Novianti Putri W, van der Tweel Ingeborg, Jong Victor L, Roes Kit Cb, Eijkemans Marinus Jc
Biostatistics and Research Support, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.
Biostatistics and Research Support, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands ; Department of Viroscience, Erasmus Medical Center Rotterdam, Rotterdam, The Netherlands.
Cancer Inform. 2015 Sep 10;14(Suppl 5):1-10. doi: 10.4137/CIN.S27718. eCollection 2015.
Most of the discoveries from gene expression data are driven by a study claiming an optimal subset of genes that play a key role in a specific disease. Meta-analysis of the available datasets can help in getting concordant results so that a real-life application may be more successful. Sequential meta-analysis (SMA) is an approach for combining studies in chronological order while preserving the type I error and pre-specifying the statistical power to detect a given effect size. We focus on the application of SMA to find gene expression signatures across experiments in acute myeloid leukemia. SMA of seven raw datasets is used to evaluate whether the accumulated samples show enough evidence or more experiments should be initiated. We found 313 differentially expressed genes, based on the cumulative information of the experiments. SMA offers an alternative to existing methods in generating a gene list by evaluating the adequacy of the cumulative information.
基因表达数据的大多数发现是由一项研究推动的，该研究声称存在一组在特定疾病中起关键作用的最优基因子集。对现有数据集进行荟萃分析有助于获得一致的结果，从而使实际应用可能更成功。序贯荟萃分析（SMA）是一种按时间顺序合并研究的方法，同时保持I型错误并预先指定检测给定效应大小的统计功效。我们专注于将SMA应用于在急性髓系白血病的实验中寻找基因表达特征。对七个原始数据集进行SMA，以评估累积样本是否显示出足够的证据，或者是否应该启动更多实验。基于实验的累积信息，我们发现了313个差异表达基因。SMA通过评估累积信息的充分性，为生成基因列表的现有方法提供了一种替代方案。
