DataRemix: a universal data transformation for optimal inference from gene expression datasets.
DataRemix: a universal data transformation for optimal inference from gene expression datasets.
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DataRemix：一种通用数据转换，用于从基因表达数据集中进行最佳推理。
DOI：
10.1093/bioinformatics/btaa745
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Chikina,Maria
中科院分区：
文献类型：
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作者：
Mao,Weiguang;Rahimikollu,Javad;Hausler,Ryan;Chikina,Maria
MotivationRNA-seq技术在转录丰度评估方面提供了前所未有的能力，可用于执行各种下游任务，如基因相关网络推断和eQTL发现。然而，由于有害的生物变异和技术协变量，原始基因表达值必须进行归一化，而不同的归一化策略可能导致下游研究中显著不同的结果。结果对基于奇异值分解的重构进行了推广，其中常用的白化、秩近似和去除顶主成分是特例。我们简单的三参数转换DataRemix，可以调整以重新衡量隐藏因素的贡献，并揭示其他隐藏的生物信号。特别是，我们证明了该方法可以有效地优先考虑生物信号而不是噪声，而无需利用外部数据集特定知识，并且可以优于明确使用已知技术因素的归一化方法。我们还表明，DataRemix可以通过汤普森采样方法有效地优化，这使得它适用于计算成本高的目标，如eQTL分析。最后，我们将我们的方法应用于宗教秩序研究和记忆与衰老项目数据集，并报告了我们所知的第一个可复制的人类大脑trans- eqtl效应。可用性和实现dataremix是一个R包，可以在GitHub上免费获得（https://github.com/wgmao/DataRemix）.Supplementary information)补充数据可以在bioinformatics online上获得。
MotivationRNA-seq technology provides unprecedented power in the assessment of the transcription abundance and can be used to perform a variety of downstream tasks such as inference of gene-correlation network and eQTL discovery. However, raw gene expression values have to be normalized for nuisance biological variation and technical covariates, and different normalization strategies can lead to dramatically different results in the downstream study.ResultsWe describe a generalization of singular value decomposition-based reconstruction for which the common techniques of whitening, rank-kapproximation and removing the topkprincipal components are special cases. Our simple three-parameter transformation, DataRemix, can be tuned to reweigh the contribution of hidden factors and reveal otherwise hidden biological signals. In particular, we demonstrate that the method can effectively prioritize biological signals over noise without leveraging external dataset-specific knowledge, and can outperform normalization methods that make explicit use of known technical factors. We also show that DataRemix can be efficiently optimized via Thompson sampling approach, which makes it feasible for computationally expensive objectives such as eQTL analysis. Finally, we apply our method to the Religious Orders Study and Memory and Aging Project dataset, and we report what to our knowledge is the first replicabletrans-eQTL effect in human brain.Availabilityand implementationDataRemix is an R package which is freely available at GitHub (https://github.com/wgmao/DataRemix).Supplementary informationSupplementary data are available atBioinformaticsonline.