speckle

speckle provides statistical methods for analysing single cell RNA-seq data, with a focus on detecting differences in cell type proportions between experimental conditions.

Key Features:

• Cell type proportion analysis: Contains functions to analyse differences in cell type proportions between two or more experimental conditions.
• Beta distribution parameter estimation: Estimates the parameters of the Beta distribution based on a given counts matrix.
• Library size normalisation: Normalises a counts matrix to the median library size.
• Visualisation functions: Includes plotting functions to visualise cell type proportions and the mean-variance relationship in cell type proportions and counts.
• propeller method: Implements a robust and flexible method that leverages biological replication to find statistically significant differences in cell type proportions between groups.

Scientific Applications:

• Heart development studies: Applied to test for significant changes in cell type proportions related to human heart development.
• Ageing research: Used to identify cell type proportion changes associated with ageing.
• COVID-19 disease severity: Applied to analyse cell type proportion differences across COVID-19 disease severity levels.
• Disease and treatment response analysis: Enables detection of relative depletion or enrichment of particular cell types as functional consequences of disease or treatment.

Methodology:

propeller leverages biological replication to find statistically significant differences in cell type proportions between groups, with performance validated using simulated cell type proportions data under a variety of scenarios.