Tang Shaojun, Hemberg Martin, Cansizoglu Ertugrul, Belin Stephane, Kosik Kenneth, Kreiman Gabriel, Steen Hanno, Steen Judith
Departments of Pathology, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115, USA.
Department of Ophthalmology, Boston Children's Hospital, Boston, MA 02115, USA.
Nucleic Acids Res. 2016 Jun 2;44(10):e97. doi: 10.1093/nar/gkw157. Epub 2016 Mar 14.
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/.
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识别跨不同“组学”数据类型或包括时间进程在内的多维数据中的差异表达基因（DEG）。我们提出了fCI（f散度剔除指数），这是一种能够同时从连续和离散的转录组学、蛋白质组学以及整合的蛋白质基因组学数据中识别DEG的模型。我们表明，fCI可用于多种不同的数据集，并且能够明确找到显示功能调节、发育变化或调控异常的基因。将fCI应用于多个蛋白质基因组学数据集，我们鉴定出了一些显示出独特调控模式的重要基因。fCI软件包可在R Bioconductor和http://software.steenlab.org/fCI/获取。
