2026-04-28T19:17:34Zhttps://uvadoc.uva.es/oai/request

oai:uvadoc.uva.es:10324/229362021-06-23T10:10:52Zcom_10324_1151com_10324_931com_10324_894col_10324_1280

Larriba González, Yolanda Rueda Sabater, María Cristina Fernández Temprano, Miguel Alejandro Peddada, Shyamal 2017-03-31T11:15:12Z 2017-03-31T11:15:12Z 2016 http://uvadoc.uva.es/handle/10324/22936 High-throughput microarray technologies are a widely used research tool in gene expression analysis. A large variety of preprocessing methods for raw intensity measures is available to establish gene expression values. Normalization is the key stage in preprocessing methods, since it removes systematic variations in microarray data. Then, the choice of the normalization strategy can make a substantial impact to the final results. Additionally, we have observed that the identification of rhythmic circadian genes depends not only on the normalization strategy but also on the rhythmicity detection algorithm employed. We analyze three different rhythmicity detection algorithms. On the one hand, JTK and RAIN which are widely extended among biologists. On the other hand, ORIOS, a novel statistical methodology which heavily relies on Order Restricted Inference and that we propose to detect rhythmic signal for Oscillatory Systems. Results on the determination of circadian rhythms are compared using artificial microarray data and publicly available circadian data bases. application/pdf eng info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-nd/4.0/ Attribution-NonCommercial-NoDerivatives 4.0 International Influence of microarray normalization strategies and rhythmicity detection algorithms to detect circadian rhythms 9th International Conference of the ERCIM Working Group on Computational and Methodological Statistics info:eu-repo/semantics/conferenceObject