Different effects of the probe summarization algorithms PLIER and RMA on high-level analysis of Affymetrix exon arrays

BackgroundAlternative splicing is an important mechanism that increases protein diversity and functionality in higher eukaryotes. Affymetrix exon arrays are a commercialized platform used to detect alternative splicing on a genome-wide scale. Two probe summarization algorithms, PLIER (Probe Logarithmic Intensity Error) and RMA (Robust Multichip Average), are commonly used to compute gene-level and exon-level expression values. However, a systematic comparison of these two algorithms on their effects on high-level analysis of the arrays has not yet been reported.ResultsIn this study, we showed that PLIER summarization led to over-estimation of gene-level expression changes, relative to exon-level expression changes, in two-group comparisons. Consequently, it led to detection of substantially more skipped exons on up-regulated genes, as well as substantially more included (i.e., non-skipped) exons on down-regulated genes. In contrast, this bias was not observed for RMA-summarized data. By using a published human tissue dataset, we compared the tissue-specific expression and splicing detected by Affymetrix exon arrays with those detected based on expressed sequence databases. We found the tendency of PLIER was not supported by the expressed sequence data.ConclusionWe showed that the tendency of PLIER in detection of alternative splicing is likely caused by a technical bias in the approach, rather than a biological bias. Moreover, we observed abnormal summarization results when using the PLIER algorithm, indicating that mathematical problems, such as numerical instability, may affect PLIER performance.

[1]  Wei Zhang,et al.  Identification of genetic variants and gene expression relationships associated with pharmacogenes in humans , 2008, Pharmacogenetics and genomics.

[2]  Guide to Probe Logarithmic Intensity Error ( PLIER ) Estimation , 2005 .

[3]  Y. Xing,et al.  Probe Selection and Expression Index Computation of Affymetrix Exon Arrays , 2006, PloS one.

[4]  Michael B. Stadler,et al.  Overestimation of alternative splicing caused by variable probe characteristics in exon arrays , 2009, Nucleic acids research.

[5]  Leslie Wilson,et al.  Differential regulation of microtubule dynamics by three- and four-repeat tau: Implications for the onset of neurodegenerative disease , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[6]  Anne-Mette K. Hein,et al.  Alternative Splicing in Colon, Bladder, and Prostate Cancer Identified by Exon Array Analysis*S , 2008, Molecular & Cellular Proteomics.

[7]  Wen Zhu,et al.  Splicing factors PTBP1 and PTBP2 promote proliferation and migration of glioma cell lines. , 2009, Brain : a journal of neurology.

[8]  J. Castle,et al.  Genome-Wide Survey of Human Alternative Pre-mRNA Splicing with Exon Junction Microarrays , 2003, Science.

[9]  Cheol-Goo Hur,et al.  TISA: tissue-specific alternative splicing in human and mouse genes. , 2006, DNA research : an international journal for rapid publication of reports on genes and genomes.

[10]  Tyson A. Clark,et al.  Alternative splicing and differential gene expression in colon cancer detected by a whole genome exon array , 2006, BMC Genomics.

[11]  Tyson A. Clark,et al.  Discovery of tissue-specific exons using comprehensive human exon microarrays , 2007, Genome Biology.

[12]  T. Godfrey,et al.  Whole genome exon arrays identify differential expression of alternatively spliced, cancer-related genes in lung cancer , 2008, Nucleic acids research.

[13]  Junhee Seok,et al.  Using high-density exon arrays to profile gene expression in closely related species , 2009, Nucleic acids research.