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

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;: