scDD

scDD models differential distributions in single-cell RNA-seq data to identify and quantify complex gene expression changes and cellular heterogeneity.

Key Features:

• Flexible Modeling: scDD utilizes Dirichlet Process mixture models to represent complex gene expression distributions in single-cell RNA-seq data.
• Detection of Differential Distribution Patterns: scDD identifies genes with differential distribution patterns across biological conditions, detecting changes beyond simple mean shifts.
• Characterization of Complex Differences: scDD characterizes complex expression differences that occur within and among distinct cellular states.
• Simulation Functions: scDD provides functions to simulate data with specific patterns from negative binomial distributions.

Scientific Applications:

• Quantifying Cellular Heterogeneity: scDD characterizes expression differences across distinct cellular states to inform on cellular heterogeneity.
• Comparative Analysis Across Conditions: scDD enables comparison of gene expression distribution patterns between conditions or treatments to identify differential behavior.
• Detection of Subtle Expression Changes: scDD detects subtle and complex distributional changes that may be missed by mean-based methods.

Methodology:

Implements Dirichlet Process mixture models to model distinct expression states and includes simulation functions that generate data from negative binomial distributions.