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BEARscc: robustness of single-cell clusters determined using simulated technical replicates

ABSTRACT: BEARscc: robustness of single-cell clusters determined using simulated technical replicates

OTHER RELATED OMICS DATASETS IN: GSE95155

PROVIDER: PRJNA376200 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR5277240_1.fastq.gz	Fastqsanger.gz
	SRR5277240_2.fastq.gz	Fastqsanger.gz
	SRR5277250_1.fastq.gz	Fastqsanger.gz
	SRR5277250_2.fastq.gz	Fastqsanger.gz
	SRR5277260_1.fastq.gz	Fastqsanger.gz

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BEARscc: robustness of single-cell clusters determined using simulated technical replicates

Project description:Technical variance is a major confounding factor in single-cell RNA sequencing, not least because measurements on the same cell are not replicable. We developed BEARscc, a tool that simulates experiment-specific technical replicates based on a probabilistic model of technical variance trained on RNA spike-in measurements. We demonstrate that the tool improves the unsupervised classification of cells and aids the interpretation of single-cell RNA-seq experiments.

2018-04-03 | GSE95155 | GEO

BEARscc determines robustness of single-cell clusters using simulated technical replicates.

Project description:Single-cell messenger RNA sequencing (scRNA-seq) has emerged as a powerful tool to study cellular heterogeneity within complex tissues. Subpopulations of cells with common gene expression profiles can be identified by applying unsupervised clustering algorithms. However, technical variance is a major confounding factor in scRNA-seq, not least because it is not possible to replicate measurements on the same cell. Here, we present BEARscc, a tool that uses RNA spike-in controls to simulate experiment-specific technical replicates. BEARscc works with a wide range of existing clustering algorithms to assess the robustness of clusters to technical variation. We demonstrate that the tool improves the unsupervised classification of cells and facilitates the biological interpretation of single-cell RNA-seq experiments.

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Project description:Background: RNA-seq is revolutionizing the way we study transcriptomes. mRNA can be surveyed without prior knowledge of gene transcripts. Alternative splicing of transcript isoforms and the identification of previously unknown exons are being reported. Initial reports of differences in exon usage, and splicing between samples as well as quantitative differences among samples are beginning to surface. Biological variation has been reported to be larger than technical variation. In addition, technical variation has been reported to be in line with expectations due to random sampling. However, strategies for dealing with technical variation will differ depending on the magnitude. The size of technical variance, and the role of sampling are examined in this manuscript. Results: Independent Solexa/Illumina experiments containing technical replicates are analyzed. When coverage is low, large disagreements between technical replicates are apparent. Exon detection between technical replicates is highly variable when the coverage is less than 5 reads per nucleotide and estimates of gene expression are more likely to disagree when coverage is low. Although large disagreements in the estimates of expression are observed at all levels of coverage. Conclusions: Technical variability is too high to ignore. Technical variability results in inconsistent detection of exons at low levels of coverage. Further, the estimate of the relative abundance of a transcript can substantially disagree, even when coverage levels are high. This may be due to the low sampling fraction and if so, it will persist as an issue needing to be addressed in experimental design even as the next wave of technology produces larger numbers of reads. We provide practical recommendations for dealing with the technical variability, without dramatic cost increases.

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