Article
Soft windowing application to improve analysis of high-throughput phenotyping data.
Bioinformatics (Oxford, England) - 1 Mar 2020
Haselimashhadi Hamed, Mason Jeremy C, Munoz-Fuentes Violeta, López-Gómez Federico, Babalola Kolawole, Acar Elif F, Kumar Vivek, White Jacqui, Flenniken Ann M, King Ruairidh, Straiton Ewan, Seavitt John Richard, Gaspero Angelina, Garza Arturo, Christianson Audrey E, Hsu Chih-Wei, Reynolds Corey L, Lanza Denise G, Lorenzo Isabel, Green Jennie R, Gallegos Juan J, Bohat Ritu, Samaco Rodney C, Veeraragavan Surabi, Kim Jong Kyoung, Miller Gregor, Fuchs Helmult, Garrett Lillian, Becker Lore, Kang Yeon Kyung, Clary David, Cho Soo Young, Tamura Masaru, Tanaka Nobuhiko, Soo Kyung Dong, Bezginov Alexandr, About Ghina Bou, Champy Marie-France, Vasseur Laurent, Leblanc Sophie, Meziane Hamid, Selloum Mohammed, Reilly Patrick T, Spielmann Nadine, Maier Holger, Gailus-Durner Valerie, Sorg Tania, Hiroshi Masuya, Yuichi Obata, Heaney Jason D, Dickinson Mary E, Wolfgang Wurst, Tocchini-Valentini Glauco P, Lloyd Kevin C Kent, McKerlie Colin, Seong Je Kyung, Yann Herault, de Angelis Martin Hrabé, Brown Steve D M, Smedley Damian, Flicek Paul, Mallon Ann-Marie, Parkinson Helen, Meehan Terrence F
Abstract excerpt
MOTIVATION: High-throughput phenomic projects generate complex data from small treatment and large control groups that increase the power of the analyses but introduce variation over time. A method is needed to utlize a set of temporally local controls that maximizes analytic power while minimizing noise from unspecified environmental factors. RESULTS: Here we introduce 'soft windowing', a methodological approach...
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