Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

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

ABSTRACT: 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.

OTHER RELATED OMICS DATASETS IN: PRJNA376200

ORGANISM(S): other sequences Homo sapiens

PROVIDER: GSE95155 | GEO | 2018/04/03

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

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

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

| PRJNA376200 | ENA

DNA methylation profiles of liver and placenta technical replicates

Project description:We profiled DNA from liver and placenta technical replicate samples on the Illumina HumanMethylation450 BeadChip array. There technical replicates represent a single liver and a single placenta sample.

2014-02-19 | GSE52731 | 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.

| S-EPMC5864873 | biostudies-literature

Technical replicates to determine array-to-array reproducibility.

Project description:Technical replicates, consisting of four independent hybridizations of the same labelled cRNA, were performed to determine array-to-array reproducibility. After normalization, correlation index between all replicates exceeded 98%.

2009-02-20 | GSE12118 | GEO

Characterization of technical variation in ChIP-on-chip studies

Project description:Analysis of technical variance of ChIP-on-Chip studies by characterization of Myc-binding in HL60 cells. Keywords: Chip-on-chip

2008-11-01 | GSE8448 | GEO

Study to evaluate the effect of technical repetition on gene expression measurements

Project description:To evaluate the effect of technical repetition on gene expression measurements, we repeated the laboratory protocol at 6 different steps. Information was used to select probesets with a high technical reproducibility for the development of gene signatures.

2019-05-30 | GSE129558 | GEO

Technical controls MiCA

Project description:Technical controls MiCA

| PRJEB30293 | ENA

Evaluation of technical (array-to-array) reproducibility of L. vannamei microarray

Project description:Five aliquots of the same RNA sample were labeled and hybridized in parallel to the L. vannamei microarray, in order to assess the technical reproducibility of the tool Keywords: technical validation

2006-06-01 | GSE4954 | GEO

RNA-seq: technical variability and sampling (male)

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.

2014-01-15 | GSE54068 | GEO

RNA-seq: technical variability and sampling (female)

2014-01-15 | GSE54067 | GEO