Software

R and Python packages (maintainer)

SOMbrero

SOMbrero is an R package that implements stochastic self-organizing maps (SOM) variants for numeric and non numeric datasets.
Project home page on CRAN (since 2015): https://cran.r-project.org/package=SOMbrero
Project website: http://sombrero.clementine.wf/
Project repositories: Forge INRAE and Github
SOMbrero comes with a Web User Interface (using shiny) that can be used by typing the command line: 
 sombreroWUI()
or tested online at https://sombrero.sk8.inrae.fr/

If you are using SOMbrero, please cite:
  • Villa-Vialaneix N. (2017) Stochastic self-organizing map variants with the R package SOMbrero. Proceedings of the 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM 2017), 1-7.
  • Olteanu M., Villa-Vialaneix N. (2015) On-line relational and multiple relational SOM. Neurocomputing, 147, 15-30. DOI: 10.1016/j.neucom.2013.11.047
  • Mariette J., Rossi F., Olteanu M., Villa-Vialaneix N. (2016) Accelerating stochastic kernel SOM. XXVth European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2017), Verleysen M. (ed), i6doc, Bruges, Belgium, 269-274.

mixKernel

mixKernel is an R package that provides methods to combine kernels for unsupervised exploratory analysis. Different solutions are implemented to compute a meta-kernel, in a consensus way or in a way that best preserves the original topology of the data. mixKernel also integrates kernel PCA to visualize similarities between samples in a non linear space and from the multiple source point of view. Functions to assess and display important variables are also provided in the package.
Project home page on CRAN (since 2017): https://cran.r-project.org/package=mixKernel
Project website: http://mixkernel.clementine.wf/
Project repository: Forge INRAE

If you are using mixKernel, please cite:
  • Mariette, J., Villa-Vialaneix, N. (2017) Unsupervised multiple kernel learning for heterogeneous data integration. Bioinformatics, 34(6), 1009-1015. DOI: 10.1093/bioinformatics/btx682
  • Brouard, C., Mariette, J., Flamary, R., Vialaneix, N. (2022) Feature selection for kernel methods in systems biology. NAR Genomics and Bioinformatics, 4, lqac014. DOI: 10.1093/nargab/lqac014

SISIR

SISIR is an R package that implements the sparse interval sliced inverse regression. It also provides an implementation of standard ridge and sparse SIR for high dimensional data.
Project home page on CRAN (since 2020): https://cran.r-project.org/package=SISIR
Project repository: Forge INRAE

If you are using SISIR, please cite:
  • Picheny V., Servien R., Villa-Vialaneix N. (2016) Interpretable sparse SIR for digitized functional data. Statistics and Computing, 29, 255-267. DOI: 10.1007/s11222-018-9806-6
  • Servien, R., Vialaneix, N. (2024) A random forest approach for interval selection in functional regression. Statistical Analysis and Data Mining, 17, e11705. DOI: 10.1002/sam.11705

NMFProfiler

NMFProfiler is a Python package (available on PyPi) to perform multi-omics integration for samples stratified in groups. It is based on a supervised version of the NMF.
Project home page on PyPI (since 2024): https://pypi.org/project/NMFProfiler/
Project repository: Forge INRAE

If you are using NMFProfiler, please cite:
  • Mercadié, A., Gravier, É., Josse, G., Fournier, I., Viodé, C., Vialaneix, N., & Brouard, C. (2025) NMFProfiler: A multi-omics integration method for samples stratified in groups. Bioinformatics, 41(2), btaf066. DOI: 10.1093/bioinformatics/btaf066

treediff

treediff performs tests to detect differences in structure between families of trees: The method is based on cophenetic distances and aggregated Student’s tests. The package includes a wrapper to deal with Hi-C data (chromatine conformation data based on high-thoughput sequencing).
Project home page on CRAN (since 2024): https://cran.r-project.org/package=treediff
Project repository: Forge INRAE

If you are using treediff, please cite:
  • Neuvial, P., Randriamihamison, N., Chavent, M., Foissac, S., Vialaneix, N. (2024) A two-sample tree-based test for hierarchically organized genomic signals. Journal of the Royal Statistical Society, Series C, 73(3), 774-795. DOI: 10.1093/jrsssc/qlae011

RNAseqNet

RNAseqNet is an R package that implements network inference with log-linear Poisson Graphical Model. Hot-deck multiple imputation method can be used to improve the reliability of the inference with an auxiliary dataset.
Project home page on CRAN (since 2017): https://cran.r-project.org/package=RNAseqNet
Project repository: Forge INRAE

If you are using RNAseqNet, please cite:
  • Imbert, A., Valsesia, A., Le Gall, C., Armenise, C., Gourraud, P.A., Viguerie, N. and Villa-Vialaneix, N. (2017) Multiple hot-deck imputation for network inference from RNA sequencing data. Bioinformatics, 34(10), 1726-1732. DOI: 10.1093/bioinformatics/btx819

NiLeDAM

NiLeDAM is an R package for dating monazites from measurements of triplets (U,Th,Pb) obtained by a microprobe technique. The implemented method is the one described in the article (Montel et al., 1996) and the package has been developed from the original programs made in basic language by Jean-Marc Montel (École Nationale Supérieure de Géologie, France). An example, kindly provided by Anne-Magali Seydoux-Guillaume is included in the package and is the one described in the article (Seydoux-Guillaume et al., 2012).
Monazite image by Aangelo (personal work) - licence GFDL or CC-BY-SA-3.0-2.5-2.0-1.0, via Wikimedia Commons
Project home page on CRAN (since 2017): https://cran.r-project.org/package=NiLeDAM
Project repository: Github


