Soft windowing application to improve analysis of high-throughput phenotyping data

Hamed Haselimashhadi , Jeremy Mason , Violeta Munoz-Fuentes , Federico López-Gómez , Kolawole Babalola , Elif Acar , Vivek Kumar , Jacqui White , Ann Flenniken , Ruairidh King , Ewan Straiton , John Richard Seavitt , Angelina Gaspero , Arturo Garza , Audrey Christianson , Chih-Wei Hsu , Corey Reynolds , Denise Lanza , Isabel Lorenzo , Jennie Green , Juan Gallegos , Ritu Bohat , Rodney Samaco , Surabi Veeraragavan , Jong Kyoung Kim , Gregor Miller , Helmult Fuchs , Lillian Garrett , Lore Becker , Yeon Kyung Kang , David Clary , Soo Young Cho , Masaru Tamura , Nobuhiko Tanaka , Kyung Dong Soo , Alexandr Bezginov , Ghina Bou About (1) , Marie-France Champy (1) , Laurent Vasseur (1) , Sophie Leblanc (1) , Hamid Meziane (1) , Mohammed Selloum (1) , Patrick Reilly (1) , Nadine Spielmann , Holger Maier , Valerie Gailus-Durner , Tania Sorg (1) , Masuya Hiroshi , Obata Yuichi , Jason Heaney , Mary Dickinson , Wurst Wolfgang , Glauco Tocchini-Valentini , Kevin Lloyd , Colin Mckerlie , Je Kyung Seong , Herault Yann (1) , Martin Hrabé de Angelis , Steve Brown , Damian Smedley , Paul Flicek , Ann-Marie Mallon , Helen Parkinson , Terrence Meehan

1 IGBMC - Institut de Génétique et de Biologie Moléculaire et Cellulaire

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 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

Domaines: Bio-Informatique, Biologie Systémique [q-bio.QM]; Bio-informatique [q-bio.QM]

Hamed Haselimashhadi, Jeremy Mason, Violeta Munoz-Fuentes, Federico López-Gómez, Kolawole Babalola, et al.. Soft windowing application to improve analysis of high-throughput phenotyping data. Bioinformatics, 2020, 36 (5), pp.1492-1500. ⟨10.1093/bioinformatics/btz744⟩. ⟨hal-03065532⟩