Extracting a low-dimensional description of multiple gene expression datasets reveals a potential driver for tumor-associated stroma in ovarian cancer.
Extracting a low-dimensional description of multiple gene expression datasets reveals a potential driver for tumor-associated stroma in ovarian cancer.
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DOI：
10.1186/s13073-016-0319-7
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发表时间：
2016-06-10
期刊：
影响因子：
12.3
通讯作者：
Lee SI
中科院分区：
文献类型：
--
作者：
Celik S;Logsdon BA;Battle S;Drescher CW;Rendi M;Hawkins RD;Lee SI
在多个独立的疾病研究中保守的表达数据模式可能代表疾病基础的重要分子事件。我们提出的INSPIRE方法来推断模块的共表达基因和模块之间的依赖关系，从多个表达数据集，可能包含不同的基因集。我们表明，INSPIRE推断更准确的模型比现有的方法来提取表达数据的低维表示。我们证明，将INSPIRE应用于9个卵巢癌数据集，可以产生一种新的肿瘤相关基质HOPX标记物和潜在驱动因素，然后进行实验验证。INSPIRE的实施情况见http://inspire.cs.washington.edu。本文的在线版本（doi：10.1186/s13073-016-0319-7）包含补充材料，可供授权用户使用。
Patterns in expression data conserved across multiple independent disease studies are likely to represent important molecular events underlying the disease. We present the INSPIRE method to infer modules of co-expressed genes and the dependencies among the modules from multiple expression datasets that may contain different sets of genes. We show that INSPIRE infers more accurate models than existing methods to extract low-dimensional representation of expression data. We demonstrate that applying INSPIRE to nine ovarian cancer datasets leads to a new marker and potential driver of tumor-associated stroma, HOPX, followed by experimental validation. The implementation of INSPIRE is available at http://inspire.cs.washington.edu. The online version of this article (doi:10.1186/s13073-016-0319-7) contains supplementary material, which is available to authorized users.