Package Recipe 'bioconductor-sincell'

recipe bioconductor-sincell

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

Homepage:: https://bioconductor.org/packages/3.20/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:

1.42.0-0, 1.38.0-1, 1.38.0-0, 1.34.0-0, 1.32.0-0, 1.30.0-1, 1.30.0-0, 1.26.0-2, 1.26.0-1,

1.42.0-0, 1.38.0-1, 1.38.0-0, 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:

on libblas >=3.9.0,<4.0a0
on libgcc >=14
on liblapack >=3.9.0,<4.0a0
on liblzma >=5.8.2,<6.0a0
on libstdcxx >=14
on libzlib >=1.3.1,<2.0a0
on r-base >=4.5,<4.6.0a0
on r-cluster
on r-entropy
on r-fastica
on r-fields
on r-ggplot2
on r-igraph
on r-mass
on r-proxy
on r-rcpp >=0.11.2
on r-reshape2
on r-rtsne
on r-scatterplot3d
on r-statmod
on r-tsp

Additional platforms:

linux-aarch64

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-sincell

to add into an existing workspace instead, run:

pixi add bioconductor-sincell

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-sincell

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-sincell

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-sincell:<tag>

(see bioconductor-sincell/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-sincell/README.html)