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Cited by 131Bioinformatics and Genomic NetworksChronic Obstructive Pulmonary Disease (COPD) ResearchGene expression and cancer classification

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Integrative phenotyping framework (iPF): integrative clustering of multiple omics data identifies novel lung disease subphenotypes

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Kim, SungHwan, Herazo-Maya, Jose D., Kang, Dongwan D., Juan-Guardela, Brenda M., Tedrow, John, Martinez, Fernando J., Sciurba, Frank C., Tseng, George C., Kaminski, Naftali·2015·BMC Genomics
DOI 10.1186/s12864-015-2170-4PMC PMC4642618
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Abstract

BACKGROUND: The increased multi-omics information on carefully phenotyped patients in studies of complex diseases requires novel methods for data integration. Unlike continuous intensity measurements from most omics data sets, phenome data contain clinical variables that are binary, ordinal and categorical. RESULTS: In this paper we introduce an integrative phenotyping framework (iPF) for disease subtype discovery. A feature topology plot was developed for effective dimension reduction and visualization of multi-omics data. The approach is free of model assumption and robust to data noises or missingness. We developed a workflow to integrate homogeneous patient clustering from different omics data in an agglomerative manner and then visualized heterogeneous clustering of pairwise omics sources. We applied the framework to two batches of lung samples obtained from patients diagnosed with chronic obstructive lung disease (COPD) or interstitial lung disease (ILD) with well-characterized clinical (phenomic) data, mRNA and microRNA expression profiles. Application of iPF to the first training batch identified clusters of patients consisting of homogenous disease phenotypes as well as clusters with intermediate disease characteristics. Analysis of the second batch revealed a similar data structure, confirming the presence of intermediate clusters. Genes in the intermediate clusters were enriched with inflammatory and immune functional annotations, suggesting that they represent mechanistically distinct disease subphenotypes that may response to immunomodulatory therapies. The iPF software package and all source codes are publicly available. CONCLUSIONS: Identification of subclusters with distinct clinical and biomolecular characteristics suggests that integration of phenomic and other omics information could lead to identification of novel mechanism-based disease sub-phenotypes.

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Crossref · 10.1186/s12864-015-2170-4

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Fig. 1 Flow chart of Integrative Phenotyping Framework (iPF): Integrative phenotyping framework (iPF) includes the following steps (1) Data preprocessing (2) Feature concatenation (3) Dimension reduction (4) Feature smoothing (5) Clustering for subtype discovery and visualization
Fig. 1 Flow chart of Integrative Phenotyping Framework (iPF): Integrative phenotyping framework (iPF) includes the following steps (1) Data preprocessing (2) Feature concatenation (3) Dimension reduction (4) Feature smoothing (5) Clustering for subtype discovery and visualizationCC BY · PubMed Central
Fig. 2 Overview of integrative clustering in integrative phenotyping framework (iPF): (a) Vertically combined multiple omics data sets (b) A distance matrix between any two features within and across omic data sets (c) Multidimensional scaling (MDS) mapping to a two-dimensional Euclidean space (d) Smoothed feature intensities in the reduced 2D space for each patient (e) Unsupervised clustering to identify potential disease subtypes and averaged feature intensities for representative plots of each cluster
Fig. 2 Overview of integrative clustering in integrative phenotyping framework (iPF): (a) Vertically combined multiple omics data sets (b) A distance matrix between any two features within and across omic data sets (c) Multidimensional scaling (MDS) mapping to a two-dimensional Euclidean space (d) Smoothed feature intensities in the reduced 2D space for each patient (e) Unsupervised clustering to identify potential disease subtypes and averaged feature intensities for representative plots of each clusterCC BY · PubMed Central
Fig. 3 Graphical illustration of heterogeneous data sets comparison scheme in iPF. Step 1-2: Compare and combine all possible pairs of omics data sets until we produce homogenous clustering results. Step 3: Pairwise comparison until identifying any heterogeneous data sets
Fig. 3 Graphical illustration of heterogeneous data sets comparison scheme in iPF. Step 1-2: Compare and combine all possible pairs of omics data sets until we produce homogenous clustering results. Step 3: Pairwise comparison until identifying any heterogeneous data setsCC BY · PubMed Central
Fig. 4 iPF clustering results for multiple omics data sets using all samples (Clinical, mRNA + miRNA). This figure shows three clusters are generated in each omics data source, and the feature topology plots for clusters from the first and the second omics data sources on the left and on the top, respectively
Fig. 4 iPF clustering results for multiple omics data sets using all samples (Clinical, mRNA + miRNA). This figure shows three clusters are generated in each omics data source, and the feature topology plots for clusters from the first and the second omics data sources on the left and on the top, respectivelyCC BY · PubMed Central
Fig. 5 Heatmap for the four modules of gene expression and miRNA features which significantly differentiate three clusters (COPD, Intermediate, and ILD). We performed gene co-expression cluster analysis using partition around modoids (PAM) to identify four gene and miRNA modules. When perform clustering, the 88 miRNAs intensities are turned to the opposite direction (by multiplying the expression intensities by -1) to show that most miRNAs have inhibitory effects on mRNA expression
Fig. 5 Heatmap for the four modules of gene expression and miRNA features which significantly differentiate three clusters (COPD, Intermediate, and ILD). We performed gene co-expression cluster analysis using partition around modoids (PAM) to identify four gene and miRNA modules. When perform clustering, the 88 miRNAs intensities are turned to the opposite direction (by multiplying the expression intensities by -1) to show that most miRNAs have inhibitory effects on mRNA expressionCC BY · PubMed Central

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Metadata

Authors
Kim, S., Herazo-Maya, J. D., Kang, D. D., Juan-Guardela, B. M., Tedrow, J., Martinez, F. J., Sciurba, F. C., Tseng, G. C., & Kaminski, N.
Affiliations
  • University of Pittsburgh (US)
  • Lawrence Berkeley National Laboratory (US)
  • Yale University (US)
Corresponding author
  • Tseng, George C.
  • Kaminski, Naftali
ORCID
Venue
BMC Genomics
Publisher
Springer Science and Business Media LLC
Published
2015-11-11
Volume
16
Issue
1
Pages
924-924
ISSN
1471-2164 (ISSN-L)
DOI
10.1186/s12864-015-2170-4
PMC
PMC4642618
OpenAlex
W2163675951
Cited by
123
References
39
Citations by year
2026: 6 · 2025: 10 · 2024: 20 · 2023: 18 · 2022: 12 · 2021: 11 · 2020: 10 · 2019: 11 · 2018: 7 · 2017: 9 · 2016: 8
FWCI
3.48
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Genomics (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • D-Scholarship@Pitt (University of Pittsburgh) (acceptedVersion)
Funders
National Institutes of Health (U01-HL108642, RC2-HL101715)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Chronic Obstructive Pulmonary Disease (COPD) Research
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
Keywords
multi-omics integration, disease subphenotypes, patient clustering, chronic obstructive pulmonary disease, interstitial lung disease, mRNA expression, microRNA expression, dimensionality reduction, agglomerative clustering, immunomodulatory therapy
MeSH terms
Algorithms, Computer Simulation, Humans, Lung Diseases, Phenotype, Cluster Analysis, Discriminant Analysis, Computational Biology, Genomics, Workflow, Molecular Sequence Annotation, Datasets as Topic

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