Latent rank change detection for analysis of splice-junction microarrays with nonlinear effects

Jonathan A Gelfond, Lee Ann Zarzabal, Tarea Burton, Suzanne Burns, Mari Sogayar, Luiz O Penalva

Research output: Contribution to journalArticle

Abstract

Alternative splicing of gene transcripts greatly expands the functional capacity of the genome, and certain splice isoforms may indicate specific disease states such as cancer. Splice junction microarrays interrogate thousands of splice junctions, but data analysis is difficult and error prone because of the increased complexity compared to differential gene expression analysis. We present Rank Change Detection (RCD) as a method to identify differential splicing events based upon a straightforward probabilistic model comparing the over- or underrepresentation of two or more competing isoforms. RCD has advantages over commonly used methods because it is robust to false positive errors due to nonlinear trends in microarray measurements. Further, RCD does not depend on prior knowledge of splice isoforms, yet it takes advantage of the inherent structure of mutually exclusive junctions, and it is conceptually generalizable to other types of splicing arrays or RNA-Seq. RCD specifically identifies the biologically important cases when a splice junction becomes more or less prevalent compared to other mutually exclusive junctions. The example data is from different cell lines of glioblastoma tumors assayed with Agilent microarrays.

Original languageEnglish (US)
Pages (from-to)364-380
Number of pages17
JournalAnnals of Applied Statistics
Volume5
Issue number1
DOIs
StatePublished - Mar 2011

Fingerprint

Change Detection
Nonlinear Effects
Microarrays
Microarray
Mutually exclusive
Genes
Gene Expression Analysis
RNA
Alternative Splicing
Gene expression
Tumors
Differential Expression
False Positive
Prior Knowledge
Cells
Probabilistic Model
Expand
Tumor
Data analysis
Cancer

Keywords

  • Alternative splicing
  • Gene expression analysis
  • Microarray

ASJC Scopus subject areas

  • Statistics, Probability and Uncertainty
  • Modeling and Simulation
  • Statistics and Probability

Cite this

Latent rank change detection for analysis of splice-junction microarrays with nonlinear effects. / Gelfond, Jonathan A; Zarzabal, Lee Ann; Burton, Tarea; Burns, Suzanne; Sogayar, Mari; Penalva, Luiz O.

In: Annals of Applied Statistics, Vol. 5, No. 1, 03.2011, p. 364-380.

Research output: Contribution to journalArticle

Gelfond, Jonathan A ; Zarzabal, Lee Ann ; Burton, Tarea ; Burns, Suzanne ; Sogayar, Mari ; Penalva, Luiz O. / Latent rank change detection for analysis of splice-junction microarrays with nonlinear effects. In: Annals of Applied Statistics. 2011 ; Vol. 5, No. 1. pp. 364-380.
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