,  1.30.0-1,  1.30.0-0,  1.26.0-2,  1.26.0-1,  1.26.0-0,  1.24.0-0,  1.22.0-1,  1.22.0-0,  1.20.0-0,  1.18.0-0,  1.16.0-1,  1.16.0-0,  1.14.1-0,  1.14.0-0,  1.12.0-0,  1.10.0-0

depends libblas:

>=3.9.0,<4.0a0

depends libgcc-ng:

>=12

depends liblapack:

>=3.9.0,<4.0a0

depends libstdcxx-ng:

>=12

depends r-base:

>=4.3,<4.4.0a0

depends r-cluster:

depends r-entropy:

depends r-fastica:

depends r-fields:

depends r-ggplot2:

depends r-igraph:

depends r-mass:

depends r-proxy:

depends r-rcpp:

>=0.11.2

depends r-reshape2:

depends r-rtsne:

depends r-scatterplot3d:

depends r-statmod:

depends r-tsp:

requirements:

Installation

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-sincell

and update with::

   mamba update bioconductor-sincell

To create a new environment, run:

mamba create --name myenvname bioconductor-sincell

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 quay.io/biocontainers/bioconductor-sincell:<tag>

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

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