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Multiple-cumulative probabilities used to cluster and visualize transcriptomes.

ABSTRACT: Analysis of gene expression data by clustering and visualizing played a central role in obtaining biological knowledge. Here, we used Pearson's correlation coefficient of multiple-cumulative probabilities (PCC-MCP) of genes to define the similarity of gene expression behaviors. To answer the challenge of the high-dimensional MCPs, we used icc-cluster, a clustering algorithm that obtained solutions by iterating clustering centers, with PCC-MCP to group genes. We then used t-statistic stochastic neighbor embedding (t-SNE) of KC-data to generate optimal maps for clusters of MCP (t-SNE-MCP-O maps). From the analysis of several transcriptome data sets, we demonstrated clear advantages for using icc-cluster with PCC-MCP over commonly used clustering methods. t-SNE-MCP-O was also shown to give clearly projecting boundaries for clusters of PCC-MCP, which made the relationships between clusters easy to visualize and understand.

SUBMITTER: Jia X

PROVIDER: S-EPMC5715267 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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Multiple-cumulative probabilities used to cluster and visualize transcriptomes.

Jia Xingang X Liu Yisu Y Han Qiuhong Q Lu Zuhong Z

FEBS open bio 20171113 12

Analysis of gene expression data by clustering and visualizing played a central role in obtaining biological knowledge. Here, we used Pearson's correlation coefficient of multiple-cumulative probabilities (PCC-MCP) of genes to define the similarity of gene expression behaviors. To answer the challenge of the high-dimensional MCPs, we used icc-cluster, a clustering algorithm that obtained solutions by iterating clustering centers, with PCC-MCP to group genes. We then used <i>t</i>-statistic stoch ...[more]

PMID: 29226087

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Project description:BACKGROUND:A pretest probability must be selected to calculate data to help clinicians, guideline boards and policy makers interpret diagnostic accuracy parameters. When multiple analyses for the same target condition are compared, identical pretest probabilities might be selected to facilitate the comparison. Some pretest probabilities may lead to exaggerations of the patient harms or benefits, and guidance on how and why to select a specific pretest probability is minimally described. Therefore, the aim of this study was to assess the data sources and methods used in Cochrane diagnostic test accuracy (DTA) reviews for determining pretest probabilities to facilitate the interpretation of DTA parameters. A secondary aim was to assess the use of identical pretest probabilities to compare multiple meta-analyses within the same target condition. METHODS:Cochrane DTA reviews presenting at least one meta-analytic estimate of the sensitivity and/or specificity as a primary analysis published between 2008 and January 2018 were included. Study selection and data extraction were performed by one author and checked by other authors. Observed data sources (e.g. studies in the review, or external sources) and methods to select pretest probabilities (e.g. median) were categorized. RESULTS:Fifty-nine DTA reviews were included, comprising of 308 meta-analyses. A pretest probability was used in 148 analyses. Authors used included studies in the DTA review, external sources, and author consensus as data sources for the pretest probability. Measures of central tendency with or without a measure of dispersion were used to determine the pretest probabilities, with the median most commonly used. Thirty-two target conditions had at least one identical pretest probability for all of the meta-analyses within their target condition. About half of the used identical pretest probabilities were inside the prevalence ranges from all analyses within a target condition. CONCLUSIONS:Multiple sources and methods were used to determine (identical) pretest probabilities in Cochrane DTA reviews. Indirectness and severity of downstream consequences may influence the acceptability of the certainty in calculated data with pretest probabilities. Consider: whether to present normalized frequencies, the influence of pretest probabilities on normalized frequencies, and whether to use identical pretest probabilities for meta-analyses in a target condition.

Dataset Information

Multiple-cumulative probabilities used to cluster and visualize transcriptomes.

Publications

Multiple-cumulative probabilities used to cluster and visualize transcriptomes.

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