{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,1,5]],"date-time":"2025-01-05T17:40:11Z","timestamp":1736098811685,"version":"3.32.0"},"reference-count":32,"publisher":"Oxford University Press (OUP)","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2006,1,15]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation: The classification of few tissue samples on a very large number of genes represents a non-standard problem in statistics but a usual one in microarray expression data analysis. In fact, the dimension of the feature space (the number of genes) is typically much greater than the number of tissues. We consider high-density oligonucleotide microarray data, where the expression level is associated to an \u2018absolute call\u2019, which represents a qualitative indication of whether or not a transcript is detected within a sample. The \u2018absolute call\u2019 is generally not taken in consideration in analyses.<\/jats:p><jats:p>Results: In contrast to frequently used cluster analysis methods to analyze gene expression data, we consider a problem of classification of tissues and of the variables selection. We adopted methodologies formulated by Ghahramani and Hinton and Rocci and Vichi for simultaneous dimensional reduction of genes and classification of tissues; trying to identify genes (denominated \u2018markers\u2019) that are able to distinguish between two known different classes of tissue samples. In this respect, we propose a generalization of the approach proposed by McLachlan et al. by advising to estimate the distribution of log LR statistic for testing one versus two component hypothesis in the mixture model for each gene considered individually, using a parametric bootstrap approach. We compare conditional (on \u2018absolute call\u2019) and unconditional analyses performed on dataset described in Golub et al. We show that the proposed techniques improve the results of classification of tissue samples with respect to known results on the same benchmark dataset.<\/jats:p><jats:p>Availability: The software of Ghahramani and Hinton is written in Matlab and available in \u2018Mixture of Factor Analyzers\u2019 on while the software of Rocci and Vichi is available upon request from the authors.<\/jats:p><jats:p>Contact: \u00a0francesca.martella@uniroma1.it<\/jats:p>","DOI":"10.1093\/bioinformatics\/bti779","type":"journal-article","created":{"date-parts":[[2005,11,16]],"date-time":"2005-11-16T03:08:21Z","timestamp":1132110501000},"page":"202-208","source":"Crossref","is-referenced-by-count":20,"title":["Classification of microarray data with factor mixture models"],"prefix":"10.1093","volume":"22","author":[{"given":"Francesca","family":"Martella","sequence":"first","affiliation":[{"name":"Dipartimento di Statistica, Probabilit\u00e0 e Statistiche Applicate, Universit\u00e1 degli Studi di Roma \u201cLa Sapienza\u201d \u00a0 P.le A. Moro, 5-00185, Rome, Italy"}]}],"member":"286","published-online":{"date-parts":[[2005,11,15]]},"reference":[{"key":"2023012408301953900_b1","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1038\/35000501","article-title":"Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling","volume":"403","author":"Alizadeh","year":"2000","journal-title":"Nature"},{"key":"2023012408301953900_b2","first-page":"62","article-title":"Iterative Projection Strategies for the Least-squares Fitting of Graph Theoretic Structures to Proximity Data","volume-title":"Research Report RR-94-02","author":"Arabie","year":"1994"},{"key":"2023012408301953900_b3","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1007\/978-1-4615-0873-1_6","article-title":"A method to improve detection of disease using selectively expressed genes in microarray data","volume-title":"Methods of Microarray Data Analysis","author":"Aris","year":"2002"},{"key":"2023012408301953900_b4","doi-asserted-by":"crossref","first-page":"803","DOI":"10.2307\/2532201","article-title":"Model-based Gaussian and non-Gaussian clustering","volume":"49","author":"Banfield","year":"1993","journal-title":"Biometrics"},{"key":"2023012408301953900_b5","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1038\/35020115","article-title":"Molecular classification of cutaneous malignant by gene expression profiling","volume":"406","author":"Bitter","year":"2000","journal-title":"Nature"},{"key":"2023012408301953900_b6","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1093\/bioinformatics\/19.2.185","article-title":"A comparison of normalization methods for high density oligonucleotide array data based on bias and variance","volume":"19","author":"Bolstad","year":"2003","journal-title":"Bioinformatics"},{"key":"2023012408301953900_b7","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1080\/00949659308811525","article-title":"Comparison of the mixture and the classification maximum likelihood in cluster analysis","volume":"47","author":"Celeux","year":"1993","journal-title":"J. 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