Vous trouverez ci-dessous l'ensemble des logiciels développés dans l'unité MIA Paris Saclay.


This package corresponds to the implementation of the robust approach for estimating change-points in the mean of an AR(1) Gaussian process by using the methodology described in the paper arXiv 1403.1958



Segmentation methods for array CGH analysis

Référence : 
Picard F, Lebarbier E, Hoebeke M, Rigaill G, Thiam B, Robin S (2011) Joint segmentation calling and normalization of multiple CGH profiles, Biostatistics, vol. 12 pp.413-428



C'est un package R dédié à la détection de régions corrélées dans des données d’expression, prenant en compte des variations du nombre de copies, disponible sur le CRAN.

Référence : 
Delatola, E. I., Lebarbier, E., Mary-Huard, T., Radvanyi, F., Robin, S., & Wong, J. (2017). SegCorr a statistical procedure for the detection of genomic regions of correlated expression. BMC bioinformatics, 18(1), 1-15.



This is a package for the exact and fast segmentation of large profiles

Référence : 
Cleynen A, Koskas M, Lebarbier E, Rigaill G, Robin S (2013) Segmentor3IsBack: an R package for the fast and exact segmentation of Seq-data, Algorithm for Molecular Biology, submitted 



shinySbm is a R package containing a shiny application. This application provides a user-friendly interface for network analysis based on the sbm package made by Chiquet J, Donnet S and Barbillon P (2023) CRAN. The sbm package regroups into a unique framework tools for estimating and manipulating variants of the stochastic block model. shinySbm allows you to easily apply and explore the outputs of a Stochastic Block Model without programming. It is useful if you want to analyze your network data (adjacency matrix or list of edges) without knowing the R language or to learn the basics of the sbm package.

Stochastic block models (SBMs) are probabilistic models in statistical analysis of graphs or networks, that can be used to discover or understand the (hidden/latent) structure of a network, as well as for clustering purposes.

Stochastic Block Models are applied on network to simplify the information they gather, and help visualize the main behaviours/categories/relationships present in your network. It’s a latent model which identify significant blocks (groups) of nodes with similar connectivity patterns. This could help you to know if your network: hides closed sub-communities, is hierarchical, or has another specific structure.

With shinySbm you should also be able to:

  • Easily run a Stochastic Block Model (set your model, infer associated parameters and choose the number of blocks)
  • Get some nice outputs as matrix and network plots organized by blocks
  • Get a summary of the modelling
  • Extract lists of nodes associated with their blocks


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