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一种全面且通用的差异基因表达分析性能评估方法。

A comprehensive and universal method for assessing the performance of differential gene expression analyses.

机构信息

Department of Arthritis and Immunology, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, United States of America.

出版信息

PLoS One. 2010 Sep 9;5(9):e12657. doi: 10.1371/journal.pone.0012657.

DOI:10.1371/journal.pone.0012657
PMID:20844739
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC2936572/
Abstract

The number of methods for pre-processing and analysis of gene expression data continues to increase, often making it difficult to select the most appropriate approach. We present a simple procedure for comparative estimation of a variety of methods for microarray data pre-processing and analysis. Our approach is based on the use of real microarray data in which controlled fold changes are introduced into 20% of the data to provide a metric for comparison with the unmodified data. The data modifications can be easily applied to raw data measured with any technological platform and retains all the complex structures and statistical characteristics of the real-world data. The power of the method is illustrated by its application to the quantitative comparison of different methods of normalization and analysis of microarray data. Our results demonstrate that the method of controlled modifications of real experimental data provides a simple tool for assessing the performance of data preprocessing and analysis methods.

摘要

用于基因表达数据预处理和分析的方法数量不断增加，这使得选择最合适的方法变得困难。我们提出了一种简单的程序，用于比较评估各种微阵列数据预处理和分析方法。我们的方法基于使用真实的微阵列数据，其中将受控的倍数变化引入到数据的 20%中，以便与未修改的数据进行比较。可以轻松地将数据修改应用于使用任何技术平台测量的原始数据，并保留真实世界数据的所有复杂结构和统计特征。该方法的强大功能通过其在定量比较不同的微阵列数据归一化和分析方法中的应用得到了说明。我们的结果表明，真实实验数据的受控修改方法为评估数据预处理和分析方法的性能提供了一种简单的工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b6d/2936572/5e48c2090e52/pone.0012657.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b6d/2936572/5ec9a692c465/pone.0012657.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b6d/2936572/90fddb0157fe/pone.0012657.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b6d/2936572/42990c5a1895/pone.0012657.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b6d/2936572/cd0fb419e829/pone.0012657.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b6d/2936572/926975d783ad/pone.0012657.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b6d/2936572/5e48c2090e52/pone.0012657.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b6d/2936572/5ec9a692c465/pone.0012657.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b6d/2936572/90fddb0157fe/pone.0012657.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b6d/2936572/42990c5a1895/pone.0012657.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b6d/2936572/cd0fb419e829/pone.0012657.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b6d/2936572/926975d783ad/pone.0012657.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b6d/2936572/5e48c2090e52/pone.0012657.g006.jpg

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