A new method for constructing tumor specific gene co-expression networks based on samples with tumor purity heterogeneity.
A new method for constructing tumor specific gene co-expression networks based on samples with tumor purity heterogeneity.
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DOI：
10.1093/bioinformatics/bty280
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发表时间：
2018-07-01
期刊：
影响因子：
--
通讯作者：
Wang P
中科院分区：
文献类型：
--
作者：
Petralia F;Wang L;Peng J;Yan A;Zhu J;Wang P
肿瘤组织样品通常含有未知部分的基质细胞。肿瘤纯度异质性（TPH）是近年来组学研究中的一个重要问题。具体而言，如果在推断共表达网络时忽略TPH，则可能在非肿瘤细胞和肿瘤细胞之间的基因之间估计边缘，而不是在肿瘤细胞中相互作用的基因对之间估计边缘。为了解决这个问题，我们提出了肿瘤特异性网络（TSNet），一种新的方法，构建肿瘤细胞特异性基因/蛋白质共表达网络的基础上，基因/蛋白质表达谱的肿瘤组织。TSNet将观察到的表达谱视为来自不同细胞类型的表达的混合物，并明确建模每个肿瘤样品中的肿瘤纯度百分比。使用广泛的合成数据实验，我们证明了TSNet优于一个标准的图形模型，不考虑TPH。然后，我们应用TSNet基于TCGA卵巢癌RNAseq数据来估计肿瘤特异性基因共表达网络。我们确定了新的共表达模块和枢纽结构特异性肿瘤细胞。R代码可以在https://github.com/petraf01/TSNet上找到。 补充数据可在Bioinformatics在线获得。
Tumor tissue samples often contain an unknown fraction of stromal cells. This problem is widely known as tumor purity heterogeneity (TPH) was recently recognized as a severe issue in omics studies. Specifically, if TPH is ignored when inferring co-expression networks, edges are likely to be estimated among genes with mean shift between non-tumor- and tumor cells rather than among gene pairs interacting with each other in tumor cells. To address this issue, we propose Tumor Specific Net (TSNet), a new method which constructs tumor-cell specific gene/protein co-expression networks based on gene/protein expression profiles of tumor tissues. TSNet treats the observed expression profile as a mixture of expressions from different cell types and explicitly models tumor purity percentage in each tumor sample. Using extensive synthetic data experiments, we demonstrate that TSNet outperforms a standard graphical model which does not account for TPH. We then apply TSNet to estimate tumor specific gene co-expression networks based on TCGA ovarian cancer RNAseq data. We identify novel co-expression modules and hub structure specific to tumor cells. R codes can be found at https://github.com/petraf01/TSNet. Supplementary data are available at Bioinformatics online.
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