Cargando…

Power and sample size estimation in microarray studies

BACKGROUND: Before conducting a microarray experiment, one important issue that needs to be determined is the number of arrays required in order to have adequate power to identify differentially expressed genes. This paper discusses some crucial issues in the problem formulation, parameter specifica...

Descripción completa

Detalles Bibliográficos
Autores principales: Lin, Wei-Jiun, Hsueh, Huey-Miin, Chen, James J
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2837028/
https://www.ncbi.nlm.nih.gov/pubmed/20100337
http://dx.doi.org/10.1186/1471-2105-11-48
_version_ 1782178763271831552
author Lin, Wei-Jiun
Hsueh, Huey-Miin
Chen, James J
author_facet Lin, Wei-Jiun
Hsueh, Huey-Miin
Chen, James J
author_sort Lin, Wei-Jiun
collection PubMed
description BACKGROUND: Before conducting a microarray experiment, one important issue that needs to be determined is the number of arrays required in order to have adequate power to identify differentially expressed genes. This paper discusses some crucial issues in the problem formulation, parameter specifications, and approaches that are commonly proposed for sample size estimation in microarray experiments. Common methods for sample size estimation are formulated as the minimum sample size necessary to achieve a specified sensitivity (proportion of detected truly differentially expressed genes) on average at a specified false discovery rate (FDR) level and specified expected proportion (π(1)) of the true differentially expression genes in the array. Unfortunately, the probability of detecting the specified sensitivity in such a formulation can be low. We formulate the sample size problem as the number of arrays needed to achieve a specified sensitivity with 95% probability at the specified significance level. A permutation method using a small pilot dataset to estimate sample size is proposed. This method accounts for correlation and effect size heterogeneity among genes. RESULTS: A sample size estimate based on the common formulation, to achieve the desired sensitivity on average, can be calculated using a univariate method without taking the correlation among genes into consideration. This formulation of sample size problem is inadequate because the probability of detecting the specified sensitivity can be lower than 50%. On the other hand, the needed sample size calculated by the proposed permutation method will ensure detecting at least the desired sensitivity with 95% probability. The method is shown to perform well for a real example dataset using a small pilot dataset with 4-6 samples per group. CONCLUSIONS: We recommend that the sample size problem should be formulated to detect a specified proportion of differentially expressed genes with 95% probability. This formulation ensures finding the desired proportion of true positives with high probability. The proposed permutation method takes the correlation structure and effect size heterogeneity into consideration and works well using only a small pilot dataset.
format Text
id pubmed-2837028
institution National Center for Biotechnology Information
language English
publishDate 2010
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-28370282010-03-12 Power and sample size estimation in microarray studies Lin, Wei-Jiun Hsueh, Huey-Miin Chen, James J BMC Bioinformatics Methodology article BACKGROUND: Before conducting a microarray experiment, one important issue that needs to be determined is the number of arrays required in order to have adequate power to identify differentially expressed genes. This paper discusses some crucial issues in the problem formulation, parameter specifications, and approaches that are commonly proposed for sample size estimation in microarray experiments. Common methods for sample size estimation are formulated as the minimum sample size necessary to achieve a specified sensitivity (proportion of detected truly differentially expressed genes) on average at a specified false discovery rate (FDR) level and specified expected proportion (π(1)) of the true differentially expression genes in the array. Unfortunately, the probability of detecting the specified sensitivity in such a formulation can be low. We formulate the sample size problem as the number of arrays needed to achieve a specified sensitivity with 95% probability at the specified significance level. A permutation method using a small pilot dataset to estimate sample size is proposed. This method accounts for correlation and effect size heterogeneity among genes. RESULTS: A sample size estimate based on the common formulation, to achieve the desired sensitivity on average, can be calculated using a univariate method without taking the correlation among genes into consideration. This formulation of sample size problem is inadequate because the probability of detecting the specified sensitivity can be lower than 50%. On the other hand, the needed sample size calculated by the proposed permutation method will ensure detecting at least the desired sensitivity with 95% probability. The method is shown to perform well for a real example dataset using a small pilot dataset with 4-6 samples per group. CONCLUSIONS: We recommend that the sample size problem should be formulated to detect a specified proportion of differentially expressed genes with 95% probability. This formulation ensures finding the desired proportion of true positives with high probability. The proposed permutation method takes the correlation structure and effect size heterogeneity into consideration and works well using only a small pilot dataset. BioMed Central 2010-01-25 /pmc/articles/PMC2837028/ /pubmed/20100337 http://dx.doi.org/10.1186/1471-2105-11-48 Text en Copyright ©2010 Lin et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Methodology article
Lin, Wei-Jiun
Hsueh, Huey-Miin
Chen, James J
Power and sample size estimation in microarray studies
title Power and sample size estimation in microarray studies
title_full Power and sample size estimation in microarray studies
title_fullStr Power and sample size estimation in microarray studies
title_full_unstemmed Power and sample size estimation in microarray studies
title_short Power and sample size estimation in microarray studies
title_sort power and sample size estimation in microarray studies
topic Methodology article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2837028/
https://www.ncbi.nlm.nih.gov/pubmed/20100337
http://dx.doi.org/10.1186/1471-2105-11-48
work_keys_str_mv AT linweijiun powerandsamplesizeestimationinmicroarraystudies
AT hsuehhueymiin powerandsamplesizeestimationinmicroarraystudies
AT chenjamesj powerandsamplesizeestimationinmicroarraystudies