A novel pathway analysis approach based on the unexplained disregulation of genes.
A novel pathway analysis approach based on the unexplained disregulation of genes.
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DOI：
10.1109/jproc.2016.2531000
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发表时间：
2017-03
期刊：
影响因子：
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通讯作者：
Draghici S
中科院分区：
文献类型：
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作者：
Ansari S;Voichita C;Donato M;Tagett R;Draghici S
了解任何表型的关键一步是正确识别对该表型有显着影响的信号通路。然而，大多数当前的通路分析方法在某些情况下会产生假阳性和假阴性。我们假设这种不正确的结果是由于现有方法无法区分给定基因本身的主要失调和来自上游的信号传导的影响。此外，使用下一代技术进行的现代全基因组实验需要花费大量精力来测量基因组中 30,000-100,000 个转录本的整个集合。接下来是选择数百个差异表达基因，这一步骤实际上丢弃了超过 99% 的收集数据。我们还假设，如此彻底的过滤可能会丢弃许多在表型中发挥关键作用的基因。我们提出了一种新颖的基于拓扑的通路分析方法，该方法使用整组测量来识别受到显着影响的通路，从而允许充分利用 NGS 技术提供的数据。在涉及 12 种不同人类疾病的 24 个真实数据集以及 8 个酵母敲除数据集上获得的结果表明，所提出的方法相对于最先进的方法：SPIA、GSEA 和 GSA 产生了显着的改进。主要失调分析在 R 中实现，并包含在 ROntoTools Bioconductor 包（版本 ≥ 2.0.0）中。 https://www.bioconductor.org/packages/release/bioc/html/ROntoTools.html
A crucial step in the understanding of any phenotype is the correct identification of the signaling pathways that are significantly impacted in that phenotype. However, most current pathway analysis methods produce both false positives as well as false negatives in certain circumstances. We hypothesized that such incorrect results are due to the fact that the existing methods fail to distinguish between the primary dis-regulation of a given gene itself and the effects of signaling coming from upstream. Furthermore, a modern whole-genome experiment performed with a next-generation technology spends a great deal of effort to measure the entire set of 30,000–100,000 transcripts in the genome. This is followed by the selection of a few hundreds differentially expressed genes, step that literally discards more than 99% of the collected data. We also hypothesized that such a drastic filtering could discard many genes that play crucial roles in the phenotype. We propose a novel topology-based pathway analysis method that identifies significantly impacted pathways using the entire set of measurements, thus allowing the full use of the data provided by NGS techniques. The results obtained on 24 real data sets involving 12 different human diseases, as well as on 8 yeast knock-out data sets show that the proposed method yields significant improvements with respect to the state-of-the-art methods: SPIA, GSEA and GSA. Primary dis-regulation analysis is implemented in R and included in ROntoTools Bioconductor package (versions ≥ 2.0.0). https://www.bioconductor.org/packages/release/bioc/html/ROntoTools.html