Overcoming the matched-sample bottleneck: an orthogonal approach to integrate omic data.
Overcoming the matched-sample bottleneck: an orthogonal approach to integrate omic data.
复制标题
DOI：
10.1038/srep29251
复制
发表时间：
2016-07-12
影响因子：
4.6
通讯作者：
Draghici S
中科院分区：
文献类型：
--
作者：
Nguyen T;Diaz D;Tagett R;Draghici S
MicroRNAs （miRNAs）是一种小的非编码RNA分子，其主要功能是通过与mRNA转录物杂交来调节基因产物的表达，从而抑制mRNA的翻译或降解。尽管mirna与包括癌症在内的复杂疾病有关，但它们对不同生物学途径和表型的影响在很大程度上是未知的。目前的集成方法需要样本匹配的miRNA/mRNA数据集，导致在实践中的适用性有限。由于这些方法不能整合独立实验中可用的异构信息，它们既不能解释个体研究中固有的偏差，也不能从增加的样本量中获益。在这里，我们提出了一个新的框架，能够整合独立研究（水平荟萃分析）中可用的miRNA和mRNA数据（垂直数据整合），从而对给定表型进行全面分析。为了证明该方法的实用性，我们对胰腺癌和结直肠癌进行了荟萃分析，使用了来自15个mRNA和14个miRNA表达数据集的1,471个样本。我们的二维数据集成方法大大提高了统计分析的能力，并正确识别已知的与表型有关的途径。拟议的框架具有足够的通用性，可以整合从高通量分析中获得的其他类型的数据。
MicroRNAs (miRNAs) are small non-coding RNA molecules whose primary function is to regulate the expression of gene products via hybridization to mRNA transcripts, resulting in suppression of translation or mRNA degradation. Although miRNAs have been implicated in complex diseases, including cancer, their impact on distinct biological pathways and phenotypes is largely unknown. Current integration approaches require sample-matched miRNA/mRNA datasets, resulting in limited applicability in practice. Since these approaches cannot integrate heterogeneous information available across independent experiments, they neither account for bias inherent in individual studies, nor do they benefit from increased sample size. Here we present a novel framework able to integrate miRNA and mRNA data (vertical data integration) available in independent studies (horizontal meta-analysis) allowing for a comprehensive analysis of the given phenotypes. To demonstrate the utility of our method, we conducted a meta-analysis of pancreatic and colorectal cancer, using 1,471 samples from 15 mRNA and 14 miRNA expression datasets. Our two-dimensional data integration approach greatly increases the power of statistical analysis and correctly identifies pathways known to be implicated in the phenotypes. The proposed framework is sufficiently general to integrate other types of data obtained from high-throughput assays.