TY - JOUR
T1 - mosGraphGen
T2 - a novel tool to generate multi-omics signaling graphs to facilitate integrative and interpretable graph AI model development
AU - Zhang, Heming
AU - Cao, Dekang
AU - Chen, Zirui
AU - Zhang, Xiuyuan
AU - Chen, Yixin
AU - Sessions, Cole
AU - Cruchaga, Carlos
AU - Payne, Philip
AU - Li, Guangfu
AU - Province, Michael
AU - Li, Fuhai
N1 - Publisher Copyright:
© The Author(s) 2024. Published by Oxford University Press.
PY - 2024
Y1 - 2024
N2 - Motivation: Multi-omics data, i.e. genomics, epigenomics, transcriptomics, proteomics, characterize cellular complex signaling systems from multi-level and multi-view and provide a holistic view of complex cellular signaling pathways. However, it remains challenging to integrate and interpret multi-omics data for mining critical biomarkers. Graph AI models have been widely used to analyze graph-structure datasets, and are ideal for integrative multi-omics data analysis because they can naturally integrate and represent multi-omics data as a biologically meaningful multi-level signaling graph and interpret multi-omics data via graph node and edge ranking analysis. Nevertheless, it is nontrivial for graph-AI model developers to pre-analyze multi-omics data and convert the data into biologically meaningful graphs, which can be directly fed into graph-AI models. Results: To resolve this challenge, we developed mosGraphGen (multi-omics signaling graph generator), generating Multi-omics Signaling graphs (mos-graph) of individual samples by mapping multi-omics data onto a biologically meaningful multi-level background signaling network with data normalization by aggregating measurements and aligning to the reference genome. With mosGraphGen, AI model developers can directly apply and evaluate their models using these mos-graphs. In the results, mosGraphGen was used and illustrated using two widely used multi-omics datasets of The Cancer Genome Atlas (TCGA) and Alzheimer’s disease (AD) samples. Availability and implementation: The code of mosGraphGen is open-source and publicly available via GitHub: https://github.com/FuhaiLiAiLab/mosGraphGen.
AB - Motivation: Multi-omics data, i.e. genomics, epigenomics, transcriptomics, proteomics, characterize cellular complex signaling systems from multi-level and multi-view and provide a holistic view of complex cellular signaling pathways. However, it remains challenging to integrate and interpret multi-omics data for mining critical biomarkers. Graph AI models have been widely used to analyze graph-structure datasets, and are ideal for integrative multi-omics data analysis because they can naturally integrate and represent multi-omics data as a biologically meaningful multi-level signaling graph and interpret multi-omics data via graph node and edge ranking analysis. Nevertheless, it is nontrivial for graph-AI model developers to pre-analyze multi-omics data and convert the data into biologically meaningful graphs, which can be directly fed into graph-AI models. Results: To resolve this challenge, we developed mosGraphGen (multi-omics signaling graph generator), generating Multi-omics Signaling graphs (mos-graph) of individual samples by mapping multi-omics data onto a biologically meaningful multi-level background signaling network with data normalization by aggregating measurements and aligning to the reference genome. With mosGraphGen, AI model developers can directly apply and evaluate their models using these mos-graphs. In the results, mosGraphGen was used and illustrated using two widely used multi-omics datasets of The Cancer Genome Atlas (TCGA) and Alzheimer’s disease (AD) samples. Availability and implementation: The code of mosGraphGen is open-source and publicly available via GitHub: https://github.com/FuhaiLiAiLab/mosGraphGen.
UR - http://www.scopus.com/inward/record.url?scp=85208689525&partnerID=8YFLogxK
U2 - 10.1093/bioadv/vbae151
DO - 10.1093/bioadv/vbae151
M3 - Article
C2 - 39506989
AN - SCOPUS:85208689525
SN - 2635-0041
VL - 4
JO - Bioinformatics Advances
JF - Bioinformatics Advances
IS - 1
M1 - vbae151
ER -