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

Mark C. Emerick, Giovanni Parmigiani, William S. Agnew

Research output: Contribution to journalArticle

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.

LanguageEnglish (US)
Article number16
JournalBMC Bioinformatics
Volume8
DOIs
StatePublished - 2007

Fingerprint

Multivariate Analysis
Transcriptome
Visualization
Genes
Gene
Gene Library
RNA
Proteome
Metabolism
Linkage
Clocks
Linear Models
Integrated Squared Error
Tissue
Higher Order
Bayes Estimate
Metric
Log-linear Models
Empirical Bayes
Functional Relationship

ASJC Scopus subject areas

  • Medicine(all)
  • Structural Biology
  • Applied Mathematics

Cite this

Multivariate analysis and visualization of splicing correlations in single-gene transcriptomes. / Emerick, Mark C.; Parmigiani, Giovanni; Agnew, William S.

In: BMC Bioinformatics, Vol. 8, 16, 2007.

Research output: Contribution to journalArticle

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