Code
My code is hosted on GitHub
Things you'll find there:
Kernel post-selection inference
Performing post-selection inference on kernel selection. The code, developed mainly by Lotfi Slim, is available on CRAN.
Reference:
Lotfi Slim, Clément Chatelain, Chloé-Agathe Azencott, and Jean-Philippe Vert.KernelPSI: a Post-Selection Inference Framework for Nonlinear Variable Selection, ICML, 2019.
Robust epistasis detection
Functions to perform robust epistasis detection in genome-wide association studies. The code, developed mainly by Lotfi Slim, is available on CRAN.
Reference:
Lotfi Slim, Clément Chatelain, Chloé-Agathe Azencott, and Jean-Philippe Vert. Novel methods for epistasis detection in Genome-Wide Association Studies, BioRxiv, 2018.
GWAS incorporating networks in R
A BioConductor package for using biological networks to guide GWAS, based on SConES (see below). This package was developed mainly by Héctor Climente.
Multitask Lasso with task descriptors
A multi-task Lasso approach that makes use of task descriptors. The code, developed by Víctor Bellón, is available on GitHub.
Reference:
Víctor Bellón, Véronique Stoven, and Chloé-Agathe Azencott. Multitask feature selection with task descriptors, Pacific Symposium on Biocomputing, 2016, 21:261—272 [pdf].
dmGWAS_2.3.1
Available here, a version of dmGWAS_2.3 that is compatible with igraph0.7
SConES: Selecting Connected Explanatory SNPs
SConES is a network-guided approach for analyzing genome-wide data. It allows for the discovery of multiple genetic loci that are maximally associated with a phenotype, while tending to be connected on a given biological network. This network can be constructed for example from a gene-gene interaction network (based on proximity), or in any way such that you believe that neighboring SNPs should tend to be selected together.
Reference:
Chloé-Agathe Azencott, Dominik Grimm, Mahito Sugiyama, Yoshinobu Kawahara, and Karsten M. Borgwardt. Efficient network-guided multi-locus association mapping with graph cuts, Bioinformatics, 2013, 29(13): i171—i179 DOI: 10.1093/bioinformatics/btt238 (Open Access)
Code:
Matlab code developed by Dominik Grimm, Yoshinobu Kawahara and myself is available on GitHub.
SConES is also available as part of EasyGWAS, a framework for the analysis and meta-analysis of GWAS data (with Python interfaces).
R code for Multi-Scones, developed by Mahito Sugiyama, is available on GitHub.
GLIDE: GPU-based LInear Detection of Epistasis
GLIDE is a GPU-based approach for the detection of epistasis in genome-wide data. It allows for the systematic computation of a linear regression between pairs of genetic loci and a phenotype.
Reference:
Tony Kam-Thong*, Chloé-Agathe Azencott*, Lawrence Cayton, Benno Pütz, André Altmann, Nazanin Karbalai, Philipp G. Sämann, Bernhard Schölkopf, Betram Müller-Myhsok, and Karsten M. Borgwardt. GLIDE: GPU-based linear regression for the detection of epistasis. Human Heredity, 2012, 73:220—236. DOI: 10.1159/000341885 [pdf]
Code:
CUDA code (for execution on NVIDIA GPUs) developed by Tony Kam-Thong and myself is available on GitHub.
An additional How To as well as (Python and bash) scripts for working with GLIDE, that I developed in the context of a case study, are available on GitHub.
