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license: MIT
title: 'LRcell: Differential cell type change analysis using Logistic/linear Regression'
version: 1.13.0
abstract: '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).'
authors:
- family-names: Ma
  given-names: Wenjing
  email: wenjing.ma@emory.edu
  orcid: https://orcid.org/0000-0001-8757-651X
repository: https://bioc.r-universe.dev
repository-code: https://github.com/marvinquiet/LRcell
url: https://github.com/marvinquiet/LRcell
date-released: '2021-03-10'
contact:
- family-names: Ma
  given-names: Wenjing
  email: wenjing.ma@emory.edu
  orcid: https://orcid.org/0000-0001-8757-651X