XCVATR

XCVATR identifies and maps expressed genetic variants in single-cell and bulk RNA-sequencing data to reveal their association with cellular transcriptional states.

Key Features:

• Variant Detection and Characterization: Identifies single nucleotide polymorphisms (SNPs), small insertions/deletions, and copy number variations (CNVs) from RNA-seq data.
• Integration with Cellular Transcriptional States: Links detected variants to cellular transcriptional states to assess mutation influence across heterogeneous samples such as tumor tissues.
• Visualization and Pattern Recognition: Identifies and visualizes local enrichments ("clumps") of expressed variants within embedding spaces using coordinates derived from distance metrics such as principal component analysis (PCA) or t-distributed stochastic neighbor embedding (t-SNE).
• Flexibility in Data Analysis: Operates with any type of distance metric for cell embedding computation, enabling use across different embedding approaches.

Scientific Applications:

• Tumor Sample Analysis: Applied to single-cell RNA-seq tumor samples to reveal subtle differences in the impact of CNVs on tumor cellular states.
• Transcriptional State Insights: Detects enrichment patterns of expressed variants to provide insights into the transcriptional states of cells and samples.

Methodology:

Identifies variants within RNA-seq datasets, uses embedding coordinates derived from distance metrics such as PCA or t-SNE to detect local enrichments of expressed variants ("clumps") in embedding space, visualizes these enrichments to associate variants with cellular transcriptional states, and validates findings using simulations and analyses of real datasets.