000084913 001__ 84913
000084913 005__ 20181203035721.0
000084913 0247_ $$a10.1093/biostatistics/kxj032$$2doi
000084913 037__ $$aARTICLE
000084913 245__ $$aA Laplace mixture model for the identification of differential expression in microarrays
000084913 269__ $$a2006
000084913 260__ $$c2006
000084913 336__ $$aJournal Articles
000084913 520__ $$aMicroarrays have become an important tool for studying the molecular basis of complex disease traits and fundamental biological processes. A common purpose of microarray experiments is the detection of genes that are differentially expressed under two conditions, such as treatment versus control or wild type versus knockout. We introduce a Laplace mixture model as a long-tailed alternative to the normal distribution when identifying differentially expressed genes in microarray experiments, and provide an extension to asymmetric over- or underexpression. This model permits greater flexibility than models in current use as it has the potential, at least with sufficient data, to accommodate both whole genome and restricted coverage arrays. We also propose likelihood approaches to hyperparameter estimation which are equally applicable in the Normal mixture case. The Laplace model appears to give some improvement in fit to data, though simulation studies show that our method performs similarly to several other statistical approaches to the problem of identification of differential expression.
000084913 700__ $$aBhowmick, D.
000084913 700__ $$g111184$$aDavison, A. C.$$0240476
000084913 700__ $$g151741$$aGoldstein, D. R.$$0243936
000084913 700__ $$aRuffieux, Y.
000084913 773__ $$k4$$j7$$tBiostatistics$$q630-641
000084913 8560_ $$falain.borel@epfl.ch
000084913 8564_ $$uhttp://biostatistics.oxfordjournals.org/cgi/content/abstract/7/4/630$$zURL
000084913 909C0 $$xU10124$$0252136$$pSTAT
000084913 909CO $$ooai:infoscience.tind.io:84913$$pSB$$pGLOBAL_SET$$particle
000084913 937__ $$aSTAT-ARTICLE-2006-002
000084913 970__ $$aBhowmick.Davison.Goldstein:2006/STAT
000084913 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000084913 980__ $$aARTICLE