If you are using NiLeDAM, please cite:
  • Villa-Vialaneix, N., Montel, J.M., Seydoux-Guillaume, A.M. (2013) NiLeDAM: Monazite Datation for the NiLeDAM team. R package version 0.1.
  • Montel, J.M., Foret, S., Veschambre, M., Nicollet, C., Provost, A. (1996) Electron microprobe dating of monazite. Chemical Geology, 131, 37-53.
  • Seydoux-Guillaume, A.M., Montel, J.M., Bingen, B., Bosse, V., de Parseval, P., Paquette, J.L., Janots, E., Wirth, R. (2012) Low-temperature alteration of monazite: fluid mediated coupled dissolution-precipitation, irradiation damage and disturbance of the U-Pb and Th-Pb chronometers. Chemical Geology, 330-331, 140-158.
Cited in:
  • Laurent, A.T., Seydoux-Guillaume, A.M., Duchene, S., Bingen, B., Bosse, V., Datas, L. (2016) Sulphate incorporation in monazite lattice and dating the cycle of sulphur in metamorphic belts. Contributions to Mineralogy and Petrology, 171, 94.

Other software contributions (as author)

asterics

asterics is an web application designed to facilitate statistical and integrative omics data analysis.

Maintainer: Élise Maigné
Application website: https://asterics.miat.inrae.fr/ Project repository: Forge INRAE

If you are using adjclust, please cite:
  • Maigné, É., Noirot, C., Henry, J., Adu Kesewaah, Y., Badin, L., Déjean, S., Guilmineau, C., Krebs, A., Mathevet, F., Segalini, A., Thomassin, L., Colongo, D., Gaspin, C., Liaubet, L., & Vialaneix, N. (2023) ASTERICS: A Simple Tool for the ExploRation and Integration of omiCS data. BMC Bioinformatics, 24, 391. DOI: 10.1186/s12859-023-05504-9

adjclust

adjclust is an R package that 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 application fields in bioinformatics include Genome-Wide Association Studies or Hi-C data analysis, where the similarity between items is a decreasing function of their genomic distance. Taking advantage of this feature, the implemented algorithm is time and memory efficient.

Maintainer: Pierre Neuvial
Project home page on CRAN (since 2017): https://cran.r-project.org/package=adjclust
Package website: https://pneuvial.github.io/adjclust/ Project repository: Github

If you are using adjclust, please cite:
  • Ambroise, C., Dehman, A., Neuvial, P., Rigaill, G., Vialaneix, N. (2019) Adjacency-constrained hierarchical clustering of a band similarity matrix with application to genomics. Algorithms for Molecular Biology, 14, 363-389. DOI: 10.1186/s13015-019-0157-4
  • Randriamihamison, N., Vialaneix, N., Neuvial, P. (2020) Applicability and interpretability of Ward's hierarchical agglomerative clustering with or without contiguity constraints. Journal of Classification, 38, 363-389. DOI: 10.1007/s00357-020-09377-y

ASICS

ASICS is an R package that quantifies metabolites concentration in a complex spectrum using a set of pure metabolite spectra. The identification of metabolites is performed by fitting a mixture model to the spectra of the library with a sparse penalty.

Maintainer: Gaëlle Lefort.
Project home page on Bioconductor (since 2018): http://bioconductor.org/packages/ASICS/
Project repositories: Forge INRAE and git clone https://git.bioconductor.org/packages/ASICS (Bioconductor source code)

If you are using ASICS, please cite:
  • Lefort, G., Liaubet, L., Canlet, C., Tardivel, P., Père, M.C., Quesnel, H., Paris, A., Iannuccelli, N., Vialaneix, N., Servien, R. (2019) ASICS: an R package for a whole analysis workflow of 1D 1H NMR spectra. Bioinformatics, 35(21): 4356-4363. DOI: 10.1093/bioinformatics/btz248
  • Tardivel, P., Canlet, C., Lefort, G., Tremblay-Franco, M., Debrauwer, L., Concordet, D., Servien, R. (2017). ASICS: an automatic method for identification and quantification of metabolites in complex 1D 1H NMR spectra. Metabolomics, 13(10): 109. DOI: 10.1007/s11306-017-1244-5

phoenics

phoenics is an R package that performs a differential analysis at pathway level based on metabolite quantifications and information on pathway metabolite composition. The method is based on a Principal Component Analysis step and on a linear mixed model. Automatic query of metabolic pathways is also implemented.

Maintainer: Camille Guilmineau.
Project home page on CRAN (since 2024): https://cran.r-project.org/package=phoenics
Project repository: Forge INRAE

If you are using ASICS, please cite:
  • Guilmineau, C., Tremblay-Franco, M., Vialaneix, N., & Servien, R. (2025) phoenics: a novel statistical approach for longitudinal metabolomic pathway analysis. BMC Bioinformatics, 26, 105. DOI: 10.1186/s12859-025-06118-z

hicream

hicream performs Hi-C data differential analysis based on pixel-level differential analysis and a post hoc inference strategy to quantify signal in clusters of pixels. Clusters of pixels are obtained through a connectivity-constrained two-dimensional hierarchical clustering.

Maintainer: Élise Jorge.
Project home page on CRAN (since 2025): https://cran.r-project.org/package=hicream
Project repository: Forge INRAE

Public repositories associated to articles (scripts, data)

Code (and some data) repositories

Disclaimer! Unlike previous software implementations, these repositories are usually not actively maintained.


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