Soft Windowing Application to Improve Analysis of High-throughput Phenotyping Data.
Fiche publication
Date publication
octobre 2019
Journal
Bioinformatics (Oxford, England)
Auteurs
Membres identifiés du Cancéropôle Est :
Dr SORG Tania, Dr REILLY Patrick
Tous les auteurs :
Haselimashhadi H, Mason JC, Munoz-Fuentes V, López-Gómez F, Babalola K, Acar EF, Kumar V, White J, Flenniken AM, King R, Straiton E, Seavitt JR, Gaspero A, Garza A, Christianson AE, Hsu CW, Reynolds CL, Lanza DG, Lorenzo I, Green JR, Gallegos JJ, Bohat R, Samaco RC, Veeraragavan S, Kim JK, Miller G, Fuchs H, Garrett L, Becker L, Kang YK, Clary D, Cho SY, Tamura M, Tanaka N, Soo KD, Bezginov A, About GB, Champy MF, Vasseur L, Leblanc S, Meziane H, Selloum M, Reilly PT, Spielmann N, Maier H, Gailus-Durner V, Sorg T, Hiroshi M, Yuichi O, Heaney JD, Dickinson ME, Wolfgang W, Tocchini-Valentini GP, Lloyd KCK, McKerlie C, Seong JK, Yann H, de Angelis MH, Brown SDM, Smedley D, Flicek P, Mallon AM, Parkinson H, Meehan TF
Lien Pubmed
Résumé
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 maximises analytic power while minimising noise from unspecified environmental factors.
Référence
Bioinformatics. 2019 Oct 8;: