Abstract
BackgroundWith the advance of high throughput sequencing, high-dimensional data are generated. Detecting dependence/correlation between these datasets is becoming one of most important issues in multi-dimensional data integration and co-expression network construction. RNA-sequencing data is widely used to construct gene regulatory networks. Such networks could be more accurate when methylation data, copy number aberration data and other types of data are introduced. Consequently, a general index for detecting relationships between high-dimensional data is indispensable.ResultsWe proposed a Kernel-Based RV-coefficient, named KBRV, for testing both linear and nonlinear correlation between two matrices by introducing kernel functions into RV2 (the modified RV-coefficient). Permutation test and other validation methods were used on simulated data to test the significance and rationality of KBRV. In order to demonstrate the advantages of KBRV in constructing gene regulatory networks, we applied this index on real datasets (ovarian cancer datasets and exon-level RNA-Seq data in human myeloid differentiation) to illustrate its superiority over vector correlation.ConclusionsWe concluded that KBRV is an efficient index for detecting both linear and nonlinear relationships in high dimensional data. The correlation method for high dimensional data has possible applications in the construction of gene regulatory network.
Highlights
With the advance of high throughput sequencing, high-dimensional data are generated
Results from multiomics data For a heat map of the correlation matrix calculated through Maximal Information Coefficient (MIC) shown in Fig. 9a, we observed that the correlations between all genes were weak
The integrated gene regulatory network is more reasonable for its high correlations compared to MIC’s results
Summary
With the advance of high throughput sequencing, high-dimensional data are generated. Detecting dependence/correlation between these datasets is becoming one of most important issues in multi-dimensional data integration and co-expression network construction. RNA-sequencing data is widely used to construct gene regulatory networks. Such networks could be more accurate when methylation data, copy number aberration data and other types of data are introduced. A general index for detecting relationships between high-dimensional data is indispensable. With the rapid advance in high throughput sequencing technologies, multiple, high-dimensional data types are widely available. In exploring the correlations between matrices, Robert and Escoufier first proposed the RV-coefficient in multivariate analysis and Ramsay applied it to high-dimensional data [12, 13]. Wang et al integrated RV and mutual information into Iso-Net for predicting func-
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