Article thumbnail

Impact of normalization and filtering on linkage analysis of gene expression data

By Joseph Beyene, Pingzhao Hu, Elena Parkhomenko and David Tritchler

Abstract

Using the Problem 1 data set made available for Genetic Analysis Workshop 15, we assessed sensitivity of linkage results to a correlation-based feature extraction method as well as to different normalization procedures applied to the raw Affymetrix gene expression microarray data. The impact of these procedures on heritability estimates and on expression quantitative trait loci are investigated. The filtering algorithm we propose in this paper ranks genes based on the total absolute correlation of each gene with all other genes on the array and has the potential to extract features that may play role in functional pathways and gene networks. Our results showed that the normalization and filtering algorithms can have a profound influence on genetic analysis of gene expression data

Topics: Proceedings
Publisher: BioMed Central
OAI identifier: oai:pubmedcentral.nih.gov:2367572
Provided by: PubMed Central

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.

Suggested articles

Citations

  1. Affymetrix: Microarray Suite User Guide.
  2. (2004). Cheung V: Genetic analysis of genome-wide variation in human gene expression. Nature
  3. (2006). Global variation in copy number in the human genome. Nature
  4. (2004). Irizarry RA: Preprocessing of oligonucleotide array data. Nat Biotechnol
  5. (2006). Little PF: Normalization procedures and detection of linkage signal in genetical-genomics experiments. Nat Genet
  6. (2002). LR: Merlin-rapid analysis of dense genetic maps using sparse gene flow trees. Nat Genet
  7. (2003). Speed TP: Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics