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iOmicsPASS: network-based integration of multiomics data for predictive subnetwork discovery

iOmicsPASS: network-based integration of multiomics data for predictive subnetwork discovery
iOmicsPASS: network-based integration of multiomics data for predictive subnetwork discovery

Computational tools for multiomics data integration have usually been designed for unsupervised detection of multiomics features explaining large phenotypic variations. To achieve this, some approaches extract latent signals in heterogeneous data sets from a joint statistical error model, while others use biological networks to propagate differential expression signals and find consensus signatures. However, few approaches directly consider molecular interaction as a data feature, the essential linker between different omics data sets. The increasing availability of genome-scale interactome data connecting different molecular levels motivates a new class of methods to extract interactive signals from multiomics data. Here we developed iOmicsPASS, a tool to search for predictive subnetworks consisting of molecular interactions within and between related omics data types in a supervised analysis setting. Based on user-provided network data and relevant omics data sets, iOmicsPASS computes a score for each molecular interaction, and applies a modified nearest shrunken centroid algorithm to the scores to select densely connected subnetworks that can accurately predict each phenotypic group. iOmicsPASS detects a sparse set of predictive molecular interactions without loss of prediction accuracy compared to alternative methods, and the selected network signature immediately provides mechanistic interpretation of the multiomics profile representing each sample group. Extensive simulation studies demonstrate clear benefit of interaction-level modeling. iOmicsPASS analysis of TCGA/CPTAC breast cancer data also highlights new transcriptional regulatory network underlying the basal-like subtype as positive protein markers, a result not seen through analysis of individual omics data.

Koh, Hiromi W.L.
b9043223-f948-44b7-a258-4d0fab46b84a
Fermin, Damian
5bec088e-9bd0-43ed-b1fb-1615673e8b57
Vogel, Christine
1a1259a9-d1ec-4177-a615-ed9c9559118e
Choi, Kwok Pui
01cc7fa2-6672-415b-9d79-bf569348873b
Ewing, Rob M.
022c5b04-da20-4e55-8088-44d0dc9935ae
Choi, Hyungwon
a3450432-0eda-44bb-8d8b-ab02b858fd05
Koh, Hiromi W.L.
b9043223-f948-44b7-a258-4d0fab46b84a
Fermin, Damian
5bec088e-9bd0-43ed-b1fb-1615673e8b57
Vogel, Christine
1a1259a9-d1ec-4177-a615-ed9c9559118e
Choi, Kwok Pui
01cc7fa2-6672-415b-9d79-bf569348873b
Ewing, Rob M.
022c5b04-da20-4e55-8088-44d0dc9935ae
Choi, Hyungwon
a3450432-0eda-44bb-8d8b-ab02b858fd05

Koh, Hiromi W.L., Fermin, Damian, Vogel, Christine, Choi, Kwok Pui, Ewing, Rob M. and Choi, Hyungwon (2019) iOmicsPASS: network-based integration of multiomics data for predictive subnetwork discovery. npj Systems Biology and Applications, 5 (1), [22]. (doi:10.1038/s41540-019-0099-y).

Record type: Article

Abstract

Computational tools for multiomics data integration have usually been designed for unsupervised detection of multiomics features explaining large phenotypic variations. To achieve this, some approaches extract latent signals in heterogeneous data sets from a joint statistical error model, while others use biological networks to propagate differential expression signals and find consensus signatures. However, few approaches directly consider molecular interaction as a data feature, the essential linker between different omics data sets. The increasing availability of genome-scale interactome data connecting different molecular levels motivates a new class of methods to extract interactive signals from multiomics data. Here we developed iOmicsPASS, a tool to search for predictive subnetworks consisting of molecular interactions within and between related omics data types in a supervised analysis setting. Based on user-provided network data and relevant omics data sets, iOmicsPASS computes a score for each molecular interaction, and applies a modified nearest shrunken centroid algorithm to the scores to select densely connected subnetworks that can accurately predict each phenotypic group. iOmicsPASS detects a sparse set of predictive molecular interactions without loss of prediction accuracy compared to alternative methods, and the selected network signature immediately provides mechanistic interpretation of the multiomics profile representing each sample group. Extensive simulation studies demonstrate clear benefit of interaction-level modeling. iOmicsPASS analysis of TCGA/CPTAC breast cancer data also highlights new transcriptional regulatory network underlying the basal-like subtype as positive protein markers, a result not seen through analysis of individual omics data.

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More information

Accepted/In Press date: 14 June 2019
e-pub ahead of print date: 1 December 2019

Identifiers

Local EPrints ID: 432611
URI: http://eprints.soton.ac.uk/id/eprint/432611
PURE UUID: 555fe06e-14eb-4c14-b605-0bb5e3739270
ORCID for Rob M. Ewing: ORCID iD orcid.org/0000-0001-6510-4001

Catalogue record

Date deposited: 22 Jul 2019 16:30
Last modified: 26 Nov 2021 02:59

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Contributors

Author: Hiromi W.L. Koh
Author: Damian Fermin
Author: Christine Vogel
Author: Kwok Pui Choi
Author: Rob M. Ewing ORCID iD
Author: Hyungwon Choi

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