Journal Article

XCVATR: detection and characterization of variant impact on the Embeddings of single -cell and bulk RNA-sequencing samples

Klisch, Tiemo J., Harmanci, Akdes Serin, Patel, Akash J., Harmanci, Arif

2022

Overview

Background RNA-sequencing has become a standard tool for analyzing gene activity in bulk samples and at the single-cell level. By increasing sample sizes and cell counts, this technique can uncover substantial information about cellular transcriptional states. Beyond quantification of gene expression, RNA-seq can be used for detecting variants, including single nucleotide polymorphisms, small insertions/deletions, and larger variants, such as copy number variants. Notably, joint analysis of variants with cellular transcriptional states may provide insights into the impact of mutations, especially for complex and heterogeneous samples. However, this analysis is often challenging due to a prohibitively high number of variants and cells, which are difficult to summarize and visualize. Further, there is a dearth of methods that assess and summarize the association between detected variants and cellular transcriptional states. Results Here, we introduce XCVATR (e X pressed C lusters of V ariant A lleles in T ranscriptome p R ofiles), a method that identifies variants and detects local enrichment of expressed variants within embedding of samples and cells in single-cell and bulk RNA-seq datasets. XCVATR visualizes local “clumps” of small and large-scale variants and searches for patterns of association between each variant and cellular states, as described by the coordinates of cell embedding, which can be computed independently using any type of distance metrics, such as principal component analysis or t-distributed stochastic neighbor embedding. Through simulations and analysis of real datasets, we demonstrate that XCVATR can detect enrichment of expressed variants and provide insight into the transcriptional states of cells and samples. We next sequenced 2 new single cell RNA-seq tumor samples and applied XCVATR. XCVATR revealed subtle differences in CNV impact on tumors. Conclusions XCVATR is publicly available to download from https://github.com/harmancilab/XCVATR .

• Publisher: BioMed Central,BioMed Central Ltd,BMC
• Subject: Animal Genetics and Genomics / Applications software / Biomedical and Life Sciences / Copy number variations / Electronic data processing / Embedding / Gene Expression Profiling - methods / Genetic research / Genetic transcription / Genetic variation / High-Throughput Nucleotide Sequencing - methods / Life Sciences / Methods / Microarrays / Microbial Genetics and Genomics / Plant Genetics and Genomics / Proteomics / Research Article / RNA - genetics / RNA sequencing / RNA-Seq / Sequence Analysis, RNA - methods / Single cell RNA-sequencing / Single-Cell Analysis - methods / Transcriptome / Visualization (Computers)