SCMP913

Analyse d'association du génome entier avec PLINK
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Cours signalées avec « SCMP913 »

SCMP913 Whole-Genome Association Analysis with PLINK (Aug. 2020)

Learn how to perform data cleaning and statistical analysis of large scale genetic data using PLINK and R software. We will use SciNet to run the PLINK analytical workflow to perform markers and individuals quality control, including population stratification and ancestry followed by association analysis between genetic variation and trait of interest. R-software (basic R plotting and ggplot package) will be use to visualize the results.
Enseignant: Camh Team
Catégorie: Scientific Computing
Date de début: : 18 août 2020
Date de fin: : 20 août 2020

SCMP913 PLINK (Ontario Summer School, June 2019)

This 3 hours course will focus of data cleaning and statistical analysis of large scale genetic data using PLINK and R softwares. We will use SciNet to run the PLINK analytical workflow to perform markers and individuals quality control, including population stratification and ancestry followed by association analysis between genetic variation and trait of interest. R-software (basic R plotting and ggplot package) will be use to visualize the results. -- Prerequisite: basic Linux command line skills and R
Enseignant: SciNet Team
Catégorie: Scientific Computing
Date: : lun., 24 juin 2019 - 1:30 pm

SCMP913 PLINK (Ontario Summer School, June 2018)

This 3 hours course will focus of data cleaning and statistical analysis of large scale genetic data using PLINK and R softwares. We will use SciNet to run the PLINK analytical workflow to perform markers and individuals quality control, including population stratification and ancestry followed by association analysis between genetic variation and trait of interest. R-software (basic R plotting and ggplot package) will be use to visualize the results.
Enseignant: Camh Team
Catégorie: Scientific Computing
Date: : mer., 13 juin 2018 - 9:30 am

SCMP913 PLINK (Ontario Summer School, July 2017)

Part of the 2017 Ontario Summer School.
Enseignant: SciNet Team
Catégorie: Scientific Computing
Date: : lun., 24 juil. 2017 - 9:30 am