stochprofML

stochprofML quantifies molecular heterogeneity by applying maximum likelihood estimation to cumulative expression data from small random pools of cells.

Key Features:

• Stochastic profiling: Analyzes variability in gene expression across cell populations within heterogeneous tissues using pooled measurements.
• Maximum Likelihood Estimation (MLE): Employs MLE principles to estimate parameters describing underlying heterogeneity from cumulative expression data.
• Analysis of small random pools: Operates on cumulative expression data generated from small random pools of cells rather than individual-cell measurements.
• Processing of pooled single-cell data: Processes pooled single-cell expression data directly without requiring demixing of mixed samples.
• Parameterization of heterogeneity: Estimates composition of cell pools and identifies differences between cell populations across samples.
• Simulation-based validation: Has been evaluated using simulation studies to assess accuracy in recovering heterogeneity parameters.
• Implementation: Provided as an R package for computational analysis.

Scientific Applications:

• Developmental biology: Quantifies cellular heterogeneity to investigate regulatory mechanisms and cell fate decisions during development.
• Disease research: Detects differences in cell population composition to study tissue heterogeneity relevant to disease progression and therapeutic targeting.
• Single-cell analysis with pooled samples: Enables analysis of pooled single-cell expression data when individual-cell profiling is limited by sample size or cost.

Methodology:

Applies maximum likelihood estimation to cumulative expression data from small random pools of cells to parameterize heterogeneity and uses simulation studies for validation.