TY - JOUR
T1 - MOPA
T2 - An integrative multi-omics pathway analysis method for measuring omics activity
AU - Jeon, Jaemin
AU - Han, Eon Yong
AU - Jung, Inuk
N1 - Publisher Copyright:
Copyright: © 2023 Jeon et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2023/3
Y1 - 2023/3
N2 - Pathways are composed of proteins forming a network to represent specific biological mechanisms and are often used to measure enrichment scores based on a list of genes in means to measure their biological activity. The pathway analysis is a de facto standard downstream analysis procedure in most genomic and transcriptomic studies. Here, we present MOPA (Multi-Omics Pathway Analysis), which is a multi-omics integrative method that scores individual pathways in a sample wise manner in terms of enriched multi-omics regulatory activity, which we refer to mES (multi-omics Enrichment Score). The mES score reflects the strength of regulatory relations between multi-omics in units of pathways. In addition, MOPA is able to measure how much each omics contribute to mES that may be used to observe what kind of omics are active in a pathway within a sample group (e.g., subtype, gender), which we refer to OCR (Omics Contribution Rate). Using nine different cancer types, 93 clinical features and three types of omics (i.e., gene expression, miRNA and methylation), MOPA was used to search for clinical features that were explainable in context of multi-omics. By evaluating the performance of MOPA, we showed that it yielded higher or at least equal performance compared to previous single and multi-omics pathway analysis tools. We find that the advantage of MOPA is the ability to explain pathways in terms of omics relation using mES and OCR. As one of the results, the TGF-beta signaling pathway was captured as an important pathway that showed distinct mES and OCR values specific to the CMS4 subtype in colon adenocarcinoma. The mES and OCR metrics suggested that the mRNA and miRNA expressions were significantly different from the other subtypes, which was concordant with previous studies. The MOPA software is available at https://github.com/jaeminjj/MOPA.
AB - Pathways are composed of proteins forming a network to represent specific biological mechanisms and are often used to measure enrichment scores based on a list of genes in means to measure their biological activity. The pathway analysis is a de facto standard downstream analysis procedure in most genomic and transcriptomic studies. Here, we present MOPA (Multi-Omics Pathway Analysis), which is a multi-omics integrative method that scores individual pathways in a sample wise manner in terms of enriched multi-omics regulatory activity, which we refer to mES (multi-omics Enrichment Score). The mES score reflects the strength of regulatory relations between multi-omics in units of pathways. In addition, MOPA is able to measure how much each omics contribute to mES that may be used to observe what kind of omics are active in a pathway within a sample group (e.g., subtype, gender), which we refer to OCR (Omics Contribution Rate). Using nine different cancer types, 93 clinical features and three types of omics (i.e., gene expression, miRNA and methylation), MOPA was used to search for clinical features that were explainable in context of multi-omics. By evaluating the performance of MOPA, we showed that it yielded higher or at least equal performance compared to previous single and multi-omics pathway analysis tools. We find that the advantage of MOPA is the ability to explain pathways in terms of omics relation using mES and OCR. As one of the results, the TGF-beta signaling pathway was captured as an important pathway that showed distinct mES and OCR values specific to the CMS4 subtype in colon adenocarcinoma. The mES and OCR metrics suggested that the mRNA and miRNA expressions were significantly different from the other subtypes, which was concordant with previous studies. The MOPA software is available at https://github.com/jaeminjj/MOPA.
UR - http://www.scopus.com/inward/record.url?scp=85150243203&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0278272
DO - 10.1371/journal.pone.0278272
M3 - Article
C2 - 36928437
AN - SCOPUS:85150243203
SN - 1932-6203
VL - 18
JO - PLoS ONE
JF - PLoS ONE
IS - 3 March
M1 - e0278272
ER -