An assessment of recently published gene expression data analyses: reporting experimental design and statistical factors.
An assessment of recently published gene expression data analyses: reporting experimental design and statistical factors.
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DOI：
10.1186/1472-6947-6-27
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发表时间：
2006-06-21
影响因子：
3.5
通讯作者：
Azuaje, Francisco
中科院分区：
文献类型：
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作者：
Jafari, Peyman;Azuaje, Francisco
背景：大规模基因表达数据的分析是功能基因组学和识别潜在药物靶点的基本途径。除非经过充分的设计和报告，否则这类研究得出的结果是不可信的。方法：我们回顾了2003年至2005年发表的数百篇MEDLINE收录的涉及基因表达数据分析的论文。根据样本量、统计能力和软件可获得性等因素对这些文献进行检验。结果：在检验的文献中，293篇文献由应用和新方法组成。这些论文没有报道样本大小和统计能量估计的方法。分别有57篇(37.5%)和104篇(68.4%)的方法学论文没有对数据转换和数据分析(例如分类)之前应用的归一化技术进行明确说明。在介绍生物医学相关应用的论文中，41篇(29.1%)没有报告数据归一化，83篇(58.9%)没有描述应用的归一化技术。基于聚类的分析、t检验和方差分析是微阵列数据分析中应用最广泛的技术。但值得注意的是，只有5篇(3.5%)的申请论文涉及t检验和ANOVA应用中方差齐性假设的陈述或参考。仍然有必要促进对应用的软件包或其可用性的报告。结论：最近发表的基因表达数据分析研究可能缺乏正确评估其设计质量和潜在影响所需的关键信息。需要更严格地报告重要的实验因素，如统计能力和样本量，以及对所应用的统计方法的正确说明和理由。这份文件强调了确定报告表达数据的统计设计和分析所需的最低信息集的重要性。通过改进统计分析报告的做法，科学界可以促进质量保证和同行审查进程，以及结果的重现性。
BACKGROUND: The analysis of large-scale gene expression data is a fundamental approach to functional genomics and the identification of potential drug targets. Results derived from such studies cannot be trusted unless they are adequately designed and reported. The purpose of this study is to assess current practices on the reporting of experimental design and statistical analyses in gene expression-based studies.METHODS: We reviewed hundreds of MEDLINE-indexed papers involving gene expression data analysis, which were published between 2003 and 2005. These papers were examined on the basis of their reporting of several factors, such as sample size, statistical power and software availability.RESULTS: Among the examined papers, we concentrated on 293 papers consisting of applications and new methodologies. These papers did not report approaches to sample size and statistical power estimation. Explicit statements on data transformation and descriptions of the normalisation techniques applied prior to data analyses (e.g. classification) were not reported in 57 (37.5%) and 104 (68.4%) of the methodology papers respectively. With regard to papers presenting biomedical-relevant applications, 41(29.1 %) of these papers did not report on data normalisation and 83 (58.9%) did not describe the normalisation technique applied. Clustering-based analysis, the t-test and ANOVA represent the most widely applied techniques in microarray data analysis. But remarkably, only 5 (3.5%) of the application papers included statements or references to assumption about variance homogeneity for the application of the t-test and ANOVA. There is still a need to promote the reporting of software packages applied or their availability.CONCLUSION: Recently-published gene expression data analysis studies may lack key information required for properly assessing their design quality and potential impact. There is a need for more rigorous reporting of important experimental factors such as statistical power and sample size, as well as the correct description and justification of statistical methods applied. This paper highlights the importance of defining a minimum set of information required for reporting on statistical design and analysis of expression data. By improving practices of statistical analysis reporting, the scientific community can facilitate quality assurance and peer-review processes, as well as the reproducibility of results.