Accurate Computation of Survival Statistics in Genome-Wide Studies

Vandin, Fabio; Papoutsaki, Alexandra; Raphael, Benjamin J.; Upfal, Eli

doi:10.1371/journal.pcbi.1004071

Cited by 30 publications

(31 citation statements)

References 41 publications

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“…Age, gender, mutation number and tumor stage were used as covariates. ExaLT, which was specifically designed for unequal sample sizes in TCGA data was compiled and run using the—table command according to the author’s readme on GitHub [ 15 ].…”

Section: Methodsmentioning

confidence: 99%

Inherited Disease Genetics Improves the Identification of Cancer-Associated Genes

Zhao

Pritchard

2016

PLoS Genet

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The identification of biologically significant variants in cancer genomes is critical to therapeutic discovery, but it is limited by the statistical power needed to discern driver from passenger. Independent biological data can be used to filter cancer exomes and increase statistical power. Large genetic databases for inherited diseases are uniquely suited to this task because they contain specific amino acid alterations with known pathogenicity and molecular mechanisms. However, no rigorous method to overlay this information onto the cancer exome exists. Here, we present a computational methodology that overlays any variant database onto the somatic mutations in all cancer exomes. We validate the computation experimentally and identify novel associations in a re-analysis of 7362 cancer exomes. This analysis identified activating SOS1 mutations associated with Noonan syndrome as significantly altered in melanoma and the first kinase-activating mutations in ACVR1 associated with adult tumors. Beyond a filter, significant variants found in both rare cancers and rare inherited diseases increase the unmet medical need for therapeutics that target these variants and may bootstrap drug discovery efforts in orphan indications.

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Section: Methodsmentioning

confidence: 99%

Inherited Disease Genetics Improves the Identification of Cancer-Associated Genes

Zhao

Pritchard

2016

PLoS Genet

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“…To derive a p-value for the logrank test, the χ 2 test for independence, and the Kruskal-Wallis test, the statistic for these three tests is assumed to have χ 2 distribution. However, for the logrank test and χ 2 test this approximation is not accurate for small sample sizes and unbalanced cluster sizes, especially for large values of the test statistic (this was shown for example in [109] for the logrank test in the case of two clusters). Indeed, we encountered in our analysis cases where the approximation gave extreme pvalues (< 10 −10 ) for very small clusters (n = 3).…”

Section: Benchmarkmentioning

confidence: 99%

Multi-omic and multi-view clustering algorithms: review and cancer benchmark

Rappoport

Shamir

2018

Preprint

143

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High throughput experimental methods developed in recent years have been used to collect large biomedical omics datasets. Clustering of such datasets has proven invaluable for biological and medical research, and helped reveal structure in data from several domains. Such analysis is often based on investigation of a single omic. The decreasing cost and development of additional high throughput methods now enable measurement of multi-omic data. Clustering multi-omic data has the potential to reveal further systems-level insights, but raises computational and biological challenges. Here we review algorithms for multi-omics clustering, and discuss key issues in applying these algorithms. Our review covers methods developed specifically for multi-omic data as well as generic multi-view methods developed in the machine learning community for joint clustering of multiple data types.In addition, using cancer data from TCGA, we perform an extensive benchmark spanning ten different cancer types, providing the first systematic benchmark comparison of leading multi-omics and multiview clustering algorithms. The results highlight several key questions regarding the use of single-vs. multi-omics, the choice of clustering strategy, the power of generic multi-view methods and the use of approximated p-values for gauging solution quality. Due to the rapidly increasing use of multi-omics data, these issues may be important for future progress in the field.

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“…For all methods, for all trials, the resulting clusters are balanced in terms of the number of patients participating in the clusters except two trials of MCCA. Log-rank test is known to result in unrealistically low p-values when one of cluster size is small [47]. In those two trials, MCCA's extremely low p-values are due to cluster sizes of 9 and 14.…”

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

confidence: 96%

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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Accurate Computation of Survival Statistics in Genome-Wide Studies

Cited by 30 publications

References 41 publications

Inherited Disease Genetics Improves the Identification of Cancer-Associated Genes

Inherited Disease Genetics Improves the Identification of Cancer-Associated Genes

Multi-omic and multi-view clustering algorithms: review and cancer benchmark

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

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