The PowerAtlas: a power and sample size atlas for microarray experimental design and research

BACKGROUND: Microarrays permit biologists to simultaneously measure the mRNA abundance of thousands of genes. An important issue facing investigators planning microarray experiments is how to estimate the sample size required for good statistical power. What is the projected sample size or number of replicate chips needed to address the multiple hypotheses with acceptable accuracy? Statistical methods exist for calculating power based upon a single hypothesis, using estimates of the variability in data from pilot studies. There is, however, a need for methods to estimate power and/or required sample sizes in situations where multiple hypotheses are being tested, such as in microarray experiments. In addition, investigators frequently do not have pilot data to estimate the sample sizes required for microarray studies. RESULTS: To address this challenge, we have developed a Microrarray PowerAtlas [1]. The atlas enables estimation of statistical power by allowing investigators to appropriately plan studies by building upon previous studies that have similar experimental characteristics. Currently, there are sample sizes and power estimates based on 632 experiments from Gene Expression Omnibus (GEO). The PowerAtlas also permits investigators to upload their own pilot data and derive power and sample size estimates from these data. This resource will be updated regularly with new datasets from GEO and other databases such as The Nottingham Arabidopsis Stock Center (NASC). CONCLUSION: This resource provides a valuable tool for investigators who are planning efficient microarray studies and estimating required sample sizes

HDBStat!: A platform-independent software suite for statistical analysis of high dimensional biology data

BACKGROUND: Many efforts in microarray data analysis are focused on providing tools and methods for the qualitative analysis of microarray data. HDBStat! (High-Dimensional Biology-Statistics) is a software package designed for analysis of high dimensional biology data such as microarray data. It was initially developed for the analysis of microarray gene expression data, but it can also be used for some applications in proteomics and other aspects of genomics. HDBStat! provides statisticians and biologists a flexible and easy-to-use interface to analyze complex microarray data using a variety of methods for data preprocessing, quality control analysis and hypothesis testing. RESULTS: Results generated from data preprocessing methods, quality control analysis and hypothesis testing methods are output in the form of Excel CSV tables, graphs and an Html report summarizing data analysis. CONCLUSION: HDBStat! is a platform-independent software that is freely available to academic institutions and non-profit organizations. It can be downloaded from our website

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<p><b>Copyright information:</b></p><p>Taken from "HDBStat!: A platform-independent software suite for statistical analysis of high dimensional biology data"</p><p>BMC Bioinformatics 2005;6():86-86.</p><p>Published online 6 Apr 2005</p><p>PMCID:PMC1087834.</p><p>Copyright © 2005 Trivedi et al; licensee BioMed Central Ltd.</p

HDBStat!: A platform-independent software suite for statistical analysis of high dimensional biology data-4

<p><b>Copyright information:</b></p><p>Taken from "HDBStat!: A platform-independent software suite for statistical analysis of high dimensional biology data"</p><p>BMC Bioinformatics 2005;6():86-86.</p><p>Published online 6 Apr 2005</p><p>PMCID:PMC1087834.</p><p>Copyright © 2005 Trivedi et al; licensee BioMed Central Ltd.</p