Package Recipe 'bioconductor-scshapes'

recipe bioconductor-scshapes

A Statistical Framework for Modeling and Identifying Differential Distributions in Single-cell RNA-sequencing Data

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

We present a novel statistical framework for identifying differential distributions in single-cell RNA-sequencing (scRNA-seq) data between treatment conditions by modeling gene expression read counts using generalized linear models (GLMs). We model each gene independently under each treatment condition using error distributions Poisson (P), Negative Binomial (NB), Zero-inflated Poisson (ZIP) and Zero-inflated Negative Binomial (ZINB) with log link function and model based normalization for differences in sequencing depth. Since all four distributions considered in our framework belong to the same family of distributions, we first perform a Kolmogorov-Smirnov (KS) test to select genes belonging to the family of ZINB distributions. Genes passing the KS test will be then modeled using GLMs. Model selection is done by calculating the Bayesian Information Criterion (BIC) and likelihood ratio test (LRT) statistic.

package bioconductor-scshapes¶

Versions:

1.16.0-0, 1.12.0-0, 1.8.0-0, 1.6.0-0, 1.4.0-0, 1.0.0-0

Depends:

on bioconductor-biocparallel >=1.44.0,<1.45.0
on r-base >=4.5,<4.6.0a0
on r-dgof
on r-emdbook
on r-magrittr
on r-mass
on r-matrix
on r-pscl
on r-vgam

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-scshapes

to add into an existing workspace instead, run:

pixi add bioconductor-scshapes

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-scshapes

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-scshapes

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-scshapes:<tag>

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