Canonical correlation analysis for RNA-seq co-expression networks.
Canonical correlation analysis for RNA-seq co-expression networks.
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DOI：
10.1093/nar/gkt145
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发表时间：
2013-04
影响因子：
14.9
通讯作者：
Xiong M
中科院分区：
文献类型：
--
作者：
Hong S;Chen X;Jin L;Xiong M
下一代测序的数字转录组分析发现了大量的mRNA变异。基因表达的变异是许多生物学过程的基础，是揭示常见疾病机制的关键。然而，目前使用整体基因表达构建共表达网络的方法最初是为微阵列表达数据而设计的，它们忽略了基因表达的大量变化。为了利用外显子、基因组位置水平和等位基因特异性表达的信息，我们开发了新的基于组分的方法，单变量和双变量典型相关分析，用于构建带有RNA-seq数据的共表达网络。为了评估我们的共表达网络推断方法与RNA-seq数据的性能，我们将它们应用于TCGA数据库中的肺鳞状细胞癌表达数据以及我们的双相情感障碍和精神分裂症RNA-seq研究。初步结果表明，通过典型相关分析和RNA-seq数据构建的共表达网络为深入了解生物过程和疾病机制提供了丰富的遗传和分子信息。我们的新方法大大优于目前基于整体基因表达水平的微阵列表达数据或RNA-seq数据构建共表达网络的统计方法。
Digital transcriptome analysis by next-generation sequencing discovers substantial mRNA variants. Variation in gene expression underlies many biological processes and holds a key to unravelling mechanism of common diseases. However, the current methods for construction of co-expression networks using overall gene expression are originally designed for microarray expression data, and they overlook a large number of variations in gene expressions. To use information on exon, genomic positional level and allele-specific expressions, we develop novel component-based methods, single and bivariate canonical correlation analysis, for construction of co-expression networks with RNA-seq data. To evaluate the performance of our methods for co-expression network inference with RNA-seq data, they are applied to lung squamous cell cancer expression data from TCGA database and our bipolar disorder and schizophrenia RNA-seq study. The preliminary results demonstrate that the co-expression networks constructed by canonical correlation analysis and RNA-seq data provide rich genetic and molecular information to gain insight into biological processes and disease mechanism. Our new methods substantially outperform the current statistical methods for co-expression network construction with microarray expression data or RNA-seq data based on overall gene expression levels.
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影响因子：
14.9
作者：
Kanehisa M;Goto S;Sato Y;Furumichi M;Tanabe M
通讯作者：
Tanabe M
DOI：
10.1093/bioinformatics/btn653
发表时间：
2009-03-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Chelala C;Khan A;Lemoine NR
通讯作者：
Lemoine NR
影响因子：
64.8
作者：
Duff, K;Eckman, C;Younkin, S
通讯作者：
Younkin, S
影响因子：
11
作者：
Devon, RS;Anderson, S;Porteous, DJ
通讯作者：
Porteous, DJ
影响因子：
14.9
作者：
UniProt Consortium
通讯作者：
UniProt Consortium