deGPS is a powerful tool for detecting differential expression in RNA-sequencing studies.
deGPS is a powerful tool for detecting differential expression in RNA-sequencing studies.
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deGPS 是检测 RNA 测序研究中差异表达的强大工具
DOI：
10.1186/s12864-015-1676-0
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发表时间：
2015-06-13
期刊：
影响因子：
4.4
通讯作者：
Lu Y
中科院分区：
文献类型：
--
作者：
Chu C;Fang Z;Hua X;Yang Y;Chen E;Cowley AW Jr;Liang M;Liu P;Lu Y
NGS技术的出现允许对全基因组转录组进行分析（即，RNA-Seq）以前所未有的速度和非常低的成本。与其他方法如微阵列相比，RNA-Seq提供了更精确的转录水平及其亚型测量。RNA-Seq的一个基本目标是更好地识别不同生物或疾病条件之间的表达变化。然而，从RNA-Seq计数数据检测差异表达的现有方法尚未在大规模RNA-Seq数据集中进行全面评估。它们中的许多遭受I型错误的膨胀和在控制错误发现率方面的失败，特别是在RNA-Seq实验中存在异常高的序列读取计数的情况下。为了应对这些挑战，我们提出了一个强大而强大的工具，称为deGPS，用于检测RNA-Seq数据中的差异表达。该框架包含基于广义泊松分布建模序列计数数据的新的归一化方法，然后是基于置换的差异表达测试。我们在几个大规模TCGA RNA-Seq项目的模拟数据集、compcodeR软件包的无偏基准数据和果蝇发育转录组的真实的RNA-Seq数据中系统地评估了我们的新工具。deGPS可以精确地控制用于检测差异表达的I型错误和错误发现率，并且在RNA-Seq实验中存在异常高序列读取计数的情况下是稳健的。实现我们的deGPS的软件在具有并行计算的R包中发布（https：//github.com/LL-LAB-MCW/deGPS）。deGPS是一个强大而强大的工具，用于数据归一化和检测RNA-Seq实验中的不同表达。除了RNA-Seq之外，deGPS还有潜力显著增强许多其他高通量平台（如ChIP-Seq、MBD-Seq和RIP-Seq）未来的数据分析工作。本文的在线版本（doi：10.1186/s12864-015-1676-0）包含补充材料，可供授权用户使用。
The advent of the NGS technologies has permitted profiling of whole-genome transcriptomes (i.e., RNA-Seq) at unprecedented speed and very low cost. RNA-Seq provides a far more precise measurement of transcript levels and their isoforms compared to other methods such as microarrays. A fundamental goal of RNA-Seq is to better identify expression changes between different biological or disease conditions. However, existing methods for detecting differential expression from RNA-Seq count data have not been comprehensively evaluated in large-scale RNA-Seq datasets. Many of them suffer from inflation of type I error and failure in controlling false discovery rate especially in the presence of abnormal high sequence read counts in RNA-Seq experiments. To address these challenges, we propose a powerful and robust tool, termed deGPS, for detecting differential expression in RNA-Seq data. This framework contains new normalization methods based on generalized Poisson distribution modeling sequence count data, followed by permutation-based differential expression tests. We systematically evaluated our new tool in simulated datasets from several large-scale TCGA RNA-Seq projects, unbiased benchmark data from compcodeR package, and real RNA-Seq data from the development transcriptome of Drosophila. deGPS can precisely control type I error and false discovery rate for the detection of differential expression and is robust in the presence of abnormal high sequence read counts in RNA-Seq experiments. Software implementing our deGPS was released within an R package with parallel computations (https://github.com/LL-LAB-MCW/deGPS). deGPS is a powerful and robust tool for data normalization and detecting different expression in RNA-Seq experiments. Beyond RNA-Seq, deGPS has the potential to significantly enhance future data analysis efforts from many other high-throughput platforms such as ChIP-Seq, MBD-Seq and RIP-Seq. The online version of this article (doi:10.1186/s12864-015-1676-0) contains supplementary material, which is available to authorized users.
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影响因子：
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作者：
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通讯作者：
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作者：
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DOI：
10.1515/1544-6115.1826
发表时间：
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影响因子：
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DOI：
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