Package Recipe 'bioconductor-lrcell'

recipe bioconductor-lrcell

Differential cell type change analysis using Logistic/linear Regression

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

The goal of LRcell is to identify specific sub-cell types that drives the changes observed in a bulk RNA-seq differential gene expression experiment. To achieve this, LRcell utilizes sets of cell marker genes acquired from single-cell RNA-sequencing (scRNA-seq) as indicators for various cell types in the tissue of interest. Next, for each cell type, using its marker genes as indicators, we apply Logistic Regression on the complete set of genes with differential expression p-values to calculate a cell-type significance p-value. Finally, these p-values are compared to predict which one(s) are likely to be responsible for the differential gene expression pattern observed in the bulk RNA-seq experiments. LRcell is inspired by the LRpath[@sartor2009lrpath] algorithm developed by Sartor et al., originally designed for pathway/gene set enrichment analysis. LRcell contains three major components: LRcell analysis, plot generation and marker gene selection. All modules in this package are written in R. This package also provides marker genes in the Prefrontal Cortex (pFC) human brain region, human PBMC and nine mouse brain regions (Frontal Cortex, Cerebellum, Globus Pallidus, Hippocampus, Entopeduncular, Posterior Cortex, Striatum, Substantia Nigra and Thalamus).

package bioconductor-lrcell¶

Versions:

1.18.0-0, 1.14.0-0, 1.10.0-0, 1.8.0-0, 1.6.0-0, 1.2.0-0, 1.0.0-0

Depends:

on bioconductor-annotationhub >=4.0.0,<4.1.0
on bioconductor-biocparallel >=1.44.0,<1.45.0
on bioconductor-experimenthub >=3.0.0,<3.1.0
on r-base >=4.5,<4.6.0a0
on r-dplyr
on r-ggplot2
on r-ggrepel
on r-magrittr

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-lrcell

to add into an existing workspace instead, run:

pixi add bioconductor-lrcell

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-lrcell

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-lrcell

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-lrcell:<tag>

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