Software

Software

  • MultiRNAflow
    • The R package MultiRNAflow provides an easy to use unified framework allowing to make both unsupervised and supervised analysis (differential expression analysis) for RNAseq datasets with an arbitrary number of biological conditions and time points. In particular, this package makes a deep downstream analysis of differential expression information, e.g. identifying temporal patterns across biological conditions and differentially expresses genes which are specific to a biological condition for each time.
    • https://bioconductor.org/packages/release/bioc/html/MultiRNAflow.html
  • Harissa
    • Harissa is a Python package for both inference and simulation of gene regulatory networks, based on stochastic gene expression with transcriptional bursting. It was implemented in the context of a mechanistic approach to gene regulatory network inference from single-cell data.
    • https://github.com/ulysseherbach/harissa
  • quantCurves
  • cvmgof
    • cvmgof is an R library devoted to Cramer-von Mises goodness-of-fit tests. It implements three nonparametric statistical methods based on Cramer-von Mises statistics to estimate and test a regression model.
    • https://cran.r-project.org/web/packages/cvmgof/index.html

From former team BIGS

  • Angio-Analytics
    • Angio-Analytics allows the pharmacodynamic characterization of anti-vascular effects in anti-cancer treatments.
  • HSPOR
    • Several functions that allow by different methods to infer a piecewise polynomial regression model under regularity constraints, namely continuity or differentiability of the link function. The implemented functions are either specific to data with two regimes, or generic for any number of regimes, which can be given by the user or learned by the algorithm.
    • https://cran.r-project.org/web/packages/HSPOR/
  • In silico
    • To speed up the preclinical development of medical engineered nanomaterials, we have designed an integrated computing platform dedicated to the virtual screening of nanostructured materials activated by X-ray making it possible to select nano-objects presenting interesting medical properties faster. The main advantage of this in silico design approach is to virtually screen a lot of possible formulations and to rapidly select the most promising ones. The platform can currently handle the accelerated design of radiation therapy enhancing nanoparticles and medical imaging nano-sized contrast agents as well as the comparison between nano-objects and the optimization of existing materials.
  • kosel
    • Performs variable selection for many types of L1-regularised regressions using the revisited knockoffs procedure. This procedure uses a matrix of knockoffs of the covariates independent from the response variable Y. The idea is to determine if a covariate belongs to the model depending on whether it enters the model before or after its knockoff. The procedure suits for a wide range of regressions with various types of response variables. Regression models available are exported from the R packages 'glmnet' and 'ordinalNet'. Based on the paper linked to via the URL below: Gegout A., Gueudin A., Karmann C. (2019) <arXiv:1907.03153>
    • https://cran.r-project.org/web/packages/kosel/kosel.pdf
  • SesIndexCreatoR
    • This package allows computing and visualizing socioeconomic indices and categories distributions from datasets of socioeconomic variables (These tools were developed as part of the EquitArea Project, a public health program).
    • http://www.equitarea.org/documents/packages_1.0-0/
  • starm R
    • Estimation and model selection of the two-time centered autologistic regression model based on Gegout-Petit A., Guerin-Dubrana L., Li S. "A new centered spatio-temporal autologistic regression model. Application to local spread of plant diseases." 2019 <arXiv:1811.06782>. Application for the spatio-temporal modelling of the spread of a disease on a grid over time.
  • ARMADA
    • Two steps variable selection procedure in a context of high-dimensional dependent data with few observations. A first step is dedicated to eliminate the dependency between variables (clustering of variables, followed by factor analysis inside each cluster). A second step consists in variable selection by aggregation of adapted methods.
    • https://cran.r-project.org/web/packages/armada/

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