R

HiCDOC normalizes intrachromosomal Hi-C matrices, uses unsupervised learning to predict A/B compartments from multiple replicates, and detects significant compartment changes between experiment conditions. It provides a collection of functions assembled into a pipeline to filter and normalize the data, predict the compartments and visualize the results. It accepts several type of data: tabular .tsv files, Cooler .cool or .mcool files, Juicer .hic files or HiC-Pro .matrix and .bed files.

SISIR est un paquet dédié à la réduction de dimension dans des espaces fonctionnels.

GPGame est un paquet dédié à la recherche d’équilibre de jeux (Nash, etc.) par optimisation bayésienne.

Kernel-based methods are powerful methods for integrating heterogeneous types of data. mixKernel provides methods to combine multiple kernels for unsupervised exploratory analysis or to select features in a kernel for unsupervised analysis or kernel-output prediction. For the multiple kernel problem, different solutions have been implemented to obtain a consensus kernel or a kernel that best preserves the original topology of the data. mixKernel also integrates kernel PCA to visualize similarities between samples in a nonlinear space and from the multiple source point of view. Functions to assess and display important variables using permutations are also provided in the package.

GADAG est un paquet dédié à l’apprentissage de graphes acycliques dirigés (DAG) creux.

SOMbrero is an R package that implements stochastic self-organizing maps (SOM) variants for numeric and non numeric datasets.