title: A statistical framework for consolidating "sibling" probe sets for Affymetrix GeneChip data.
abstract: BACKGROUND
  Affymetrix GeneChip typically contains multiple probe sets per gene, defined as sibling probe sets in this study. These probe sets may or may not behave similar across treatments. The most appropriate way of consolidating sibling probe sets suitable for analysis is an open problem. We propose the Analysis of Variance (ANOVA) framework to decide which sibling probe sets can be consolidated.
  
  
  RESULTS
  The ANOVA model allows us to separate the sibling probe sets into two types: those behave similarly across treatments and those behave differently across treatments. We found that consolidation of sibling probe sets of the former type results in large increase in the number of differentially expressed genes under various statistical criteria. The approach to selecting sibling probe sets suitable for consolidating is implemented in R language and freely available from http://research.stowers-institute.org/hul/affy/.
  
  
  CONCLUSIONS
  Our ANOVA analysis of sibling probe sets provides a statistical framework for selecting sibling probe sets for consolidation. Consolidating sibling probe sets by pooling data from each greatly improves the estimates of a gene expression level and results in identification of more biologically relevant genes. Sibling probe sets that do not qualify for consolidation may represent annotation errors or other artifacts, or may correspond to differentially processed transcripts of the same gene that require further analysis.
authors.affiliation: Bioinformatics Center, Stowers Institute for Medical Research, 1000 E 50th St, Kansas City, MO 64110, USA. hul@stowers-institute.org
authors.forename: Hua
authors.initials: H
authors.lastname: Li
authors.forename: Dongxiao
authors.initials: D
authors.lastname: Zhu
authors.forename: Malcolm
authors.initials: M
authors.lastname: Cook
mesh_terms: D000465:Algorithms; D000704:Analysis of Variance; D000818:Animals; D050976:Basic-Leucine Zipper Transcription Factors / Q000172:deficiency / Q000235:genetics; D014408:Biomarkers, Tumor / Q000235:genetics; D001699:Biometry; D016002:Discriminant Analysis; D005136:Eye Proteins / Q000235:genetics; D020869:Gene Expression Profiling / Q000706:statistics & numerical data; D015894:Genome, Human; D023281:Genomics / Q000706:statistics & numerical data; D006801:Humans; D008297:Male; D051379:Mice; D018345:Mice, Knockout; D015336:Molecular Probe Techniques / Q000706:statistics & numerical data; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*; D012985:Software Design; D013091:Spermatogenesis / Q000235:genetics; D013093:Spermatogonia / Q000166:cytology / Q000378:metabolism; D013964:Thyroid Neoplasms / Q000235:genetics
chemical_list: D050976:Basic-Leucine Zipper Transcription Factors; D014408:Biomarkers, Tumor; D005136:Eye Proteins; C084884:Nrl protein, mouse