Abstract
BackgroundThe advance of omics technologies has made possible to measure several data modalities on a system of interest. In this work, we illustrate how the Non-Parametric Combination methodology, namely NPC, can be used for simultaneously assessing the association of different molecular quantities with an outcome of interest. We argue that NPC methods have several potential applications in integrating heterogeneous omics technologies, as for example identifying genes whose methylation and transcriptional levels are jointly deregulated, or finding proteins whose abundance shows the same trends of the expression of their encoding genes.ResultsWe implemented the NPC methodology within “omicsNPC”, an R function specifically tailored for the characteristics of omics data. We compare omicsNPC against a range of alternative methods on simulated as well as on real data. Comparisons on simulated data point out that omicsNPC produces unbiased / calibrated p-values and performs equally or significantly better than the other methods included in the study; furthermore, the analysis of real data show that omicsNPC (a) exhibits higher statistical power than other methods, (b) it is easily applicable in a number of different scenarios, and (c) its results have improved biological interpretability.ConclusionsThe omicsNPC function competitively behaves in all comparisons conducted in this study. Taking into account that the method (i) requires minimal assumptions, (ii) it can be used on different studies designs and (iii) it captures the dependences among heterogeneous data modalities, omicsNPC provides a flexible and statistically powerful solution for the integrative analysis of different omics data.
Highlights
Recent developments in various high-throughput technologies have heightened the need for integrative analysis methods
We argue that Non-Parametric Combination (NPC) methods have several potential applications in integrating heterogeneous omics technologies, as for example identifying genes whose methylation and transcriptional levels are jointly deregulated, or finding proteins whose abundance shows the same trends of the expression of their encoding genes
Comparisons on simulated data point out that omicsNPC produces unbiased / calibrated p-values and performs or significantly better than the other methods included in the study; the analysis of real data show that omicsNPC (a) exhibits higher statistical power than other methods, (b) it is applicable in a number of different scenarios, and (c) its results have improved biological interpretability
Summary
Recent developments in various high-throughput technologies have heightened the need for integrative analysis methods. The key idea is to measure several aspects of the same system in order to gain a deeper understanding of the underlying biological mechanisms In such settings, a common tasks is identifying molecular quantities that are (a) measured by different omics technologies, (b) related to each other (e.g., associated to the same gene), and (c) that are conjointly affected by the factor(s) under study or associated to a relevant outcome, in a statistically significant way. Other studies investigate factors that simultaneously enhance the expression of a given protein and the abundance of its related metabolites [4,5] Another scenario (somewhat less common) is the measurement of the same molecular quantities with different technologies, as for example when previously produced microarray gene expression profiles should be co-analyzed with newly produced RNA-seq data [6]. We argue that NPC methods have several potential applications in integrating heterogeneous omics technologies, as for example identifying genes whose methylation and transcriptional levels are jointly deregulated, or finding proteins whose abundance shows the same trends of the expression of their encoding genes
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