LRcell

LRcell identifies specific sub-cell types that drive changes observed in bulk RNA-seq differential gene expression experiments by applying Logistic Regression on differential expression p-values using single-cell RNA-sequencing-derived cell marker genes.

Key Features:

• LRcell analysis: Applies Logistic Regression on the complete set of genes with differential expression p-values to calculate a cell-type significance p-value for each cell type using its marker genes as indicators.
• Plot generation: Generates visualizations to support interpretation of cell type enrichment results.
• Marker gene selection: Provides functionality to select and utilize cell marker genes for LRcell analysis.
• Pre-built marker gene sets: Includes marker genes for the Prefrontal Cortex (pFC) human brain region, human PBMC, and nine mouse brain regions including Frontal Cortex, Cerebellum, Globus Pallidus, Hippocampus, Entopeduncular, Posterior Cortex, Striatum, Substantia Nigra, and Thalamus.

Scientific Applications:

• Bulk RNA-seq deconvolution: Identifies which specific sub-cell types are likely responsible for differential gene expression patterns observed in bulk RNA-seq experiments.
• Cell type-specific change detection: Predicts cell types driving transcriptional changes in tissues of interest by comparing cell-type significance p-values.

Methodology:

LRcell utilizes sets of cell marker genes acquired from single-cell RNA-sequencing as indicators for various cell types. For each cell type, using its marker genes as indicators, Logistic Regression is applied on the complete set of genes with differential expression p-values to calculate a cell-type significance p-value. These p-values are then compared to predict which cell type(s) are likely responsible for the differential gene expression pattern. LRcell is inspired by the LRpath algorithm developed by Sartor et al. for pathway/gene set enrichment analysis.