Dataset Information

Multivariate analysis and visualization of splicing correlations in single-gene transcriptomes.

ABSTRACT:

Background

RNA metabolism, through 'combinatorial splicing', can generate enormous structural diversity in the proteome. Alternative domains may interact, however, with unpredictable phenotypic consequences, necessitating integrated RNA-level regulation of molecular composition. Splicing correlations within transcripts of single genes provide valuable clues to functional relationships among molecular domains as well as genomic targets for higher-order splicing regulation.

Results

We present tools to visualize complex splicing patterns in full-length cDNA libraries. Developmental changes in pair-wise correlations are presented vectorially in 'clock plots' and linkage grids. Higher-order correlations are assessed statistically through Monte Carlo analysis of a log-linear model with an empirical-Bayes estimate of the true probabilities of observed and unobserved splice forms. Log-linear coefficients are visualized in a 'spliceprint,' a signature of splice correlations in the transcriptome. We present two novel metrics: the linkage change index, which measures the directional change in pair-wise correlation with tissue differentiation, and the accuracy index, a very simple goodness-of-fit metric that is more sensitive than the integrated squared error when applied to sparsely populated tables, and unlike chi-square, does not diverge at low variance. Considerable attention is given to sparse contingency tables, which are inherent to single-gene libraries.

Conclusion

Patterns of splicing correlations are revealed, which span a broad range of interaction order and change in development. The methods have a broad scope of applicability, beyond the single gene--including, for example, multiple gene interactions in the complete transcriptome.

SUBMITTER: Emerick MC

PROVIDER: S-EPMC1785386 | biostudies-literature | 2007 Jan

REPOSITORIES: biostudies-literature

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Publications

Multivariate analysis and visualization of splicing correlations in single-gene transcriptomes.

Emerick Mark C MC Parmigiani Giovanni G Agnew William S WS

BMC bioinformatics 20070118

<h4>Background</h4>RNA metabolism, through 'combinatorial splicing', can generate enormous structural diversity in the proteome. Alternative domains may interact, however, with unpredictable phenotypic consequences, necessitating integrated RNA-level regulation of molecular composition. Splicing correlations within transcripts of single genes provide valuable clues to functional relationships among molecular domains as well as genomic targets for higher-order splicing regulation.<h4>Results</h4> ...[more]

PMID: 17233916

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Project description:BACKGROUND:Gene Set Enrichment Analysis (GSEA) is a powerful tool to identify enriched functional categories of informative biomarkers. Canonical GSEA takes one-dimensional feature scores derived from the data of one platform as inputs. Numerous extensions of GSEA handling multimodal OMIC data are proposed, yet none of them explicitly captures combinatorial relations of feature scores from multiple platforms. RESULTS:We propose multivariate GSEA (MGSEA) to capture combinatorial relations of gene set enrichment among multiple platform features. MGSEA successfully captures designed feature relations from simulated data. By applying it to the scores of delineating breast cancer and glioblastoma multiforme (GBM) subtypes from The Cancer Genome Atlas (TCGA) datasets of CNV, DNA methylation and mRNA expressions, we find that breast cancer and GBM data yield both similar and distinct outcomes. Among the enriched functional categories, subtype-specific biomarkers are dominated by mRNA expression in many functional categories in both cancer types and also by CNV in many functional categories in breast cancer. The enriched functional categories belonging to distinct combinatorial patterns are involved different oncogenic processes: cell proliferation (such as cell cycle control, estrogen responses, MYC and E2F targets) for mRNA expression in breast cancer, invasion and metastasis (such as cell adhesion and epithelial-mesenchymal transition (EMT)) for CNV in breast cancer, and diverse processes (such as immune and inflammatory responses, cell adhesion, angiogenesis, and EMT) for mRNA expression in GBM. These observations persist in two external datasets (Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) for breast cancer and Repository for Molecular Brain Neoplasia Data (REMBRANDT) for GBM) and are consistent with knowledge of cancer subtypes. We further compare the characteristics of MGSEA with several extensions of GSEA and point out the pros and cons of each method. CONCLUSIONS:We demonstrated the utility of MGSEA by inferring the combinatorial relations of multiple platforms for cancer subtype delineation in three multi-OMIC datasets: TCGA, METABRIC and REMBRANDT. The inferred combinatorial patterns are consistent with the current knowledge and also reveal novel insights about cancer subtypes. MGSEA can be further applied to any genotype-phenotype association problems with multimodal OMIC data.

Dataset Information

Multivariate analysis and visualization of splicing correlations in single-gene transcriptomes.

Background

Results

Conclusion

Publications

Multivariate analysis and visualization of splicing correlations in single-gene transcriptomes.

Similar Datasets

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