基因表达谱数据多基因分析的分区解耦方法
Partition Decoupling for Multi-gene Analysis of Gene Expression Profiling Data
摘要：
本文将一种新的无监督统计学习方法--分区解耦方法推广应用到基因表达数据中。由于PDM具有揭示数据中存在的非线性和非凸几何形状的能力，因此PDM是对典型的基因表达分析算法的改进，允许多基因分析，即使当单个基因不表现出差异表达时，该多基因分析也可以揭示表型差异。在这里，我们将PDM应用于公开的基因表达数据集，并证明我们能够以比通过其他方法获得的更高的准确度识别细胞类型和治疗。通过应用它在一个途径的途径的方式，我们展示了如何PDM可用于找到一套机械相关的基因，歧视表型。
英文摘要：
We present the extention and application of a new unsupervised statistical learning technique--the Partition Decoupling Method--to gene expression data. Because it has the ability to reveal non-linear and non-convex geometries present in the data, the PDM is an improvement over typical gene expression analysis algorithms, permitting a multi-gene analysis that can reveal phenotypic differences even when the individual genes do not exhibit differential expression. Here, we apply the PDM to publicly-available gene expression data sets, and demonstrate that we are able to identify cell types and treatments with higher accuracy than is obtained through other approaches. By applying it in a pathway-by-pathway fashion, we demonstrate how the PDM may be used to find sets of mechanistically-related genes that discriminate phenotypes.
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