markerpen

markerpen identifies cell-type-specific marker genes from bulk transcriptome data by analyzing gene co-expression patterns to refine candidate marker lists for downstream analyses such as deconvolution.

Key Features:

• Identification from Bulk Data: Leverages the correlation structure in bulk transcriptome data, exploiting that marker genes for a given cell type show high correlation across tissue samples because their measured expression is driven by cell-type proportion.
• Semi-Supervised Algorithm: Integrates published information about potential marker genes with bulk transcriptome data using a semi-supervised approach to evaluate candidate markers.
• Refinement Process: Refines candidate marker lists by adding or removing genes based on their correlation patterns in the analyzed bulk samples to improve marker accuracy and reliability.
• Implementation: Provided as an R package for computational analysis of marker gene identification from bulk data.

Scientific Applications:

• Deconvolution Studies: Enables estimation of cell-type composition in mixed tissue samples by providing refined cell-type-specific marker genes.
• Gene Expression Analysis: Supports improved interpretation of tissue- and condition-specific expression patterns by identifying reliable marker genes from bulk data.

Methodology:

Analyzes the correlation structure of bulk transcriptome data and applies a semi-supervised algorithm that integrates published candidate marker lists with bulk data to refine markers by adding or removing genes based on correlation patterns.