title: What statisticians should know about microarray gene expression technology.
abstract: This chapter briefly reviews how laboratories generate microarray data. This information may give data analysts a better appreciation of the technical sources of variability in the data and the importance of minimizing such variability by normalization methods or exclusion of aberrant arrays.
authors: Functional Genomics Center, University of Rochester, Rochester, NY, USA. Stephen_Welle@urmc.rochester.edu
authors: Stephen
authors: S
authors: Welle
mesh_terms: D000818:Animals; D001483:Base Sequence; D019295:Computational Biology; D017931:DNA Primers / Q000235:genetics; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000379:methods* / Q000592:standards; D006801:Humans; D007091:Image Processing, Computer-Assisted; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods* / Q000592:standards
chemical_list: D017931:DNA Primers