title: Local-pooled-error test for identifying differentially expressed genes with a small number of replicated microarrays.
abstract: BACKGROUND
  In microarray studies gene discovery based on fold-change values is often misleading because error variability for each gene is heterogeneous under different biological conditions and intensity ranges. Several statistical testing methods for differential gene expression have been suggested, but some of these approaches are underpowered and result in high false positive rates because within-gene variance estimates are based on a small number of replicated arrays.
  
  
  RESULTS
  We propose to use local-pooled-error (LPE) estimates and robust statistical tests for evaluating significance of each gene's differential expression. Our LPE estimation is based on pooling errors within genes and between replicate arrays for genes in which expression values are similar. We have applied our LPE method to compare gene expression in naïve and activated CD8+ T-cells. Our results show that the LPE method effectively identifies significant differential-expression patterns with a small number of replicated arrays.
  
  
  BACKGROUND
  The methodology is implemented with S-PLUS and R functions available at http://hesweb1.med.virginia.edu/bioinformatics
authors.affiliation: Hospital West Complex, Rm. 3181, P.O. Box 800717, University of Virginia School of Medicine, Charlottesville, VA 22908, USA. nj7w@virginia.edu
authors.forename: Nitin
authors.initials: N
authors.lastname: Jain
authors.forename: Jayant
authors.initials: J
authors.lastname: Thatte
authors.forename: Thomas
authors.initials: T
authors.lastname: Braciale
authors.forename: Klaus
authors.initials: K
authors.lastname: Ley
authors.forename: Michael
authors.initials: M
authors.lastname: O'Connell
authors.forename: Jae K
authors.initials: JK
authors.lastname: Lee
mesh_terms: D000465:Algorithms; D000818:Animals; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D014644:Genetic Variation; D006801:Humans; D051379:Mice; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D018401:Sample Size; D013601:T-Lymphocytes / Q000276:immunology / Q000378:metabolism