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A pair of datasets for microRNA expression profiling to examine the use of careful study design for assigning arrays to samples

We set out to demonstrate the logistic feasibility of careful experimental design for microarray studies and its level of scientific benefits for improving the accuracy and reproducibility of data inference. Towards this end, we conducted a study of microRNA expression using endometrioid endometrial...

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Autores principales: Qin, Li-Xuan, Huang, Huei-Chung, Villafania, Liliana, Cavatore, Magali, Olvera, Narciso, Levine, Douglas A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5952865/
https://www.ncbi.nlm.nih.gov/pubmed/29762551
http://dx.doi.org/10.1038/sdata.2018.84
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author Qin, Li-Xuan
Huang, Huei-Chung
Villafania, Liliana
Cavatore, Magali
Olvera, Narciso
Levine, Douglas A.
author_facet Qin, Li-Xuan
Huang, Huei-Chung
Villafania, Liliana
Cavatore, Magali
Olvera, Narciso
Levine, Douglas A.
author_sort Qin, Li-Xuan
collection PubMed
description We set out to demonstrate the logistic feasibility of careful experimental design for microarray studies and its level of scientific benefits for improving the accuracy and reproducibility of data inference. Towards this end, we conducted a study of microRNA expression using endometrioid endometrial tumours (n=96) and serous ovarian tumours (n=96) that were primary, untreated, and collected from 2000 to 2012 at Memorial Sloan Kettering Cancer Center. The same set of tumour tissue samples were profiled twice using the Agilent microRNA microarrays: once under an ideal experimental condition with balanced array-to-sample allocation and uniform handling; a second time by mimicking typical practice, with arrays assigned in the order of sample collection and processed by two technicians in multiple batches. This paper provides a detailed description of the generation and validation of this unique dataset pair so that the research community can re-use it to investigate other statistical questions regarding microarray study design and data analysis, and to address biological questions on the relevance of microRNA expression in gynaecologic cancer.
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spelling pubmed-59528652018-05-30 A pair of datasets for microRNA expression profiling to examine the use of careful study design for assigning arrays to samples Qin, Li-Xuan Huang, Huei-Chung Villafania, Liliana Cavatore, Magali Olvera, Narciso Levine, Douglas A. Sci Data Data Descriptor We set out to demonstrate the logistic feasibility of careful experimental design for microarray studies and its level of scientific benefits for improving the accuracy and reproducibility of data inference. Towards this end, we conducted a study of microRNA expression using endometrioid endometrial tumours (n=96) and serous ovarian tumours (n=96) that were primary, untreated, and collected from 2000 to 2012 at Memorial Sloan Kettering Cancer Center. The same set of tumour tissue samples were profiled twice using the Agilent microRNA microarrays: once under an ideal experimental condition with balanced array-to-sample allocation and uniform handling; a second time by mimicking typical practice, with arrays assigned in the order of sample collection and processed by two technicians in multiple batches. This paper provides a detailed description of the generation and validation of this unique dataset pair so that the research community can re-use it to investigate other statistical questions regarding microarray study design and data analysis, and to address biological questions on the relevance of microRNA expression in gynaecologic cancer. Nature Publishing Group 2018-05-15 /pmc/articles/PMC5952865/ /pubmed/29762551 http://dx.doi.org/10.1038/sdata.2018.84 Text en Copyright © 2018, The Author(s) http://creativecommons.org/licenses/by/4.0/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files made available in this article.
spellingShingle Data Descriptor
Qin, Li-Xuan
Huang, Huei-Chung
Villafania, Liliana
Cavatore, Magali
Olvera, Narciso
Levine, Douglas A.
A pair of datasets for microRNA expression profiling to examine the use of careful study design for assigning arrays to samples
title A pair of datasets for microRNA expression profiling to examine the use of careful study design for assigning arrays to samples
title_full A pair of datasets for microRNA expression profiling to examine the use of careful study design for assigning arrays to samples
title_fullStr A pair of datasets for microRNA expression profiling to examine the use of careful study design for assigning arrays to samples
title_full_unstemmed A pair of datasets for microRNA expression profiling to examine the use of careful study design for assigning arrays to samples
title_short A pair of datasets for microRNA expression profiling to examine the use of careful study design for assigning arrays to samples
title_sort pair of datasets for microrna expression profiling to examine the use of careful study design for assigning arrays to samples
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5952865/
https://www.ncbi.nlm.nih.gov/pubmed/29762551
http://dx.doi.org/10.1038/sdata.2018.84
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