scReQTL: an approach to correlate SNVs to gene expression from individual scRNA-seq datasets

Liu, Hongyu; Prashant, N M; Bousounis, Pavlos; Alomran, Nawaf; Ibeawuchi, Helen; Sein, Justin; Słowiński, Piotr; Tsaneva-Atanasova, Krasimira; Horvath, Anélia

doi:10.1101/2020.07.13.200956

Cited by 5 publications

(15 citation statements)

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“…SCReadCounts can be applied in conjunction with variant callers to estimate the cell-specific allele expression of germline or somatic SNVs. To explore this application, we called variants from the pooled alignments using GATK (v4.1.7.0, [ 22 ]), and filtered the calls retaining high quality biallelic positions for which both the variant and the reference allele were supported by a minimum of 50 sequencing reads, as we have previously described [ 24 , 26 ]. The variant lists were then provided to SCReadCounts together with the corresponding alignments and the STARsolo generated list of error corrected barcodes.…”

Section: Resultsmentioning

confidence: 99%

“…In addition, VAF RNA estimates can be used to explore correlations between allele- and gene-expression in single cells using scReQTL. Our previous research applying scReQTL on normal adipose datasets has shown that scReQTLs are substantially enriched in GWAS-significant SNVs and in known gene-gene interactions [ 24 ].…”

Section: Resultsmentioning

confidence: 99%

“…Here, a major consideration is the balance between inclusivity (low minR) and higher-confidence VAF RNA estimates (high minR). In our analyses we use different minR depending on the application [ 24 , 26 ]. For example, to confidently estimate allele expression of germline variants in highly transcribed genes, high minR is needed.…”

Section: Discussionmentioning

confidence: 99%

“…This is particularly useful in scRNA-seq settings, where it enables distinguishing cells with monoallelic reference expression from those with no gene expression. The later can be used to assess cell-level allele dynamics, and to correlate variant expression to gene expression [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

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SCReadCounts: estimation of cell-level SNVs expression from scRNA-seq data

et al. 2021

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Background Recent studies have demonstrated the utility of scRNA-seq SNVs to distinguish tumor from normal cells, characterize intra-tumoral heterogeneity, and define mutation-associated expression signatures. In addition to cancer studies, SNVs from single cells have been useful in studies of transcriptional burst kinetics, allelic expression, chromosome X inactivation, ploidy estimations, and haplotype inference. Results To aid these types of studies, we have developed a tool, SCReadCounts, for cell-level tabulation of the sequencing read counts bearing SNV reference and variant alleles from barcoded scRNA-seq alignments. Provided genomic loci and expected alleles, SCReadCounts generates cell-SNV matrices with the absolute variant- and reference-harboring read counts, as well as cell-SNV matrices of expressed Variant Allele Fraction (VAFRNA) suitable for a variety of downstream applications. We demonstrate three different SCReadCounts applications on 59,884 cells from seven neuroblastoma samples: (1) estimation of cell-level expression of known somatic mutations and RNA-editing sites, (2) estimation of cell- level allele expression of biallelic SNVs, and (3) a discovery mode assessment of the reference and each of the three alternative nucleotides at genomic positions of interest that does not require prior SNV information. For the later, we applied SCReadCounts on the coding regions of KRAS, where it identified known and novel somatic mutations in a low-to-moderate proportion of cells. The SCReadCounts read counts module is benchmarked against the analogous modules of GATK and Samtools. SCReadCounts is freely available (https://github.com/HorvathLab/NGS) as 64-bit self-contained binary distributions for Linux and MacOS, in addition to Python source. Conclusions SCReadCounts supplies a fast and efficient solution for estimation of cell-level SNV expression from scRNA-seq data. SCReadCounts enables distinguishing cells with monoallelic reference expression from those with no gene expression and is applicable to assess SNVs present in only a small proportion of the cells, such as somatic mutations in cancer.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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SCReadCounts: estimation of cell-level SNVs expression from scRNA-seq data

et al. 2021

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“…Estimation of single nucleotide variants (SNV) expression from single cell RNA sequencing (scRNA-seq) data is an emerging field with quickly expanding applications, including assessment of allele expression, transcriptional burst kinetics, quantitative loci traits (QTLs), haplotype inference, X-chromosome inactivation, and demultiplexing (Vu et al ., 2019; Larsson et al ., 2019; Reinius et al ., 2016; Van Der Wijst et al ., 2018; Hongyu Liu; Prashant et al ., 2019, 2020; Edsgärd et al ., 2016; Xu et al ., 2019; Griffiths et al ., 2017; D’Antonio-Chronowska et al ., 2019). In cancer, studies on cell-level genetic heterogeneity have been instrumental to trace lineages and resolve subclonal architecture (Vu et al ., 2019; Lee et al ., 2017; Puram et al ., 2017; Venteicher et al ., 2017; Müller et al ., 2016).…”

Section: Introductionmentioning

confidence: 99%

SCReadCounts: Estimation of cell-level SNVs from scRNA-seq data

Prashant

Alomran

Chen

et al. 2020

Preprint

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SummarySCReadCounts is a method for a cell-level estimation of the sequencing read counts bearing a particular nucleotide at genomic positions of interest from barcoded scRNA-seq alignments. SCReadCounts generates an array of outputs, including cell-SNV matrices with the absolute variant-harboring read counts, as well as cell-SNV matrices with expressed Variant Allele Fraction (VAFRNA); we demonstrate its application to estimate cell level expression of somatic mutations and RNA-editing on cancer datasets. SCReadCounts is benchmarked against GATK and Samtools and is freely available as a 64-bit self-contained binary distribution (Linux), along with MacOS and Python installation.Availabilityhttps://github.com/HorvathLab/NGS/tree/master/SCReadCountsSupplementary InformationSCReadCounts_Supplementary_Data.zip

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Genetic control of the dynamic transcriptional response to immune stimuli and glucocorticoids at single cell resolution

Jiang

Zilioli

et al. 2021

Preprint

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Synthetic glucocorticoids are used to treat many immune conditions, such as asthma and severe COVID-19. Single cell data capture fine-grained details of transcriptional variability and dynamics to gain a better understanding of the molecular underpinnings of inter-individual variation in drug response. We used single cell RNA-seq to study the dynamics of the transcriptional response to glucocorticoids in activated PBMCs from African American donors. We employed novel statistical approaches to calculate a mean-independent measure of gene expression variability and a measure of transcriptional response pseudotime. We demonstrated that glucocorticoids reverse the effects of immune stimulation on both gene expression mean and variability. Our novel measure of gene expression response dynamics separated cells by response status and identified dynamic transcriptional patterns along the response pseudotime. We identified genetic variants regulating gene expression mean and variability, including treatment-specific effects, and demonstrated widespread genetic regulation of the transcriptional dynamics of the gene expression response.

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scReQTL: an approach to correlate SNVs to gene expression from individual scRNA-seq datasets

Cited by 5 publications

References 51 publications

SCReadCounts: estimation of cell-level SNVs expression from scRNA-seq data

SCReadCounts: estimation of cell-level SNVs expression from scRNA-seq data

SCReadCounts: Estimation of cell-level SNVs from scRNA-seq data

Genetic control of the dynamic transcriptional response to immune stimuli and glucocorticoids at single cell resolution

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