RESA

RESA identifies expressed somatic single nucleotide variants (SNVs) from single-cell RNA sequencing (scRNA-seq) data to enable analysis of mutational heterogeneity and drug resistance in cancer.

Key Features:

• Precision and Performance: Demonstrates an average precision of 0.77 across three in silico spike-in datasets for expressed somatic mutation detection.
• Benchmarking Success: Outperforms existing methods across 19 diverse datasets in benchmarking comparisons.
• Intratumor Heterogeneity Analysis: Has been applied to a melanoma drug resistance dataset to analyze intratumor mutational heterogeneity.

Scientific Applications:

• Understanding Tumor Evolution: Identifies actively expressed mutations to inform evolutionary pathways within tumors.
• Investigating Drug Resistance: Detects expressed somatic mutations associated with treatment response in melanoma drug resistance datasets.
• Enhancing Single-Cell Analysis: Improves reliability of mutational analysis at single-cell resolution to characterize cellular diversity within tumors.

Methodology:

Performs de novo identification of mutations directly from scRNA-seq data and applies recurrent expression analysis by focusing on recurrently expressed single nucleotide variants (SNVs) within a computational framework tailored to scRNA-seq data complexities.