The characteristic direction: a geometrical approach to identify differentially expressed genes.
The characteristic direction: a geometrical approach to identify differentially expressed genes.
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DOI：
10.1186/1471-2105-15-79
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发表时间：
2014-03-21
影响因子：
3
通讯作者：
Ma'ayan A
中科院分区：
文献类型：
--
作者：
Clark NR;Hu KS;Feldmann AS;Kou Y;Chen EY;Duan Q;Ma'ayan A
鉴别差异表达基因（DEG）是进行全基因组表达谱研究的基本步骤。通常，通过单变量方法识别DEG，例如处理cDNA微阵列的微阵列显著性分析（SAM）或微阵列数据线性模型（LIMMA），以及基于负二项分布的差异基因表达分析（DESeq）或用于RNA-seq分析的R中数字基因表达数据的实证分析（edgeR）。在这里，我们提出了一种新的几何多元方法来识别DEG称为特征方向。我们通过大量的微阵列实验证明，在转录因子（TF）和药物扰动响应的背景下，特征方向方法比现有的识别DEG的方法明显更敏感。我们还使用合成数据和RNA-Seq数据对特征方向方法进行了基准测试。从Gene expression Omnibus （GEO）中提取的大量来自TF扰动（73个实验）和药物扰动（130个实验）的微阵列表达数据，以及描述两种弥漫性大b细胞淋巴瘤亚型全基因组基因表达和STAT3 DNA结合的RNA-Seq研究，用于使用真实数据对该方法进行基准测试。识别受干扰tf的DNA结合位点的ChIP-Seq数据，以及已知的干扰药物的药物靶点，被用作验证的先验知识银标准。在所有情况下，特征方向DEG调用方法都优于其他方法。我们发现，当药物在不同的环境下作用于细胞时，与药物靶点相互作用的蛋白质是差异表达的，并且通过特征方向方法发现了更多相应的基因。此外，我们还表明，与基因集富集分析（GSEA）和超几何测试相比，特征方向概念化可以用于进行改进的基因集富集分析。特征方向方法的应用可能会揭示当前最先进的DEG方法尚未发现的相关生物学机制。该方法可以通过使用四种流行编程语言的各种开源代码实现免费访问：R， Python， MATLAB和Mathematica，所有这些都可以在：http://www.maayanlab.net/CD上获得。
Identifying differentially expressed genes (DEG) is a fundamental step in studies that perform genome wide expression profiling. Typically, DEG are identified by univariate approaches such as Significance Analysis of Microarrays (SAM) or Linear Models for Microarray Data (LIMMA) for processing cDNA microarrays, and differential gene expression analysis based on the negative binomial distribution (DESeq) or Empirical analysis of Digital Gene Expression data in R (edgeR) for RNA-seq profiling. Here we present a new geometrical multivariate approach to identify DEG called the Characteristic Direction. We demonstrate that the Characteristic Direction method is significantly more sensitive than existing methods for identifying DEG in the context of transcription factor (TF) and drug perturbation responses over a large number of microarray experiments. We also benchmarked the Characteristic Direction method using synthetic data, as well as RNA-Seq data. A large collection of microarray expression data from TF perturbations (73 experiments) and drug perturbations (130 experiments) extracted from the Gene Expression Omnibus (GEO), as well as an RNA-Seq study that profiled genome-wide gene expression and STAT3 DNA binding in two subtypes of diffuse large B-cell Lymphoma, were used for benchmarking the method using real data. ChIP-Seq data identifying DNA binding sites of the perturbed TFs, as well as known drug targets of the perturbing drugs, were used as prior knowledge silver-standard for validation. In all cases the Characteristic Direction DEG calling method outperformed other methods. We find that when drugs are applied to cells in various contexts, the proteins that interact with the drug-targets are differentially expressed and more of the corresponding genes are discovered by the Characteristic Direction method. In addition, we show that the Characteristic Direction conceptualization can be used to perform improved gene set enrichment analyses when compared with the gene-set enrichment analysis (GSEA) and the hypergeometric test. The application of the Characteristic Direction method may shed new light on relevant biological mechanisms that would have remained undiscovered by the current state-of-the-art DEG methods. The method is freely accessible via various open source code implementations using four popular programming languages: R, Python, MATLAB and Mathematica, all available at: http://www.maayanlab.net/CD.
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