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Multi-omics subtyping pipeline for chronic obstructive pulmonary disease
- Source :
- PLoS ONE, Vol 16, Iss 8, p e0255337 (2021), PLoS ONE
- Publication Year :
- 2021
- Publisher :
- Public Library of Science (PLoS), 2021.
-
Abstract
- Chronic Obstructive Pulmonary Disease (COPD) is the third leading cause of mortality in the United States; however, COPD has heterogeneous clinical phenotypes. This is the first large scale attempt which uses transcriptomics, proteomics, and metabolomics (multi-omics) to determine whether there are molecularly defined clusters with distinct clinical phenotypes that may underlie the clinical heterogeneity. Subjects included 3,278 subjects from the COPDGene cohort with at least one of the following profiles: whole blood transcriptomes (2,650 subjects); plasma proteomes (1,013 subjects); and plasma metabolomes (1,136 subjects). 489 subjects had all three contemporaneous -omics profiles. Autoencoder embeddings were performed individually for each -omics dataset. Embeddings underwent subspace clustering using MineClus, either individually by -omics or combined, followed by recursive feature selection based on Support Vector Machines. Clusters were tested for associations with clinical variables. Optimal single -omics clustering typically resulted in two clusters. Although there was overlap for individual -omics cluster membership, each -omics cluster tended to be defined by unique molecular pathways. For example, prominent molecular features of the metabolome-based clustering included sphingomyelin, while key molecular features of the transcriptome-based clusters were related to immune and bacterial responses. We also found that when we integrated the -omics data at a later stage, we identified subtypes that varied based on age, severity of disease, in addition to diffusing capacity of the lungs for carbon monoxide, and precent on atrial fibrillation. In contrast, when we integrated the -omics data at an earlier stage by treating all data sets equally, there were no clinical differences between subtypes. Similar to clinical clustering, which has revealed multiple heterogenous clinical phenotypes, we show that transcriptomics, proteomics, and metabolomics tend to define clusters of COPD patients with different clinical characteristics. Thus, integrating these different -omics data sets affords additional insight into the molecular nature of COPD and its heterogeneity.
- Subjects :
- Proteomics
Male
Support Vector Machine
Pulmonology
Databases, Factual
Physiology
Disease
Biochemistry
Database and Informatics Methods
Pulmonary Disease, Chronic Obstructive
Mathematical and Statistical Techniques
Medicine and Health Sciences
Cluster Analysis
Gene Regulatory Networks
Multidisciplinary
Proteomic Databases
Age Factors
Genomics
Middle Aged
Subtyping
Body Fluids
Blood
Phenotype
Proteome
Medicine
Female
Anatomy
Transcriptome Analysis
Research Article
Subspace Clustering
Chronic Obstructive Pulmonary Disease
Science
Computational biology
Biology
Research and Analysis Methods
Metabolomics
Genetics
Metabolome
Humans
Cluster analysis
Aged
Emphysema
Gene Expression Profiling
Biology and Life Sciences
Computational Biology
Genome Analysis
Omics
Blood Counts
Metabolism
Biological Databases
Gene Expression Regulation
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 16
- Issue :
- 8
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....049b8c803fed0cc0a7fb9d3133185ed1