title: Computational methods for gene expression-based tumor classification.
abstract: Gene expression profiles may offer more or additional information than classic morphologic- and histologic-based tumor classification systems. Because the number of tissue samples examined is usually much smaller than the number of genes examined, efficient data reduction and analysis methods are critical. In this report, we propose a principal component and discriminant analysis method of tumor classification using gene expression profile data. Expression of 2000 genes in 40 tumor and 22 normal colon tissue samples is used to examine the feasibility of gene expression-based tumor classification systems. Using this method, the percentage of correctly classified normal and tumor tissue was 87.0%. The combined approach using principal components and discriminant analysis provided superior sensitivity and specificity compared to an approach using simple differences in the expression levels of individual genes.
authors: University of Texas-Houston Health Science Center, Houston, TX, USA. mxiong@utsph.sph.uth.tmc.edu
authors: M
authors: M
authors: Xiong
authors: L
authors: L
authors: Jin
authors: W
authors: W
authors: Li
authors: E
authors: E
authors: Boerwinkle
mesh_terms: D000704:Analysis of Variance; D003106:Colon / Q000378:metabolism* / Q000473:pathology; D003110:Colonic Neoplasms / Q000145:classification* / Q000235:genetics*; D019295:Computational Biology / Q000379:methods* / Q000706:statistics & numerical data; D016002:Discriminant Analysis; D020869:Gene Expression Profiling / Q000379:methods* / Q000706:statistics & numerical data; D015972:Gene Expression Regulation, Neoplastic / Q000235:genetics; D052138:Genes, Neoplasm / Q000235:genetics; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data; D011897:Random Allocation; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity