TCC: an R package for comparing tag count data with robust normalization strategies.
TCC: an R package for comparing tag count data with robust normalization strategies.
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DOI：
10.1186/1471-2105-14-219
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发表时间：
2013-07-09
影响因子：
3
通讯作者：
Kadota K
中科院分区：
文献类型：
--
作者：
Sun J;Nishiyama T;Shimizu K;Kadota K
基于下一代测序技术的差异表达分析是研究RNA表达的基本手段。我们最近开发了一种用于两组重复RNA-Seq数据的多步归一化方法(称为TBT)，并证明了四个R包(Edger、DESeq、baySeq和NBPSeq)中的统计方法与TBT一起可以产生一个很好的排序基因列表，其中真正差异表达基因(DEG)排名最高，非DEG排名最低。然而，目前的TBT方法的优点是以巨大的计算时间为代价的。此外，R包没有基于这种多步骤策略的归一化方法。TCC(标签计数比较的首字母缩写)是一个R包，它提供了一系列用于标签计数数据差异表达分析的函数。该程序包结合了多步归一化方法，其策略是在执行数据归一化之前删除潜在的DEG。基于这种DEG消除策略的归一化函数(DEGES)包括(I)原始的基于DEGES的TBT方法，用于有或没有重复的两组数据，(Ii)对于有或没有重复的两组数据的更快的方法，以及(Iii)用于多组比较的方法。TCC提供了一个简单的统一界面，通过Edger、DESeq和baySeq提供的功能组合来执行此类分析。此外，还提供了用于在各种条件下生成模拟数据的函数和由现有程序包中的函数组成的可选的DEGES过程。生物信息学科学家可以使用TCC来评估他们的方法，熟悉其他R包的生物学家可以很容易地了解TCC中的工作。TCC中的DEGES对于标签计数数据的准确标准化是必不可少的，特别是当其中一个样本中上调和下调的DEG在其数量上存在极大偏差时。TCC对于分析从无偏向到极度偏向的差异表达的各种场景中的标签计数数据很有用。Tcc可在http://www.iu.a.u-tokyo.ac.jp/~kadota/TCC/上获得，并将出现在BioConductor(来自版本的http://bioconductor.org/))中。2.13.
Differential expression analysis based on “next-generation” sequencing technologies is a fundamental means of studying RNA expression. We recently developed a multi-step normalization method (called TbT) for two-group RNA-seq data with replicates and demonstrated that the statistical methods available in four R packages (edgeR, DESeq, baySeq, and NBPSeq) together with TbT can produce a well-ranked gene list in which true differentially expressed genes (DEGs) are top-ranked and non-DEGs are bottom ranked. However, the advantages of the current TbT method come at the cost of a huge computation time. Moreover, the R packages did not have normalization methods based on such a multi-step strategy. TCC (an acronym for Tag Count Comparison) is an R package that provides a series of functions for differential expression analysis of tag count data. The package incorporates multi-step normalization methods, whose strategy is to remove potential DEGs before performing the data normalization. The normalization function based on this DEG elimination strategy (DEGES) includes (i) the original TbT method based on DEGES for two-group data with or without replicates, (ii) much faster methods for two-group data with or without replicates, and (iii) methods for multi-group comparison. TCC provides a simple unified interface to perform such analyses with combinations of functions provided by edgeR, DESeq, and baySeq. Additionally, a function for generating simulation data under various conditions and alternative DEGES procedures consisting of functions in the existing packages are provided. Bioinformatics scientists can use TCC to evaluate their methods, and biologists familiar with other R packages can easily learn what is done in TCC. DEGES in TCC is essential for accurate normalization of tag count data, especially when up- and down-regulated DEGs in one of the samples are extremely biased in their number. TCC is useful for analyzing tag count data in various scenarios ranging from unbiased to extremely biased differential expression. TCC is available at http://www.iu.a.u-tokyo.ac.jp/~kadota/TCC/ and will appear in Bioconductor (http://bioconductor.org/) from ver. 2.13.
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