Multiple functional linear model for association analysis of RNA-seq with imaging.
Multiple functional linear model for association analysis of RNA-seq with imaging.
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DOI：
10.1007/s40484-015-0048-8
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发表时间：
2015-06
期刊：
影响因子：
--
通讯作者：
Xiong M
中科院分区：
文献类型：
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作者：
Jiang J;Lin N;Guo S;Chen J;Xiong M
新兴的基因组和解剖成像数据的综合分析尚未得到很好的发展，提供了宝贵的信息，为整体发现疾病的基因组结构，并有可能打开一个新的途径，发现新的疾病易感基因，不能确定，如果他们被单独分析。成像和基因组数据分析成功的一个关键问题是如何减少它们的维度。大多数先前的用于成像信息提取和RNA-seq数据缩减的方法不探索成像空间信息，并且通常忽略基因组位置水平上的基因表达变异。为了克服这些局限性，我们扩展功能主成分分析从一维到二维（2DFPCA）表示成像数据，并开发了一个多功能线性模型（MFLM），其中图像的功能主得分作为多个数量性状和跨基因的RNA-seq配置文件作为一个功能预测器，用于评估基因表达与图像的关联。所开发的方法已应用于卵巢癌和肾透明细胞癌（KIRC）研究的图像和RNA-seq数据。我们分别鉴定了24个和84个基因，它们的表达与卵巢癌和KIRC研究中的成像变异相关。我们的研究结果表明，许多与图像显著相关的基因没有差异表达，但揭示了它们的形态和代谢功能。结果还表明，MFLM中估计的回归系数函数的峰值通常允许发现剪接位点和基因表达的多种异构体。
Emerging integrative analysis of genomic and anatomical imaging data which has not been well developed, provides invaluable information for the holistic discovery of the genomic structure of disease and has the potential to open a new avenue for discovering novel disease susceptibility genes which cannot be identified if they are analyzed separately. A key issue to the success of imaging and genomic data analysis is how to reduce their dimensions. Most previous methods for imaging information extraction and RNA-seq data reduction do not explore imaging spatial information and often ignore gene expression variation at the genomic positional level. To overcome these limitations, we extend functional principle component analysis from one dimension to two dimensions (2DFPCA) for representing imaging data and develop a multiple functional linear model (MFLM) in which functional principal scores of images are taken as multiple quantitative traits and RNA-seq profile across a gene is taken as a function predictor for assessing the association of gene expression with images. The developed method has been applied to image and RNA-seq data of ovarian cancer and kidney renal clear cell carcinoma (KIRC) studies. We identified 24 and 84 genes whose expressions were associated with imaging variations in ovarian cancer and KIRC studies, respectively. Our results showed that many significantly associated genes with images were not differentially expressed, but revealed their morphological and metabolic functions. The results also demonstrated that the peaks of the estimated regression coefficient function in the MFLM often allowed the discovery of splicing sites and multiple isoforms of gene expressions.