Red Panda

Red Panda: Variant Detection in Single-Cell RNA Sequencing Data

Red Panda detects single nucleotide variations and micro insertions/deletions (1–50 bp) from single-cell RNA sequencing (scRNA-seq) data using data-specific features to improve variant calling accuracy in single-cell transcriptomes.

Key Features:

• scRNA-seq-Specific Methodology: Leverages transcript-level and cell-specific information inherent to scRNA-seq to refine variant identification.
• High Positive Predictive Value: Achieved 45.0% PPV, outperforming FreeBayes, GATK HaplotypeCaller, GATK UnifiedGenotyper, Monovar, and Platypus (5.8%–41.53%).
• High Sensitivity: Reached 72.44% sensitivity on simulated mouse embryonic fibroblast (MEF) alignments.

Scientific Applications:

• Genetic Disease Research: Identifies variants in rare cell populations to support studies of cancer and autoimmune disorders using scRNA-seq data.

Methodology:

Validated on scRNA-seq datasets from human articular chondrocytes, mouse embryonic fibroblasts (MEFs), and simulated MEF alignments; performance evaluated using positive predictive value and sensitivity metrics in comparison with FreeBayes, GATK HaplotypeCaller, GATK UnifiedGenotyper, Monovar, and Platypus.