Integrating different data types by regularized unsupervised multiple kernel learning with application to cancer subtype discovery

Speicher, Nora K.; Pfeifer, Nico

doi:10.1093/bioinformatics/btv244

Cited by 149 publications

(100 citation statements)

References 14 publications

(16 reference statements)

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“…This distinction has implications on the fitting procedures and their scalability. Frequentist formulations of clustering models have generally been those that assume common cluster boundaries across data sources [Kormaksson et al, 2012, Speicher and Pfeifer, 2015, Shen et al, 2009. Algorithms with similar aims that decompose aggregations of data matrices into lower dimension spaces have also been developed [Chalise and Fridley, 2017, Chalise et al, 2014, Chen et al, 2008.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Two-Way Latent Structure Model for Genomic Data Integration Reveals Few Pan-Genomic Cluster Subtypes in a Breast Cancer Cohort

Swanson

Lien

Bergholtz

et al. 2018

Preprint

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We propose a new Bayesian parametric model for integrative, unsupervised clustering across data sources. Such approaches are important in disease subtyping. In our method, we cluster samples in relation to each data source, distinguishing it from methods like Cluster of Cluster Assignments and iCluster, but cluster labels have across-dataset meaning, allowing cluster information to be shared between data sources. We propose a two-way latent structure of cluster assignment variables, within each dataset and across datasets (per sample), which allows borrowing strength across data sources. Importantly, a common scaling across data sources is not required. Inference is obtained by a Gibbs Sampler, which we improve to better cope with sparsity of unoccupied clusters and speed of convergence, and fit models with Gaussian and more general densities which influences across-dataset cluster label sharing. Uniquely, our formulation makes no nestedness assumptions of samples across data sources. We apply our model to a Norwegian breast cancer cohort of ductal carcinoma in-situ and invasive tumors, comprised of somatic copy-number alteration, methylation and expression datasets. We find enrichment in the Her2 subtype and ductal carcinoma among those observations exhibiting greater cluster correspondence across expression and CNA data.

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Section: Introductionmentioning

confidence: 99%

A Bayesian Two-Way Latent Structure Model for Genomic Data Integration Reveals Few Pan-Genomic Cluster Subtypes in a Breast Cancer Cohort

Swanson

Lien

Bergholtz

et al. 2018

Preprint

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“…We compare PAMOGK with eight other multi-omics methods. These include kmeans [26], MCCA [54], LRACluster [55], rMKL-LPP [45], iClusterBayes [29], PINS [34], SNF [51], and finally Spectral Clustering [58]. These methods cover all methods that are included in a recent comparative benchmark study by Rappoport et al [36] with the exception of multiNMF [24], which we are not able to run properly.…”

Section: Comparison With the State-of-the Art Multi-omics Methods Permentioning

confidence: 99%

“…Several generic multi-view kernel clustering methods (reviewed in [56]) have been developed where some have been applied to cancer subtyping. rMKL-LPP [45] extends the [23] multi-view kernel framework to the multi-omics clustering. A kernel matrix is computed from each omic data type, and a linear combination of kernels is sought for the clustering of the patients in kernel k-means.…”

Section: Introductionmentioning

confidence: 99%

PAMOGK: A Pathway Graph Kernel based Multi-Omics Clustering Approach for Discovering Cancer Patient Subgroups

Tepeli

Ünal

Akdemir

et al. 2019

Preprint

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Yasin Ilkagan Tepeli 1[0000−0002−3375−6678] , Ali BurakÜnal 2,3[0000−0002−7279−620X] , Furkan Mustafa Akdemir 3[0000−0003−0948−5756] , and Oznur Tastan 1[0000−0001−7058−5372]Abstract. Accurate classification of patients into homogeneous molecular subgroups is critical for the development of effective therapeutics and for deciphering what drives these different subtypes to cancer. However, the extensive molecular heterogeneity observed among cancer patients presents a challenge. The availability of multi-omic data catalogs for large cohorts of cancer patients provides multiple views into the molecular biology of the tumors with unprecedented resolution. In this work, we develop PAMOGK, which integrates multi-omics patient data and incorporates the existing knowledge on biological pathways. PAMOGK is well suited to deal with the sparsity of alterations in assessing patient similarities. We develop a novel graph kernel which we denote as smoothed shortest path graph kernel, which evaluates patient similarities based on a single molecular alteration type in the context of pathway. To corroborate multiple views of patients evaluated by hundreds of pathways and molecular alteration combinations, PAMOGK uses multi-view kernel clustering. We apply PAMOGK to find subgroups of kidney renal clear cell carcinoma (KIRC) patients, which results in four clusters with significantly different survival times (pvalue = 7.4e-10). The patient subgroups also differ with respect to other clinical parameters such as tumor stage and grade, and primary tumor and metastasis tumor spreads. When we compare PAMOGK to 8 other state-of-the-art existing multi-omics clustering methods, PAMOGK consistently outperforms these in terms of its ability to partition patients into groups with different survival distributions. PAMOGK enables extracting the relative importance of pathways and molecular data types. PAMOGK is available at github.com/tastanlab/pamogk

