Searching for Alternative Splicing With a Joint Model on Probe Measurability and Expression Intensities

Abstract

The exon tiling array offers a high throughput technology to search for aberrant splicing in biomedical research, but few methods of analysis for splicing detection have been tested both statistically and empirically. Noisy measurements on nonresponsive probe selection regions and outlying intensities at some of the samples tend to distort model-based assessments. We propose a robust analysis of variance approach that incorporates an informative model on probe measurability and uses median regression rank scores for better reliability in alternative splicing detection. We study the validity and effectiveness of our proposed approach in contrast with some of the existing methods through an empirical investigation of a brain cancer experiment, where a set of biologically validated genes for splicing and nonsplicing are available. Our study demonstrates favorable performance of the proposed ranking method, but shows that analysis of statistical significance cannot be trusted from any conventional use of p-values. We warn of any routine attempt to interpret p-values and their derivatives in model-based detection of alternative splicing.

KEY WORDS:

• Alternative splicing
• ANOVA
• Median regression model
• Noninformative probes
• Probabilistic model

Acknowledgments

This research work is partially supported by the National Science Foundation Grants DMS-0706818, DMS-0604229, and DMS-1007396; National Institute of Health Grants R01GM080503-01A1, R21CA129671, and NCI CA97007; and National Natural Science of China Grant 10828102. We thank Drs. Ralf Krahe and Gilbert Cote for providing us the brain cancer dataset, Dr. Keith Baggerly for helpful discussions, and Dr. Dimos Gaidatzis for help on using COSIE software. We also thank anonymous referees for many constructive comments that led to improvements in the article.