ImmQuant

ImmQuant applies the DCQ (Differential Cell Quantification) deconvolution algorithm to bulk transcriptome data to estimate immune cell-type proportions in complex tissues for analysis of immune-cell subset composition.

Key Features:

• Deconvolution methodology: Implements the DCQ algorithm to predict differences in cell-type quantities across multiple tissue samples based on transcription profiles.
• Transcriptome-based analysis: Operates on bulk transcriptome/transcription profiles to infer cellular composition from gene expression data.
• Comparative quantification: Estimates relative changes and proportions of different cell types between samples.
• Visualization tools: Provides visualization of inferred cell-type alterations to support inspection and interpretation of deconvolution results.

Scientific Applications:

• Investigation of immune cell subsets: Enables detailed analysis of hundreds of immune cell subsets in mouse tissues and a few dozen cell types in human samples.
• Disease and pathology research: Supports studies of immune-cell composition to investigate mechanisms underlying major diseases and pathologies.

Methodology:

Performs computational deconvolution using the DCQ algorithm on bulk transcriptome data to estimate proportions of different cell types and predict differences in cell-type quantities across samples.