A gene expression bar code for microarray data.
The ability to measure genome-wide expression holds great promise for characterizing cells and distinguishing diseased from normal tissues. Thus far, microarray technology has been useful only for measuring relative expression between two or more samples, which has handicapped its ability to classify tissue types. Here we present a method that can successfully predict tissue type based on data from a single hybridization. A preliminary web-tool is available online (http://rafalab.jhsph.edu/barcode/).
Zilliox
Michael J
MJ
W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street, Baltimore, Maryland 21205, USA.
Irizarry
Rafael A
RA
D000465:Algorithms; D000818:Animals; D001943:Breast Neoplasms / Q000175:diagnosis / Q000235:genetics; D019295:Computational Biology / Q000379:methods; D030541:Databases, Genetic; D001330:Electronic Data Processing / Q000379:methods*; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods* / Q000706:statistics & numerical data; D016678:Genome / Q000235:genetics*; D006801:Humans; D020407:Internet; D051379:Mice; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods* / Q000706:statistics & numerical data; D011379:Prognosis