Identifying disease-associated pathways in one-phenotype data based on reversal gene expression orderings.
Identifying disease-associated pathways in one-phenotype data based on reversal gene expression orderings.
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基于逆转基因表达顺序识别单表型数据中的疾病相关途径
DOI：
10.1038/s41598-017-01536-3
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发表时间：
2017-05-02
影响因子：
4.6
通讯作者：
Guo Z
中科院分区：
文献类型：
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作者：
Hong G;Li H;Zhang J;Guan Q;Chen R;Guo Z
由于组织活检的侵入性，研究人员通常无法收集足够的正常对照与患病样本进行比较。我们开发了一种途径富集工具DRFunc，通过结合其他实验的正常对照来检测显著疾病中断的途径。该方法使用不同癌症的微阵列和RNA-seq表达数据进行了验证。从不同的独立数据集中，在病例和对照之间鉴定出高度一致的差异排序（DR）基因对。DR基因对用于DRFuncalximm，通过结合其他研究的对照，检测一种表型表达数据中显著中断的途径。DRFuncaltism通过检测胶质母细胞瘤样品中的重要途径来举例说明。该算法还可以用于检测具有弱表达信号的数据集中的改变的通路，如对化疗治疗的乳腺癌样品的表达数据的分析所示。
Due to the invasiveness nature of tissue biopsy, it is common that investigators cannot collect sufficient normal controls for comparison with diseased samples. We developed a pathway enrichment tool,DRFunc, to detect significantly disease-disrupted pathways by incorporating normal controls from other experiments. The method was validated using both microarray and RNA-seq expression data for different cancers. The high concordant differentially ranked (DR) gene pairs were identified between cases and controls from different independent datasets. The DR gene pairs were used in theDRFuncalgorithm to detect significantly disrupted pathways in one-phenotype expression data by combing controls from other studies. TheDRFuncalgorithm was exemplified by the detection of significant pathways in glioblastoma samples. The algorithm can also be used to detect altered pathways in the datasets with weak expression signals, as shown by the analysis on the expression data of chemotherapy-treated breast cancer samples.