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

• Published in: BMC bioinformatics 2007-01, Vol.8 (1), p.16-16, Article 16
• Main Authors: Emerick, Mark C, Parmigiani, Giovanni, Agnew, William S
• Format: Article
• Language: English
• Subjects: Alternative Splicing - genetics; Gene Library; Genetic Variation - genetics; Humans; Methodology; Models, Genetic; Multivariate Analysis; RNA Splice Sites - genetics; RNA Splicing - genetics; Spliceosomes - chemistry; Spliceosomes - genetics; Transcription, Genetic - genetics
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/1471-2105-8-16

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. 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. 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.