Cargando…

Quantitative reproducibility analysis for identifying reproducible targets from high-throughput experiments

BACKGROUND: High-throughput assays are widely used in biological research to select potential targets. One single high-throughput experiment can efficiently study a large number of candidates simultaneously, but is subject to substantial variability. Therefore it is scientifically important to perfo...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhang, Wenfei, Liu, Ying, Zhang, Mindy, Zhu, Cheng, Lu, Yuefeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5553769/
https://www.ncbi.nlm.nih.gov/pubmed/28800759
http://dx.doi.org/10.1186/s12918-017-0444-y
_version_ 1783256670647877632
author Zhang, Wenfei
Liu, Ying
Zhang, Mindy
Zhu, Cheng
Lu, Yuefeng
author_facet Zhang, Wenfei
Liu, Ying
Zhang, Mindy
Zhu, Cheng
Lu, Yuefeng
author_sort Zhang, Wenfei
collection PubMed
description BACKGROUND: High-throughput assays are widely used in biological research to select potential targets. One single high-throughput experiment can efficiently study a large number of candidates simultaneously, but is subject to substantial variability. Therefore it is scientifically important to performance quantitative reproducibility analysis to identify reproducible targets with consistent and significant signals across replicate experiments. A few methods exist, but all have limitations. METHODS: In this paper, we propose a new method for identifying reproducible targets. Considering a Bayesian hierarchical model, we show that the test statistics from replicate experiments follow a mixture of multivariate Gaussian distributions, with the one component with zero-mean representing the irreproducible targets. RESULTS: A target is thus classified as reproducible or irreproducible based on its posterior probability belonging to the reproducible components. We study the performance of our proposed method using simulations and a real data example. CONCLUSION: The proposed method is shown to have favorable performance in identifying reproducible targets compared to other methods.
format Online
Article
Text
id pubmed-5553769
institution National Center for Biotechnology Information
language English
publishDate 2017
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-55537692017-08-15 Quantitative reproducibility analysis for identifying reproducible targets from high-throughput experiments Zhang, Wenfei Liu, Ying Zhang, Mindy Zhu, Cheng Lu, Yuefeng BMC Syst Biol Methodology Article BACKGROUND: High-throughput assays are widely used in biological research to select potential targets. One single high-throughput experiment can efficiently study a large number of candidates simultaneously, but is subject to substantial variability. Therefore it is scientifically important to performance quantitative reproducibility analysis to identify reproducible targets with consistent and significant signals across replicate experiments. A few methods exist, but all have limitations. METHODS: In this paper, we propose a new method for identifying reproducible targets. Considering a Bayesian hierarchical model, we show that the test statistics from replicate experiments follow a mixture of multivariate Gaussian distributions, with the one component with zero-mean representing the irreproducible targets. RESULTS: A target is thus classified as reproducible or irreproducible based on its posterior probability belonging to the reproducible components. We study the performance of our proposed method using simulations and a real data example. CONCLUSION: The proposed method is shown to have favorable performance in identifying reproducible targets compared to other methods. BioMed Central 2017-08-11 /pmc/articles/PMC5553769/ /pubmed/28800759 http://dx.doi.org/10.1186/s12918-017-0444-y Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Methodology Article
Zhang, Wenfei
Liu, Ying
Zhang, Mindy
Zhu, Cheng
Lu, Yuefeng
Quantitative reproducibility analysis for identifying reproducible targets from high-throughput experiments
title Quantitative reproducibility analysis for identifying reproducible targets from high-throughput experiments
title_full Quantitative reproducibility analysis for identifying reproducible targets from high-throughput experiments
title_fullStr Quantitative reproducibility analysis for identifying reproducible targets from high-throughput experiments
title_full_unstemmed Quantitative reproducibility analysis for identifying reproducible targets from high-throughput experiments
title_short Quantitative reproducibility analysis for identifying reproducible targets from high-throughput experiments
title_sort quantitative reproducibility analysis for identifying reproducible targets from high-throughput experiments
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5553769/
https://www.ncbi.nlm.nih.gov/pubmed/28800759
http://dx.doi.org/10.1186/s12918-017-0444-y
work_keys_str_mv AT zhangwenfei quantitativereproducibilityanalysisforidentifyingreproducibletargetsfromhighthroughputexperiments
AT liuying quantitativereproducibilityanalysisforidentifyingreproducibletargetsfromhighthroughputexperiments
AT zhangmindy quantitativereproducibilityanalysisforidentifyingreproducibletargetsfromhighthroughputexperiments
AT zhucheng quantitativereproducibilityanalysisforidentifyingreproducibletargetsfromhighthroughputexperiments
AT luyuefeng quantitativereproducibilityanalysisforidentifyingreproducibletargetsfromhighthroughputexperiments