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“…In the early-integration approach, also known as juxtaposition-based, the multi-omics datasets are first concatenated into one matrix. To deal with the high-dimensionality of the joint dataset, these methods generally adopt matrix factorization (68,53,55,52), statistical (46,69,70,59,57,44,71,72,73,55), and machine learning tools (74,73,55). Although the dimensionality reduction procedure is necessary and may improve the predictive performance, it can also cause the loss of key information (66).…”

Section: Background and Related Workmentioning

confidence: 99%

“…Conversely, our INF method for omics data integration is an improvement of the popular Similarity Network Fusion (SNF) approach (5), which has inspired several studies in the scientific literature, specifically in cancer genomics (97,98,99,100,74,82,101). SNF maximizes the shared or correlated information between multiple datasets by combining data through inference of a joint network-based model, accounting for how informative each data type is to the observed similarity between samples.…”

Section: Integrative Network Fusionmentioning

confidence: 99%

Integrative Network Fusion: a multi-omics approach in molecular profiling

Chierici

Bussola

Marcolini

et al. 2020

Preprint

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Recent technological advances and international efforts, such as The Cancer Genome Atlas (TCGA), have made available several pan-cancer datasets encompassing multiple omics layers with detailed clinical information in large collection of samples. The need has thus arisen for the development of computational methods aimed at improving cancer subtyping and biomarker identification from multi-modal data. Here we apply the Integrative Network Fusion (INF) pipeline, which combines multiple omics layers exploiting Similarity Network Fusion (SNF) within a machine learning predictive framework. INF includes a feature ranking scheme (rSNF) on SNF-integrated features, used by a classifier over juxtaposed multi-omics features (juXT). In particular, we show instances of INF implementing Random Forest (RF) and linear Support Vector Machine (LSVM) as the classifier, and two baseline RF and LSVM models are also trained on juXT. A compact RF model, called rSNFi, trained on the intersection of top-ranked biomarkers from the two approaches juXT and rSNF is finally derived. All the classifiers are run in a 10x5-fold crossvalidation schema to warrant reproducibility, following the guidelines for an unbiased Data Analysis Plan by the US FDA-led initiatives MAQC/SEQC. INF is demonstrated on four classification tasks on three multi-modal TCGA oncogenomics datasets. Gene expression, protein abundances and copy number variants are used to predict estrogen receptor status (BRCA-ER, N=381) and breast invasive carcinoma subtypes (BRCA-subtypes, N=305), while gene expression, miRNA expression and methylation data is used as predictor layers for acute myeloid leukemia and renal clear cell carcinoma survival (AML-OS, N=157; KIRC-OS, N=181). In test, INF achieved similar Matthews Correlation Coefficient (MCC) values and 97% to 83% smaller feature sizes (FS), compared with juXT for BRCA-ER (MCC: 0.83 vs 0.80; FS: 56 vs 1801) and BRCA-subtypes 1 Chierici et al. INF(0.84 vs 0.80; 302 vs 1801), improving KIRC-OS performance (0.38 vs 0.31; 111 vs 2319). INF predictions are generally more accurate in test than one-dimensional omics models, with smaller signatures too, where transcriptomics consistently play the leading role. Overall, the INF framework effectively integrates multiple data levels in oncogenomics classification tasks, improving over the performance of single layers alone and naive juxtaposition, and provides compact signature sizes 1 .

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Integrating different data types by regularized unsupervised multiple kernel learning with application to cancer subtype discovery

Cited by 149 publications

References 14 publications

A Bayesian Two-Way Latent Structure Model for Genomic Data Integration Reveals Few Pan-Genomic Cluster Subtypes in a Breast Cancer Cohort

A Bayesian Two-Way Latent Structure Model for Genomic Data Integration Reveals Few Pan-Genomic Cluster Subtypes in a Breast Cancer Cohort

PAMOGK: A Pathway Graph Kernel based Multi-Omics Clustering Approach for Discovering Cancer Patient Subgroups

Integrative Network Fusion: a multi-omics approach in molecular profiling

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