Dataset Information

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

ABSTRACT:

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.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Haselimashhadi H

PROVIDER: S-EPMC7115897 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Haselimashhadi Hamed H Mason Jeremy C JC Munoz-Fuentes Violeta V López-Gómez Federico F Babalola Kolawole K Acar Elif F EF Kumar Vivek V White Jacqui J Flenniken Ann M AM King Ruairidh R Straiton Ewan E Seavitt John Richard JR Gaspero Angelina A Garza Arturo A Christianson Audrey E AE Hsu Chih-Wei CW Reynolds Corey L CL Lanza Denise G DG Lorenzo Isabel I Green Jennie R JR Gallegos Juan J JJ Bohat Ritu R Samaco Rodney C RC Veeraragavan Surabi S Kim Jong Kyoung JK Miller Gregor G Fuchs Helmult H Garrett Lillian L Becker Lore L Kang Yeon Kyung YK Clary David D Cho Soo Young SY Tamura Masaru M Tanaka Nobuhiko N Soo Kyung Dong KD Bezginov Alexandr A About Ghina Bou GB Champy Marie-France MF Vasseur Laurent L Leblanc Sophie S Meziane Hamid H Selloum Mohammed M Reilly Patrick T PT Spielmann Nadine N Maier Holger H Gailus-Durner Valerie V Sorg Tania T Hiroshi Masuya M Yuichi Obata O Heaney Jason D JD Dickinson Mary E ME Wolfgang Wurst W Tocchini-Valentini Glauco P GP Lloyd Kevin C Kent KCK McKerlie Colin C Seong Je Kyung JK Yann Herault H de Angelis Martin Hrabé MH Brown Steve D M SDM Smedley Damian D Flicek Paul P Mallon Ann-Marie AM Parkinson Helen H Meehan Terrence F TF

Bioinformatics (Oxford, England) 20200301 5

<h4>Motivation</h4>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.<h4>Results</h4>Here we introduce 'soft windowing', a methodological approach that selects a window of time that includes the most appropriate contr ...[more]

PMID: 31591642

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Dataset Information

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

Motivation

Results

Availability and implementation

Supplementary information

Publications

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

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