In:
PLOS Computational Biology, Public Library of Science (PLoS), Vol. 17, No. 10 ( 2021-10-22), p. e1008986-
Abstract:
High-throughput data such as metabolomics, genomics, transcriptomics, and proteomics have become familiar data types within the “-omics” family. For this work, we focus on subsets that interact with one another and represent these “pathways” as graphs. Observed pathways often have disjoint components, i.e., nodes or sets of nodes (metabolites, etc.) not connected to any other within the pathway, which notably lessens testing power. In this paper we propose the Pa thway I ntegrated R egression-based K ernel A ssociation T est (PaIRKAT), a new kernel machine regression method for incorporating known pathway information into the semi-parametric kernel regression framework. This work extends previous kernel machine approaches. This paper also contributes an application of a graph kernel regularization method for overcoming disconnected pathways. By incorporating a regularized or “smoothed” graph into a score test, PaIRKAT can provide more powerful tests for associations between biological pathways and phenotypes of interest and will be helpful in identifying novel pathways for targeted clinical research. We evaluate this method through several simulation studies and an application to real metabolomics data from the COPDGene study. Our simulation studies illustrate the robustness of this method to incorrect and incomplete pathway knowledge, and the real data analysis shows meaningful improvements of testing power in pathways. PaIRKAT was developed for application to metabolomic pathway data, but the techniques are easily generalizable to other data sources with a graph-like structure.
Type of Medium:
Online Resource
ISSN:
1553-7358
DOI:
10.1371/journal.pcbi.1008986
DOI:
10.1371/journal.pcbi.1008986.g001
DOI:
10.1371/journal.pcbi.1008986.g002
DOI:
10.1371/journal.pcbi.1008986.g003
DOI:
10.1371/journal.pcbi.1008986.g004
DOI:
10.1371/journal.pcbi.1008986.g005
DOI:
10.1371/journal.pcbi.1008986.t001
DOI:
10.1371/journal.pcbi.1008986.t002
DOI:
10.1371/journal.pcbi.1008986.t003
DOI:
10.1371/journal.pcbi.1008986.t004
DOI:
10.1371/journal.pcbi.1008986.s001
DOI:
10.1371/journal.pcbi.1008986.s002
DOI:
10.1371/journal.pcbi.1008986.s003
DOI:
10.1371/journal.pcbi.1008986.s004
DOI:
10.1371/journal.pcbi.1008986.s005
DOI:
10.1371/journal.pcbi.1008986.s006
DOI:
10.1371/journal.pcbi.1008986.s007
DOI:
10.1371/journal.pcbi.1008986.s008
DOI:
10.1371/journal.pcbi.1008986.s009
DOI:
10.1371/journal.pcbi.1008986.s010
DOI:
10.1371/journal.pcbi.1008986.r001
DOI:
10.1371/journal.pcbi.1008986.r002
DOI:
10.1371/journal.pcbi.1008986.r003
DOI:
10.1371/journal.pcbi.1008986.r004
DOI:
10.1371/journal.pcbi.1008986.r005
DOI:
10.1371/journal.pcbi.1008986.r006
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2021
detail.hit.zdb_id:
2193340-6