Dataset Information

Ultra-fast scalable estimation of single-cell differentiation potency from scRNA-Seq data.

ABSTRACT:

Motivation

An important task in the analysis of single-cell RNA-Seq data is the estimation of differentiation potency, as this can help identify stem-or-multipotent cells in non-temporal studies or in tissues where differentiation hierarchies are not well established. A key challenge in the estimation of single-cell potency is the need for a fast and accurate algorithm, scalable to large scRNA-Seq studies profiling millions of cells.

Results

Here, we present a single-cell potency measure, called Correlation of Connectome and Transcriptome (CCAT), which can return accurate single-cell potency estimates of a million cells in minutes, a 100-fold improvement over current state-of-the-art methods. We benchmark CCAT against 8 other single-cell potency models and across 28 scRNA-Seq studies, encompassing over 2 million cells, demonstrating comparable accuracy than the current state-of-the-art, at a significantly reduced computational cost, and with increased robustness to dropouts.

Availability and implementation

CCAT is part of the SCENT R-package, freely available from https://github.com/aet21/SCENT.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Teschendorff AE

PROVIDER: S-EPMC8275983 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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Publications

Ultra-fast scalable estimation of single-cell differentiation potency from scRNA-Seq data.

Teschendorff Andrew E AE Maity Alok K AK Hu Xue X Weiyan Chen C Lechner Matthias M

Bioinformatics (Oxford, England) 20210701 11

<h4>Motivation</h4>An important task in the analysis of single-cell RNA-Seq data is the estimation of differentiation potency, as this can help identify stem-or-multipotent cells in non-temporal studies or in tissues where differentiation hierarchies are not well established. A key challenge in the estimation of single-cell potency is the need for a fast and accurate algorithm, scalable to large scRNA-Seq studies profiling millions of cells.<h4>Results</h4>Here, we present a single-cell potency ...[more]

PMID: 33244588

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Project description:BackgroundRecent 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.ResultsTo 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.ConclusionsSCReadCounts 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.

Dataset Information

Ultra-fast scalable estimation of single-cell differentiation potency from scRNA-Seq data.

Motivation

Results

Availability and implementation

Supplementary information

Publications

Ultra-fast scalable estimation of single-cell differentiation potency from scRNA-Seq data.

Similar Datasets

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