omicsNPC: Applying the Non-Parametric Combination Methodology to the Integrative Analysis of Heterogeneous Omics Data.

The 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 o...

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Main Authors: Nestoras Karathanasis, Ioannis Tsamardinos, Vincenzo Lagani
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2016-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC5094732?pdf=render
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author Nestoras Karathanasis
Ioannis Tsamardinos
Vincenzo Lagani
author_facet Nestoras Karathanasis
Ioannis Tsamardinos
Vincenzo Lagani
author_sort Nestoras Karathanasis
collection DOAJ
description The 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.We 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.The 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.
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spelling doaj.art-51a5b702762f48cfb0fce53959f711392022-12-22T03:47:51ZengPublic Library of Science (PLoS)PLoS ONE1932-62032016-01-011111e016554510.1371/journal.pone.0165545omicsNPC: Applying the Non-Parametric Combination Methodology to the Integrative Analysis of Heterogeneous Omics Data.Nestoras KarathanasisIoannis TsamardinosVincenzo LaganiThe 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.We 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.The 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.http://europepmc.org/articles/PMC5094732?pdf=render
spellingShingle Nestoras Karathanasis
Ioannis Tsamardinos
Vincenzo Lagani
omicsNPC: Applying the Non-Parametric Combination Methodology to the Integrative Analysis of Heterogeneous Omics Data.
PLoS ONE
title omicsNPC: Applying the Non-Parametric Combination Methodology to the Integrative Analysis of Heterogeneous Omics Data.
title_full omicsNPC: Applying the Non-Parametric Combination Methodology to the Integrative Analysis of Heterogeneous Omics Data.
title_fullStr omicsNPC: Applying the Non-Parametric Combination Methodology to the Integrative Analysis of Heterogeneous Omics Data.
title_full_unstemmed omicsNPC: Applying the Non-Parametric Combination Methodology to the Integrative Analysis of Heterogeneous Omics Data.
title_short omicsNPC: Applying the Non-Parametric Combination Methodology to the Integrative Analysis of Heterogeneous Omics Data.
title_sort omicsnpc applying the non parametric combination methodology to the integrative analysis of heterogeneous omics data
url http://europepmc.org/articles/PMC5094732?pdf=render
work_keys_str_mv AT nestoraskarathanasis omicsnpcapplyingthenonparametriccombinationmethodologytotheintegrativeanalysisofheterogeneousomicsdata
AT ioannistsamardinos omicsnpcapplyingthenonparametriccombinationmethodologytotheintegrativeanalysisofheterogeneousomicsdata
AT vincenzolagani omicsnpcapplyingthenonparametriccombinationmethodologytotheintegrativeanalysisofheterogeneousomicsdata