scRsQTL: Variation-splicing correlations from 10x Genomics Single-Cell RNA-Sequencing Data

With the recent advances in single-cell RNA-sequencing (scRNA-seq) technologies, estimation of allele expression from single cells is becoming increasingly reliable. Allele expression (AE) is both quantitative and dynamic and is an essential component of the genomic interactome. We explored the possibility to assess AE from scRNA-seq data and to correlate it to other quantitative and dynamic transcriptome features in search for functional genomic interactions. To do that, we estimate and analyze EA at heterozygous single nucleotide variant (SNV) loci from scRNA-seq data generated on 10x Genomics Chromium platform. This analysis was carried out on 26,640 human adipose-derived mesenchymal stem cells (from three healthy donors), with an average sequencing reads approximately 150K/cell (more than 4 billion scRNA-seq reads total). High quality SNV calls assessed in our study contained approximately 15% exonic and >50% intronic loci. To analyze the allele expression, we estimate the expressed Variant Allele Fraction (VAFRNA) from SNV-aware alignments and analyze its variance and distribution (mono- and bi-allelic) supported by different minimum number of sequencing reads (cutoffs). Our analysis shows that when assessing positions covered by a minimum of 3 unique sequencing reads over 50% of the heterozygous SNVs show bi-allelic expression, while at minimum of 10 reads nearly 90% of the SNVs are bi-allelic. We, then, analyze the relationship between VAFRNA and splicing, assessed as proportion of excised introns at loci with differential intron excision. To do that, we carried out splicing Quantitative Trait Loci (sQTL) analysis on this data (one cluster of a sample, herein), and analyzed the regression results. The results from our analyses demonstrate the feasibility of scVAFRNA estimation from current scRNA-seq datasets and shows that the 3’-based library generation protocol of 10x Genomics scRNA-seq data can be informative in SNV-based studies, including sQTL.

Author

• N M, Prashant N

Language

• en

Date created

• 2020

Type of Work

• Master's Thesis

License

• All rights reserved

Rights statement

• In Copyright

GW Unit

• Biochemistry & Systems Biology

Degree

• M.S.

Advisor

• Horvath, Anelia

Persistent URL

•  https://scholarspace.library.gwu.edu/etd/8049g567r