梦境：重复测量设计的强大差异表达分析。
Dream: powerful differential expression analysis for repeated measures designs.
机构信息
Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
出版信息
Bioinformatics. 2021 Apr 19;37(2):192-201. doi: 10.1093/bioinformatics/btaa687.
SUMMARY
Large-scale transcriptome studies with multiple samples per individual are widely used to study disease biology. Yet, current methods for differential expression are inadequate for cross-individual testing for these repeated measures designs. Most problematic, we observe across multiple datasets that current methods can give reproducible false-positive findings that are driven by genetic regulation of gene expression, yet are unrelated to the trait of interest. Here, we introduce a statistical software package, dream, that increases power, controls the false positive rate, enables multiple types of hypothesis tests, and integrates with standard workflows. In 12 analyses in 6 independent datasets, dream yields biological insight not found with existing software while addressing the issue of reproducible false-positive findings.
AVAILABILITY AND IMPLEMENTATION
Dream is available within the variancePartition Bioconductor package at http://bioconductor.org/packages/variancePartition.
CONTACT
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
摘要
利用每个个体的多个样本进行大规模转录组研究被广泛用于研究疾病生物学。然而，目前用于个体间差异表达的方法对于这些重复测量设计的交叉测试并不充分。最成问题的是，我们观察到多个数据集，当前的方法可以产生可重复的假阳性发现，这些发现是由基因表达的遗传调控驱动的，但与感兴趣的特征无关。在这里，我们介绍了一个统计软件包 dream，它可以提高功效，控制假阳性率，支持多种类型的假设检验，并与标准工作流程集成。在 6 个独立数据集的 12 个分析中，dream 产生了与现有软件不同的生物学见解，同时解决了可重复的假阳性发现的问题。
可用性和实现
dream 可在 http://bioconductor.org/packages/variancePartition 中的 variancePartition Bioconductor 包内使用。
联系方式
补充信息
补充数据可在 Bioinformatics 在线获取。
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