{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T15:23:46Z","timestamp":1775921026335,"version":"3.50.1"},"reference-count":24,"publisher":"Oxford University Press (OUP)","issue":"8","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2006,4,15]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation: We propose a new model-based technique for summarizing high-density oligonucleotide array data at probe level for Affymetrix GeneChips. The new summarization method is based on a factor analysis model for which a Bayesian maximum a posteriori method optimizes the model parameters under the assumption of Gaussian measurement noise. Thereafter, the RNA concentration is estimated from the model. In contrast to previous methods our new method called \u2018Factor Analysis for Robust Microarray Summarization (FARMS)\u2019 supplies both P-values indicating interesting information and signal intensity values.<\/jats:p><jats:p>Results: We compare FARMS on Affymetrix's spike-in and Gene Logic's dilution data to established algorithms like Affymetrix Microarray Suite (MAS) 5.0, Model Based Expression Index (MBEI), Robust Multi-array Average (RMA). Further, we compared FARMS with 43 other methods via the \u2018Affycomp II\u2019 competition. The experimental results show that FARMS with default parameters outperforms previous methods if both sensitivity and specificity are simultaneously considered by the area under the receiver operating curve (AUC). We measured two quantities through the AUC: correctly detected expression changes versus wrongly detected (fold change) and correctly detected significantly different expressed genes in two sets of arrays versus wrongly detected (P-value). Furthermore FARMS is computationally less expensive then RMA, MAS and MBEI.<\/jats:p><jats:p>Availability: The FARMS R package is available from<\/jats:p><jats:p>Contact: \u00a0hochreit@bioinf.jku.at<\/jats:p><jats:p>Supplementary information: \u00a0<\/jats:p>","DOI":"10.1093\/bioinformatics\/btl033","type":"journal-article","created":{"date-parts":[[2006,2,11]],"date-time":"2006-02-11T01:23:56Z","timestamp":1139621036000},"page":"943-949","source":"Crossref","is-referenced-by-count":204,"title":["A new summarization method for affymetrix probe level data"],"prefix":"10.1093","volume":"22","author":[{"given":"Sepp","family":"Hochreiter","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, Technische Universit\u00e4t Berlin 1 \u00a0 1 \u00a0 \u00a0 10587 Berlin, Germany"},{"name":"Institute of Bioinformatics, Johannes Kepler Universit\u00e4t Linz 2 \u00a0 2 \u00a0 \u00a0 4040 Linz, Austria"}]},{"given":"Djork-Arn\u00e9","family":"Clevert","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, Technische Universit\u00e4t Berlin 1 \u00a0 1 \u00a0 \u00a0 10587 Berlin, Germany"}]},{"given":"Klaus","family":"Obermayer","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, Technische Universit\u00e4t Berlin 1 \u00a0 1 \u00a0 \u00a0 10587 Berlin, Germany"}]}],"member":"286","published-online":{"date-parts":[[2006,2,10]]},"reference":[{"key":"2023012409210335900_b1","article-title":"Affymetrix","author":"Microarray Suite User Guide.","year":"2001"},{"key":"2023012409210335900_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":"2023012409210335900_b3","doi-asserted-by":"crossref","first-page":"R16.1","DOI":"10.1186\/gb-2005-6-2-r16","article-title":"Preferred analysis methods for Affymetrix GeneChips revealed by a wholly defined control dataset","volume":"6","author":"Choe","year":"2005","journal-title":"Genome Biol."},{"key":"2023012409210335900_b4","doi-asserted-by":"crossref","first-page":"research0005.1","DOI":"10.1186\/gb-2001-3-1-research0005","article-title":"Assessment of the relationship between signal transcript concentration for Affymetrix GeneChip arrays","volume":"3","author":"Chudin","year":"2001","journal-title":"Genome Biol."},{"key":"2023012409210335900_b5","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1093\/bioinformatics\/btg410","article-title":"A benchmark for Affymetrix GeneChip expression measures","volume":"20","author":"Cope","year":"2004","journal-title":"Bioinformatics"},{"key":"2023012409210335900_b6","doi-asserted-by":"crossref","first-page":"210.1","DOI":"10.1186\/gb-2003-4-4-210","article-title":"Statistical tests for differential expression in cDNA microarray experiments","volume":"4","author":"Cui","year":"2003","journal-title":"Genome Biol."},{"key":"2023012409210335900_b7","volume-title":"Optimal Statistical Decisions.","author":"DeGroot","year":"1970"},{"key":"2023012409210335900_b8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","article-title":"Maximum likelihood from incomplete data via the EM algorithm","volume":"39","author":"Dempster","year":"1977","journal-title":"J. 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