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Towards unified quality verification of synthetic count data with countsimQC


Soneson, Charlotte; Robinson, Mark D (2018). Towards unified quality verification of synthetic count data with countsimQC. Bioinformatics, 34(4):691-692.

Abstract

Statistical tools for biological data analysis are often evaluated using synthetic data, designed to mimic the features of a specific type of experimental data. The generalizability of such evaluations depends on how well the synthetic data reproduce the main characteristics of the experimental data, and we argue that an assessment of this similarity should accompany any synthetic data set used for method evaluation. We describe countsimQC, which provides a straightforward way to generate a stand-alone report that shows the main characteristics of (e.g., RNA-seq) count data and can be provided alongside a publication as verification of the appropriateness of any utilized synthetic data.
Availability and implementation: countsimQC is implemented as an R package (for R versions ≥ 3.4) and is available from https://github.com/csoneson/countsimQC under a GPL (≥ 2) license.

Abstract

Statistical tools for biological data analysis are often evaluated using synthetic data, designed to mimic the features of a specific type of experimental data. The generalizability of such evaluations depends on how well the synthetic data reproduce the main characteristics of the experimental data, and we argue that an assessment of this similarity should accompany any synthetic data set used for method evaluation. We describe countsimQC, which provides a straightforward way to generate a stand-alone report that shows the main characteristics of (e.g., RNA-seq) count data and can be provided alongside a publication as verification of the appropriateness of any utilized synthetic data.
Availability and implementation: countsimQC is implemented as an R package (for R versions ≥ 3.4) and is available from https://github.com/csoneson/countsimQC under a GPL (≥ 2) license.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Molecular Life Sciences
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Date:2018
Deposited On:09 Jan 2018 09:30
Last Modified:01 Apr 2018 01:17
Publisher:Oxford University Press
ISSN:1367-4803
OA Status:Hybrid
Free access at:PubMed ID. An embargo period may apply.
Publisher DOI:https://doi.org/10.1093/bioinformatics/btx631
PubMed ID:29028961

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