Soft windowing application to improve analysis of high-throughput phenotyping data.
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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
Аннотация
RESULTS: Here we introduce 'soft windowing', a methodological approach that selects a window of time that includes the most appropriate controls for analysis. Using phenotype data from the International Mouse Phenotyping Consortium (IMPC), adaptive windows were applied such that control data collected proximally to mutants were assigned the maximal weight, while data collected earlier or later had less weight. We applied this method to IMPC data and compared the results with those obtained from a standard non-windowed approach. Validation was performed using a resampling approach in which we demonstrate a 10% reduction of false positives from 2.5 million analyses. We applied the method to our production analysis pipeline that establishes genotype-phenotype associations by comparing mutant versus control data. We report an increase of 30% in significant P-values, as well as linkage to 106 versus 99 disease models via phenotype overlap with the soft-windowed and non-windowed approaches, respectively, from a set of 2082 mutant mouse lines. Our method is generalizable and can benefit large-scale human phenomic projects such as the UK Biobank and the All of Us resources.
AVAILABILITY AND IMPLEMENTATION: The method is freely available in the R package SmoothWin, available on CRAN http://CRAN.R-project.org/package=SmoothWin.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.