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Furthermore, many investigations ask complex biological questions by studying multiple interrelated experimental conditions. Therefore, there is a need in the field for generic statistical models to quantify protein levels even in complex study designs.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>We propose a general statistical modeling approach for protein quantification in arbitrary complex experimental designs, such as time course studies, or those involving multiple experimental factors. The approach summarizes the quantitative experimental information from all the features and all the conditions that pertain to a protein. It enables both protein significance analysis between conditions, and protein quantification in individual samples or conditions. We implement the approach in an open-source R-based software package  suitable for researchers with a limited statistics and programming background.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>We demonstrate, using as examples two experimental investigations with complex designs, that a simultaneous statistical modeling of all the relevant features and conditions yields a higher sensitivity of protein significance analysis and a higher accuracy of protein quantification as compared to commonly employed alternatives. The software is available at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"http:\/\/www.stat.purdue.edu\/~ovitek\/Software.html\" ext-link-type=\"uri\">http:\/\/www.stat.purdue.edu\/~ovitek\/Software.html<\/jats:ext-link>.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/1471-2105-13-s16-s6","type":"journal-article","created":{"date-parts":[[2012,11,5]],"date-time":"2012-11-05T11:15:25Z","timestamp":1352114125000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":112,"title":["Statistical protein quantification and significance analysis in label-free LC-MS experiments with complex designs"],"prefix":"10.1186","volume":"13","author":[{"given":"Timothy","family":"Clough","sequence":"first","affiliation":[]},{"given":"Safia","family":"Thaminy","sequence":"additional","affiliation":[]},{"given":"Susanne","family":"Ragg","sequence":"additional","affiliation":[]},{"given":"Ruedi","family":"Aebersold","sequence":"additional","affiliation":[]},{"given":"Olga","family":"Vitek","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2012,11,5]]},"reference":[{"key":"5426_CR1","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1146\/annurev-biochem-061308-093216","volume":"80","author":"J Cox","year":"2011","unstructured":"Cox J, Mann M: Quantitative, high-resolution proteomics for data-driven systems biology. 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