R packages:


  1. •GeneGeneInteR: The aim of this package is to propose several methods for testing gene-gene interaction in case-control association studies. Such a test can be done by aggregating SNP-SNP interaction tests performed at the SNP level (SSI) or by using gene-gene multidimensionnal methods (GGI) methods. The package also proposes tools for a graphic display of the results.

  2. -References:

  3. ‣M. Emily, N. Sounac, F. Kroell and M. Houée-Bigot (2020), Gene-based methods to detect gene-gene interaction in R: the GeneGeneInteR package, Journal of Statistical Software, Vol. 95, No. 12, pages 1-32.

  4. ‣M. Emily (2016) AGGrEGATOr: A Gene-based GEne-Gene interActTiOn test for case-control association studies, Statistical Application in Genetics and Molecular Biology, Vol. 15(2), pages 151-171.

  5. -[Web] on Github or [Web] on Bioconductor.

  6. •SpatialClustering: The aim of this package is to propose a method for detecting clusters of points in bi-dimensional space. Our method allows to account for a covariate in the clustering. The package proposes a graphical visualization of the clusters.

  7. -Reference: A. Bar-Hen, M. Emily and N. Picard. (2015) Spatial Cluster Detection Using Nearest Neighbour Distance, Spatial Statistics, Vol. 14, pages 400-411.

  8. -[Web] on Github.

  9. •SMILE: The aim of this package is to propose an implementation of the SMILE statistical method. Such a method aims at detecting subpopulation(s) (breed for example) under selection. Based on the haplotype data in a given genomic region, the method computes the d2s dissimilarity and performs a hierarchical clustering to extract the subpopulation under selection.

  10. -Reference: M. Emily, C. Hitte and A. Mom (2016) SMILE: a novel Dissimilarity-based Procedure for Detecting Sparse-Specific Profiles in Sparse Contingency Tables, Computational Statistics and Data Analysis, Vol. 99, pages 171-188.

  11. -[Web] on Github.

  12. •EpiTag: The aim of this package is to propose a method for selecting TagSNPs to optimize power for detecting epistasis.

  13. -Reference: M Emily and C. Friguet (2018) A survey of statistical methods for gene-gene interaction in case-control genome-wide association studies, Journal de la Société Française de Statistique, Vol. 159, No. 2, pages 84-110.

  14. -[Web] on Github.

  15. •IndOR: The aim of this package is to propose an implementation of the IndOR statistical method. Such a method aims at detecting SNPxSNP interaction by testing the independence between Odds-Ratio.

  16. -Reference: M. Emily (2012) IndOR: A new statistical procedure to test for SNP-SNP epistasis in Genome-Wide Association Studies, Statistics In Medicine, Vol. 31, No. 21, pages 2359-2373.

  17. -[Web] on Github.


Metamyl (METapredictor for AMYLoid proteins):


Metamyl is a meta-predictor of the aggregation of proteins or peptides in amyloid fibrils. In more details, Metamyl aims at combining existing methods into a meta-predictor for hot spots prediction, called MetAmyl for METapredictor for AMYLoid proteins. MetAmyl is based on a logistic regression model that aims at weighting predictions from a set of popular algorithms, statistically selected as being the most informative and complementary predictors.

  1. -Reference: M. Emily, A. Talvas and C. Delamarche. (2013) MetAmyl: a METa-predictor for AMYLoid proteins, PLoS One, 8(11): e79722.

  2. -[Web]