IDEAS

IDEAS performs individual-level differential expression analysis of single-cell RNA sequencing (scRNA-seq) data from multiple individuals to identify genes whose per-individual expression distributions differ between predefined groups.

Key Features:

• R package implementation: Implemented as an R package for analysis of scRNA-seq data across multiple individuals.
• Individual-Level Analysis: Assesses gene expression at the individual level rather than aggregating cells across samples to preserve between-individual variability.
• Distribution-Based Summary: Summarizes each gene's expression in each individual as a distribution to capture single-cell variability within individuals.
• Statistical Methodology: Employs a statistical method tailored for scRNA-seq that evaluates whether per-individual expression distributions differ between two predefined groups.

Scientific Applications:

• Disease cohort comparisons: Applied to compare gene expression profiles across patient cohorts, including autism versus controls and COVID-19 patients with differing symptom severity, to identify differential expression and candidate biomarkers.

Methodology:

From scRNA-seq data collected across multiple individuals, IDEAS summarizes per-gene expression as per-individual distributions and applies a statistical test tailored for scRNA-seq to assess whether those distributions differ between two predefined groups.