Multivariate Analysis and Visualization of Splicing Correlations in Single-Gene Transcriptomes
1 Department of Physiology, Johns Hopkins Medical School, Baltimore, MD 21205 USA
2 Departments of Oncology, Zoology, Johns Hopkins Medical School, and Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205 USA
3 Departments of Physiology and Neuroscience, Johns Hopkins Medical School, Baltimore, MD 21205 USA
BMC Bioinformatics 2007, 8:16 doi:10.1186/1471-2105-8-16Published: 18 January 2007
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.