Statistics and machine learning
mixOmics: mixOmics offers a wide range of multivariate methods for the exploration and integration of biological datasets with a particular focus on variable selection. (GPL >=2)
GEMS-AI: Machine Learning Algorithms are trained on database to provide accurate predictions. But the main issue is how far can we trust them ? Can we certify the behaviour of these algorithms ? How can we be sure that the outcome of the algorithms will be reliable and unbiased ? This need for explanation of AI-based algorithms is at the core of GEMS-AI. (GPL3)
Jointseg: Methods for fast segmentation of multivariate signals into piecewise constant profiles and for generating realistic copy-number profiles. A typical application is the joint segmentation of total DNA copy numbers and allelic ratios obtained from Single Nucleotide Polymorphism (SNP) microarrays in cancer studies. The methods are described in Pierre-Jean, Rigaill and Neuvial (2015) <doi:10.1093/bib/bbu026>. (LGPL-2.1 | LGPL-3)
adjclust: Implements a constrained version of hierarchical agglomerative clustering, in which each observation is associated to a position, and only adjacent clusters can be merged. Typical use cases include Genome-Wide Association Studies or Hi-C data analysis, where the similarity between items is a decreasing function of their genomic distance. The implemented algorithm is time and memory efficient, see Ambroise et al (2019) <doi:10.1186/s13015-019-0157-4>. (GPL-3)
sanssouci: The goal of sanssouci is to perform post hoc inference: in a multiple testing context, sanssouci provides statistical guarantees on possibly user-defined and/or data-driven sets of hypotheses. Typical use cases include differential gene expression (DGE) studies in genomics, and fMRI studies in neuroimaging. (GPL-3)
coordinate layout: Plugin for cytoscape 3.0 to explicitly represent the graph nodes at pre-defined coordinates.
LargeGraphCenterEstimator: Python package to estimate the barycenter of large weighted graphs.
CoSeq: Bioconductor package for high-throughput sequencing data analysis.
SelVarMix: R package for variable selection in supervised and unsupervised classification.
MaskMeans: Multi-view agglomeration/splitting K-means clustering algorithm.
W2reg: Train neural networks classifiers (in PyTorch) with Wasserstein-2 regularization to reduce algorithmic bias in future predictions
Signal and Image processing
FitEllipsoid: Java plug-in for ICY (free software). Image processing and ellipsoid delineation.
Utilzreg: Image registration algorithms for 2D and 3D images. Includes implementations of the "LDDMM", "Geodesic Shooting" and "LogDemons" algorithms. C++ implementation with OpenMP parallelisation.
Geoshoot: Diffeomorphic image registration using the Geodesic Shooting approach. C++ code with GPU parallelisation in OpenCL. (MIT License)
Fast Transform Learning: Matlab package to illustrate the effectiveness of a deep representation method for processing images of cancer in a dozen organs.
Regularized non-local total variation: Matlab Package for image restoration.
SlicerRegularizedFastMarching: Slicer3D package for semi-interactive 3D image segmentation.
GPELAB: Matlab Toolbox for the simulation of quantic systems.
Getfem++: Finite element library in C++. Contribution to Fluid (Navier-Stokes), fictitious domain methods, domain decomposition (Parallel implementation).
DassFlow-Py: Python code for estimating bedrock topography under polar ice caps (Antarctica).