f-divergence cutoff index to simultaneously identify differential expression in the integrated transcriptome and proteome.
f-divergence cutoff index to simultaneously identify differential expression in the integrated transcriptome and proteome.
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DOI：
10.1093/nar/gkw157
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发表时间：
2016-06-02
影响因子：
14.9
通讯作者：
Steen J
中科院分区：
文献类型：
--
作者：
Tang S;Hemberg M;Cansizoglu E;Belin S;Kosik K;Kreiman G;Steen H;Steen J
整合“组学”（即转录组学和蛋白质组学）的能力对于理解调控机制变得越来越重要。目前还没有工具可用于在不同的“组学”数据类型或包括时间过程的多维数据中识别差异表达基因（DEG）。我们提出了fCI（f-分歧切出指数），一个模型，能够同时识别DEG从连续和离散的转录组，蛋白质组和整合的蛋白基因组数据。我们表明，fCI可以用于多个不同的数据集，并可以明确地找到显示功能调节，发育变化或失调的基因。将fCI应用于几个蛋白质基因组学数据集，我们确定了一些重要的基因，显示出独特的调控模式。软件包fCI可在R Bioconductor和http://software.steenlab.org/fCI/上获得。
The ability to integrate ‘omics’ (i.e. transcriptomics and proteomics) is becoming increasingly important to the understanding of regulatory mechanisms. There are currently no tools available to identify differentially expressed genes (DEGs) across different ‘omics’ data types or multi-dimensional data including time courses. We present fCI (f-divergence Cut-out Index), a model capable of simultaneously identifying DEGs from continuous and discrete transcriptomic, proteomic and integrated proteogenomic data. We show that fCI can be used across multiple diverse sets of data and can unambiguously find genes that show functional modulation, developmental changes or misregulation. Applying fCI to several proteogenomics datasets, we identified a number of important genes that showed distinctive regulation patterns. The package fCI is available at R Bioconductor and http://software.steenlab.org/fCI/.