Gene length corrected trimmed mean of M-values (GeTMM) processing of RNA-seq data performs similarly in intersample analyses while improving intrasample comparisons.
Gene length corrected trimmed mean of M-values (GeTMM) processing of RNA-seq data performs similarly in intersample analyses while improving intrasample comparisons.
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DOI：
10.1186/s12859-018-2246-7
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发表时间：
2018-06-22
影响因子：
3
通讯作者：
Sieuwerts AM
中科院分区：
文献类型：
--
作者：
Smid M;Coebergh van den Braak RRJ;van de Werken HJG;van Riet J;van Galen A;de Weerd V;van der Vlugt-Daane M;Bril SI;Lalmahomed ZS;Kloosterman WP;Wilting SM;Foekens JA;IJzermans JNM;MATCH study group;Martens JWM;Sieuwerts AM
目前RNA测序数据的标准化方法允许样本间比较以鉴定差异表达（DE）基因或样本内比较以发现和验证基因签名。大多数关于规范化方法优化的研究通常使用模拟数据来验证方法。我们描述了一种新的方法，GeTMM，它允许间和样本内分析相同的归一化数据集。我们用的是（即未模拟）来自263例结肠癌的RNA-seq数据（无生物学重复），并使用相同的读段计数数据将GeTMM与最常用的标准化方法进行比较（即TMM（由edgeR使用）、RLE（由DESeq 2使用）和TPM）关于分布、RNA质量的影响、亚型分类、复发评分，DE基因的回忆以及与RT-qPCR数据的相关性。我们观察到GeTMM和TPM在样本内比较方面有明显的优势，而GeTMM在样本间比较中的表现与TMM和RLE标准化数据相似。关于DE基因，召回之间的归一化方法，而GeTMM显示最低数量的假阳性DE基因。值得注意的是，我们在低RNA质量的样品中观察到有限的有害影响。我们表明，GeTMM优于既定的方法，样本内比较，同时执行等效的样本间归一化使用相同的归一化数据。这些组合特性增强了RNA-seq的一般实用性，但也增强了与公共领域中许多基于阵列的基因表达数据的可比性。本文的在线版本（10.1186/s12859-018-2246-7）包含补充材料，可供授权用户使用。
Current normalization methods for RNA-sequencing data allow either for intersample comparison to identify differentially expressed (DE) genes or for intrasample comparison for the discovery and validation of gene signatures. Most studies on optimization of normalization methods typically use simulated data to validate methodologies. We describe a new method, GeTMM, which allows for both inter- and intrasample analyses with the same normalized data set. We used actual (i.e. not simulated) RNA-seq data from 263 colon cancers (no biological replicates) and used the same read count data to compare GeTMM with the most commonly used normalization methods (i.e. TMM (used by edgeR), RLE (used by DESeq2) and TPM) with respect to distributions, effect of RNA quality, subtype-classification, recurrence score, recall of DE genes and correlation to RT-qPCR data. We observed a clear benefit for GeTMM and TPM with regard to intrasample comparison while GeTMM performed similar to TMM and RLE normalized data in intersample comparisons. Regarding DE genes, recall was found comparable among the normalization methods, while GeTMM showed the lowest number of false-positive DE genes. Remarkably, we observed limited detrimental effects in samples with low RNA quality. We show that GeTMM outperforms established methods with regard to intrasample comparison while performing equivalent with regard to intersample normalization using the same normalized data. These combined properties enhance the general usefulness of RNA-seq but also the comparability to the many array-based gene expression data in the public domain. The online version of this article (10.1186/s12859-018-2246-7) contains supplementary material, which is available to authorized users.
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影响因子：
46.9
作者：
Trapnell C;Williams BA;Pertea G;Mortazavi A;Kwan G;van Baren MJ;Salzberg SL;Wold BJ;Pachter L
通讯作者：
Pachter L
影响因子：
3
作者：
Li P;Piao Y;Shon HS;Ryu KH
通讯作者：
Ryu KH
影响因子：
12.3
作者：
Robinson MD;Oshlack A
通讯作者：
Oshlack A
影响因子：
3.8
作者：
Clark-Langone KM;Sangli C;Krishnakumar J;Watson D
通讯作者：
Watson D
DOI：
10.1093/bioinformatics/btp692
发表时间：
2010-02-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Li B;Ruotti V;Stewart RM;Thomson JA;Dewey CN
通讯作者：
Dewey CN