イワモト　タカユキ   Takayuki Iwamoto   岩本　高行    所属   川崎医科大学  医学部 臨床医学　乳腺甲状腺外科学    職種   講師
論文種別	原著
言語種別	英語
査読の有無	査読あり
表題	Bayesian mixture models for assessment of gene differential behaviour and prediction of pCR through the integration of copy number and gene expression data.
掲載誌名	正式名：PloS one 略　 称：PLoS One ISSNコード：19326203/19326203
掲載区分	国外
巻・号・頁	8(7),pp.e68071
著者・共著者	Filippo Trentini, Yuan Ji, Takayuki Iwamoto, Yuan Qi, Lajos Pusztai, Peter Müller
発行年月	2013
概要	We consider modeling jointly microarray RNA expression and DNA copy number data. We propose Bayesian mixture models that define latent Gaussian probit scores for the DNA and RNA, and integrate between the two platforms via a regression of the RNA probit scores on the DNA probit scores. Such a regression conveniently allows us to include additional sample specific covariates such as biological conditions and clinical outcomes. The two developed methods are aimed respectively to make inference on differential behaviour of genes in patients showing different subtypes of breast cancer and to predict the pathological complete response (pCR) of patients borrowing strength across the genomic platforms. Posterior inference is carried out via MCMC simulations. We demonstrate the proposed methodology using a published data set consisting of 121 breast cancer patients.
DOI	10.1371/journal.pone.0068071
PMID	23874497