Privé, Florian, et al. “Efficient toolkit implementing best practices for principal component analysis of population genetic data.” BioRxiv (2019): 841452. [Preprint]

Privé, Florian, et al. “Making the most of Clumping and Thresholding for polygenic scores.” The American Journal of Human Genetics 105.6 (2019): 1213-1221. [Open access (unformatted)]

Privé, Florian, Hugues Aschard, and Michael GB Blum. “Efficient implementation of penalized regression for genetic risk prediction.” Genetics (2019). [Open access]

Privé, Florian, et al. “Efficient analysis of large-scale genome-wide data with two R packages: bigstatsr and bigsnpr.” Bioinformatics (2018). [Open access]

Rencontres R 2018: The R package bigstatsr: Memory- and Computation-Efficient Statistical Tools for Big Matrices [Slides]

eRum 2018: An R package for statistical tools with big matrices stored on disk. [Recording] [Slides]

Recomb-Genetics 2018: Predicting complex diseases: performance and robustness. [Slides]

LIFE 2018: Predicting complex diseases: performance and robustness. [Slides]

hackseq 2017: Developing advanced R tutorials for genomic data analysis. [Website]

useR!2017: The R package bigstatsr: Memory- and Computation-Efficient Tools for Big Matrices. [Recording] [Slides]

ALT’2016: Goodness-of-fit tests for the Weibull distribution with censored data. [Slides]

You can reuse (and modify) some of my presentations as long as you give attribution (just as in open source licensing).

Penalized methods for genetic data analysis (King’s College London)

Efficient analysis of large-scale matrices with two R packages: bigstatsr and bigsnpr: presentation + exercise (State of The R RUG)

Efficient statistical tools for analyzing omics data, with a focus on polygenic risk prediction (Aarhus, Denmark)

High-dimensional data: a different kind of big data (Grenoble Data Club)

The R package {bigstatsr}: memory- and computation-efficient tools for big matrices stored on disk (Grenoble RUG)

courses and practical exercises in Mathematics for students in their first semester after high school (128H in two years)

advanced R course for PhD students (57H in two years)

introductory R course for PhD students (21H)

statistical methods for first year engineer students (third year after high school) at ENSIMAG (18H)

software carpentry instructor – introductory R course (7H)