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A Bayesian latent variable approach to aggregation of partial and top-ranked lists in genomic studies.

ABSTRACT: In genomic research, it is becoming increasingly popular to perform meta-analysis, the practice of combining results from multiple studies that target a common essential biological problem. Rank aggregation, a robust meta-analytic approach, consolidates such studies at the rank level. There exists extensive research on this topic, and various methods have been developed in the past. However, these methods have two major limitations when they are applied in the genomic context. First, they are mainly designed to work with full lists, whereas partial and/or top-ranked lists prevail in genomic studies. Second, the component studies are often clustered, and the existing methods fail to utilize such information. To address the above concerns, a Bayesian latent variable approach, called BiG, is proposed to formally deal with partial and top-ranked lists and incorporate the effect of clustering. Various reasonable prior specifications for variance parameters in hierarchical models are carefully studied and compared. Simulation results demonstrate the superior performance of BiG compared with other popular rank aggregation methods under various practical settings. A non-small-cell lung cancer data example is analyzed for illustration.

SUBMITTER: Li X

PROVIDER: S-EPMC6235700 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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A Bayesian latent variable approach to aggregation of partial and top-ranked lists in genomic studies.

Li Xue X Choudhary Pankaj Kumar PK Biswas Swati S Wang Xinlei X

Statistics in medicine 20180809 28

In genomic research, it is becoming increasingly popular to perform meta-analysis, the practice of combining results from multiple studies that target a common essential biological problem. Rank aggregation, a robust meta-analytic approach, consolidates such studies at the rank level. There exists extensive research on this topic, and various methods have been developed in the past. However, these methods have two major limitations when they are applied in the genomic context. First, they are ma ...[more]

PMID: 30094911

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Project description:Computational methods for discovery of sequence elements that are enriched in a target set compared with a background set are fundamental in molecular biology research. One example is the discovery of transcription factor binding motifs that are inferred from ChIP-chip (chromatin immuno-precipitation on a microarray) measurements. Several major challenges in sequence motif discovery still require consideration: (i) the need for a principled approach to partitioning the data into target and background sets; (ii) the lack of rigorous models and of an exact p-value for measuring motif enrichment; (iii) the need for an appropriate framework for accounting for motif multiplicity; (iv) the tendency, in many of the existing methods, to report presumably significant motifs even when applied to randomly generated data. In this paper we present a statistical framework for discovering enriched sequence elements in ranked lists that resolves these four issues. We demonstrate the implementation of this framework in a software application, termed DRIM (discovery of rank imbalanced motifs), which identifies sequence motifs in lists of ranked DNA sequences. We applied DRIM to ChIP-chip and CpG methylation data and obtained the following results. (i) Identification of 50 novel putative transcription factor (TF) binding sites in yeast ChIP-chip data. The biological function of some of them was further investigated to gain new insights on transcription regulation networks in yeast. For example, our discoveries enable the elucidation of the network of the TF ARO80. Another finding concerns a systematic TF binding enhancement to sequences containing CA repeats. (ii) Discovery of novel motifs in human cancer CpG methylation data. Remarkably, most of these motifs are similar to DNA sequence elements bound by the Polycomb complex that promotes histone methylation. Our findings thus support a model in which histone methylation and CpG methylation are mechanistically linked. Overall, we demonstrate that the statistical framework embodied in the DRIM software tool is highly effective for identifying regulatory sequence elements in a variety of applications ranging from expression and ChIP-chip to CpG methylation data. DRIM is publicly available at http://bioinfo.cs.technion.ac.il/drim.

Project description:Importance:Hospital networks formed around top-ranked cancer hospitals represent an opportunity to optimize complex cancer care in the community. Objective:To compare the short- and long-term survival after complex cancer treatment at top-ranked cancer hospitals and the affiliates of top-ranked hospitals. Design, Setting, and Participants:This cohort study was conducted using data from the unabridged version of the National Cancer Database. Included patients were individuals 18 years or older who underwent surgical treatment for esophageal, gastric, lung, pancreatic, colorectal, or bladder cancer diagnosed between January 1, 2012, and December 31, 2016. Patient outcomes after complex surgical procedures for cancer at top-ranked cancer hospitals (as ranked in top 50 by US News and World Report) were compared with outcomes at affiliates of top-ranked cancer hospitals (affiliation listed in American Hospitals Association survey and confirmed by search of internet presence). Data were analyzed from July through December 2019. Exposures:Undergoing complex cancer treatment at a top-ranked cancer hospital or an affiliated hospital. Main Outcomes and Measures:The association of affiliate status with short-term survival (ie, 90-day mortality) was compared using logistic regression, and the association of affiliate status with long-term survival was compared using time-to-event models, adjusting for patient demographic, payer, clinical, and treatment factors. Results:Among 119?834 patients who underwent surgical treatment for cancer, 79?981 patients (66.7%) were treated at top-ranked cancer hospitals (median [interquartile range] age, 66 [58-74] years; 40?910 [54.9%] men) and 39?853 patients (33.3%) were treated at affiliate hospitals (median [interquartile range] age, 69 [60-77] years; 19?004 [50.0%] men). In a pooled analysis of all cancer types, adjusted perioperative mortality within 90 days of surgical treatment was higher at affiliate hospitals compared with top-ranked hospitals (odds ratio, 1.67 [95% CI, 1.49-1.89]; P?<?.001). Adjusted long-term survival following cancer treatment at affiliate hospitals was only 77% that of top-ranked hospitals (time ratio, 0.77 [95% CI, 0.72-0.83]; P?<?.001). The survival advantage was not fully explained by differences in annual surgical volume, with both long- and short-term survival remaining superior at top-ranked hospitals even after models were adjusted for volume. Conclusions and Relevance:These findings suggest that short- and long-term survival after complex cancer treatment were superior at top-ranked hospitals compared with affiliates of top-ranked hospitals. Further study of cancer care within top-ranked cancer networks could reveal collaborative opportunities to improve survival across a broad contingent of the US population.

Dataset Information

A Bayesian latent variable approach to aggregation of partial and top-ranked lists in genomic studies.

Publications

A Bayesian latent variable approach to aggregation of partial and top-ranked lists in genomic studies.

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