{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T23:42:38Z","timestamp":1773877358819,"version":"3.50.1"},"reference-count":8,"publisher":"Oxford University Press (OUP)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2006,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: A classification algorithm, based on a multi-chip, multi-SNP approach is proposed for Affymetrix SNP arrays. Current procedures for calling genotypes on SNP arrays process all the features associated with one chip and one SNP at a time. Using a large training sample where the genotype labels are known, we develop a supervised learning algorithm to obtain more accurate classification results on new data. The method we propose, RLMM, is based on a robustly fitted, linear model and uses the Mahalanobis distance for classification. The chip-to-chip non-biological variance is reduced through normalization. This model-based algorithm captures the similarities across genotype groups and probes, as well as across thousands of SNPs for accurate classification. In this paper, we apply RLMM to Affymetrix 100 K SNP array data, present classification results and compare them with genotype calls obtained from the Affymetrix procedure DM, as well as to the publicly available genotype calls from the HapMap project.<\/jats:p>\n               <jats:p>Availability: The RLMM software is implemented in R and is available from Bioconductor or from the first author at nrabbee@post.harvard.edu.<\/jats:p>\n               <jats:p>Contact: \u00a0nrabbee@stat.berkeley.edu<\/jats:p>\n               <jats:p>Supplementary information: \u00a0<\/jats:p>","DOI":"10.1093\/bioinformatics\/bti741","type":"journal-article","created":{"date-parts":[[2005,11,3]],"date-time":"2005-11-03T01:13:48Z","timestamp":1130980428000},"page":"7-12","source":"Crossref","is-referenced-by-count":292,"title":["A genotype calling algorithm for affymetrix SNP arrays"],"prefix":"10.1093","volume":"22","author":[{"given":"Nusrat","family":"Rabbee","sequence":"first","affiliation":[{"name":"Department of Statistics, University of California 1 \u00a0 1 \u00a0 \u00a0 Berkeley, CA, USA"}]},{"given":"Terence P.","family":"Speed","sequence":"additional","affiliation":[{"name":"Department of Statistics, University of California 1 \u00a0 1 \u00a0 \u00a0 Berkeley, CA, USA"},{"name":"Walter and Eliza Hall Institute of Medical Research 2 \u00a0 2 \u00a0 \u00a0 Melbourne, Australia"}]}],"member":"286","published-online":{"date-parts":[[2005,11,2]]},"reference":[{"key":"2023012408333038100_b1","article-title":"GeneChip\u00ae Human Mapping 100 K Set","author":"Affymetrix, Inc.","year":"2005"},{"key":"2023012408333038100_b2","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 variance and bias","volume":"19","author":"Bolstad","year":"2003","journal-title":"Bioinformatics"},{"key":"2023012408333038100_b3","doi-asserted-by":"crossref","first-page":"1958","DOI":"10.1093\/bioinformatics\/bti275","article-title":"Dynamic model based algorithms for screening and genotyping over 100 K SNPs on oligonucleotide microarrays","volume":"21","author":"Di","year":"2005","journal-title":"Bioinformatics"},{"key":"2023012408333038100_b4","doi-asserted-by":"crossref","first-page":"789","DOI":"10.1038\/nature02168","article-title":"The international hapmap consortium","volume":"426","author":"HapMap","year":"2003","journal-title":"Nature"},{"key":"2023012408333038100_b5","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1093\/biostatistics\/4.2.249","article-title":"Exploration, normalization, and summaries of high hensity oligonucleotide array probe level data","volume":"4","author":"Irizarry","year":"2003","journal-title":"Biostatistics"},{"key":"2023012408333038100_b6","doi-asserted-by":"crossref","first-page":"1233","DOI":"10.1038\/nbt869","article-title":"Large-scale genotyping of complex DNA","volume":"21","author":"Kennedy","year":"2003","journal-title":"Nat. Biotechnol"},{"key":"2023012408333038100_b7","doi-asserted-by":"crossref","first-page":"2397","DOI":"10.1093\/bioinformatics\/btg332","article-title":"Algorithms for large-scale genotyping microarrays","volume":"19","author":"Liu","year":"2003","journal-title":"Bioinformatics"},{"key":"2023012408333038100_b8","volume-title":"Linear Statistical Inference and Its Applications","author":"Rao","year":"2002","edition":"2nd edn"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/22\/1\/7\/48838825\/bioinformatics_22_1_7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/22\/1\/7\/48838825\/bioinformatics_22_1_7.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,24]],"date-time":"2023-01-24T08:36:50Z","timestamp":1674549410000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/22\/1\/7\/218566"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2005,11,2]]},"references-count":8,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2006,1,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/bti741","relation":{},"ISSN":["1367-4811","1367-4803"],"issn-type":[{"value":"1367-4811","type":"electronic"},{"value":"1367-4803","type":"print"}],"subject":[],"published-other":{"date-parts":[[2006,1,1]]},"published":{"date-parts":[[2005,11,2]]}}}