Package Recipe 'bioconductor-basics'

recipe bioconductor-basics

Bayesian Analysis of Single-Cell Sequencing data

Homepage:: https://bioconductor.org/packages/3.20/bioc/html/BASiCS.html
License:: GPL-3
Recipe:: /bioconductor-basics/meta.yaml

Single-cell mRNA sequencing can uncover novel cell-to-cell heterogeneity in gene expression levels in seemingly homogeneous populations of cells. However, these experiments are prone to high levels of technical noise, creating new challenges for identifying genes that show genuine heterogeneous expression within the population of cells under study. BASiCS (Bayesian Analysis of Single-Cell Sequencing data) is an integrated Bayesian hierarchical model to perform statistical analyses of single-cell RNA sequencing datasets in the context of supervised experiments (where the groups of cells of interest are known a priori, e.g. experimental conditions or cell types). BASiCS performs built-in data normalisation (global scaling) and technical noise quantification (based on spike-in genes). BASiCS provides an intuitive detection criterion for highly (or lowly) variable genes within a single group of cells. Additionally, BASiCS can compare gene expression patterns between two or more pre-specified groups of cells. Unlike traditional differential expression tools, BASiCS quantifies changes in expression that lie beyond comparisons of means, also allowing the study of changes in cell-to-cell heterogeneity. The latter can be quantified via a biological over-dispersion parameter that measures the excess of variability that is observed with respect to Poisson sampling noise, after normalisation and technical noise removal. Due to the strong mean/over-dispersion confounding that is typically observed for scRNA-seq datasets, BASiCS also tests for changes in residual over-dispersion, defined by residual values with respect to a global mean/over-dispersion trend.

package bioconductor-basics¶

Versions:

2.22.0-0, 2.18.0-0, 2.14.0-0, 2.12.3-0, 2.10.0-1, 2.10.0-0, 2.6.0-2, 2.6.0-1, 2.6.0-0,

2.22.0-0, 2.18.0-0, 2.14.0-0, 2.12.3-0, 2.10.0-1, 2.10.0-0, 2.6.0-2, 2.6.0-1, 2.6.0-0, 2.4.0-0, 2.2.2-0, 2.2.0-0, 2.0.0-0, 1.8.0-0, 1.6.0-1, 1.4.0-0

Depends:

on bioconductor-biobase >=2.70.0,<2.71.0
on bioconductor-biobase >=2.70.0,<2.71.0a0
on bioconductor-biocgenerics >=0.56.0,<0.57.0
on bioconductor-biocgenerics >=0.56.0,<0.57.0a0
on bioconductor-biocparallel >=1.44.0,<1.45.0
on bioconductor-biocparallel >=1.44.0,<1.45.0a0
on bioconductor-s4vectors >=0.48.0,<0.49.0
on bioconductor-s4vectors >=0.48.0,<0.49.0a0
on bioconductor-scran >=1.38.0,<1.39.0
on bioconductor-scran >=1.38.0,<1.39.0a0
on bioconductor-scuttle >=1.20.0,<1.21.0
on bioconductor-scuttle >=1.20.0,<1.21.0a0
on bioconductor-singlecellexperiment >=1.32.0,<1.33.0
on bioconductor-singlecellexperiment >=1.32.0,<1.33.0a0
on bioconductor-summarizedexperiment >=1.40.0,<1.41.0
on bioconductor-summarizedexperiment >=1.40.0,<1.41.0a0
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-assertthat
on r-base >=4.5,<4.6.0a0
on r-coda
on r-cowplot
on r-ggextra
on r-ggplot2
on r-hexbin
on r-mass
on r-matrix >=1.5.0
on r-matrixstats
on r-posterior
on r-rcpp >=0.11.3
on r-rcpparmadillo
on r-reshape2
on r-viridis

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-basics

to add into an existing workspace instead, run:

pixi add bioconductor-basics

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-basics

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-basics

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-basics:<tag>

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