recipe bioconductor-lrcell

Differential cell type change analysis using Logistic/linear Regression

Homepage:

https://bioconductor.org/packages/3.18/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:

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

depends bioconductor-annotationhub:

>=3.10.0,<3.11.0

depends bioconductor-biocparallel:

>=1.36.0,<1.37.0

depends bioconductor-experimenthub:

>=2.10.0,<2.11.0

depends r-base:

>=4.3,<4.4.0a0

depends r-dplyr:

depends r-ggplot2:

depends r-ggrepel:

depends r-magrittr:

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-lrcell

and update with::

   mamba update bioconductor-lrcell

To create a new environment, run:

mamba create --name myenvname bioconductor-lrcell

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-lrcell:<tag>

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

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