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Comparison of Two Methods for Detecting Alternative Splice Variants Using GeneChip(®) Exon Arrays

The Affymetrix GeneChip Exon Array can be used to detect alternative splice variants. Microarray Detection of Alternative Splicing (MIDAS) and Partek(®) Genomics Suite (Partek(®) GS) are among the most popular analytical methods used to analyze exon array data. While both methods utilize statistical...

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Detalles Bibliográficos
Autores principales: Fan, Wenhong, Stirewalt, Derek L., Radich, Jerald P., Zhao, Lueping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Master Publishing Group 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3614835/
https://www.ncbi.nlm.nih.gov/pubmed/23675234
Descripción
Sumario:The Affymetrix GeneChip Exon Array can be used to detect alternative splice variants. Microarray Detection of Alternative Splicing (MIDAS) and Partek(®) Genomics Suite (Partek(®) GS) are among the most popular analytical methods used to analyze exon array data. While both methods utilize statistical significance for testing, MIDAS and Partek(®) GS could produce somewhat different results due to different underlying assumptions. Comparing MIDAS and Partek(®) GS is quite difficult due to their substantially different mathematical formulations and assumptions regarding alternative splice variants. For meaningful comparison, we have used the previously published generalized probe model (GPM) which encompasses both MIDAS and Partek(®) GS under different assumptions. We analyzed a colon cancer exon array data set using MIDAS, Partek(®) GS and GPM. MIDAS and Partek(®) GS produced quite different sets of genes that are considered to have alternative splice variants. Further, we found that GPM produced results similar to MIDAS as well as to Partek(®) GS under their respective assumptions. Within the GPM, we show how discoveries relating to alternative variants can be quite different due to different assumptions. MIDAS focuses on relative changes in expression values across different exons within genes and tends to be robust but less efficient. Partek(®) GS, however, uses absolute expression values of individual exons within genes and tends to be more efficient but more sensitive to the presence of outliers. From our observations, we conclude that MIDAS and Partek(®) GS produce complementary results, and discoveries from both analyses should be considered.