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We exploit two ideas: non-negative matrix factorization methods that create ordered sets of predictors; and statistical testing within ordered sets which is done sequentially, removing the need for correction for multiple testing within the set.<\/jats:p><jats:p>Results: Simulations and theory point to increased statistical power. Computational algorithms are described in detail. The analysis and biological interpretation of a real dataset are given. In addition to the increased power, the benefit of our method is that the organized gene lists are likely to lead better understanding of the biology.<\/jats:p><jats:p>Availability: An SAS JMP executable script is available from<\/jats:p><jats:p>Contact: \u00a0young@niss.org<\/jats:p><jats:p>Supplementary information: \u00a0<\/jats:p>","DOI":"10.1093\/bioinformatics\/btl550","type":"journal-article","created":{"date-parts":[[2006,11,9]],"date-time":"2006-11-09T01:24:51Z","timestamp":1163035491000},"page":"44-49","source":"Crossref","is-referenced-by-count":52,"title":["Inferential, robust non-negative matrix factorization analysis of microarray data"],"prefix":"10.1093","volume":"23","author":[{"given":"Paul","family":"Fogel","sequence":"first","affiliation":[{"name":"Consultant 1 \u00a0 1 \u00a0 \u00a0 4 rue Le Goff, F-75005, Paris, France"}]},{"given":"S. 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