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It is devoted to the variable selection in model-based clustering.

It is the greedy algorithm associated to the SR modeling proposed by C. Maugis, G. Celeux and M.-L. Martin-Magniette in [1] and [2], modifying the method of Raftery and Dean [3].

This software allows to study data where individuals are described by quantitative block variables. It returns a data clustering and the selected model, composed of the number of clusters, the mixture form and the variable partition.

It is devoted to the variable selection in model-based clustering, taking into account missing values. It is a greedy algorithm associated to the SR modeling proposed in Maugis et al. (Biometrics, 2009), taking into account missing values. This software allows to study data where individuals are described by quantitative block variables. It returns a data clustering and the selected model, composed of the number of clusters and the variable partition. This software is here only proposed for Gaussian mixtures whose variance matrices are assumed to be identical and free (m=[pkLC]).

It is devoted to the variable selection in model-based clustering. It is a greedy algorithm associated to the SRUW modeling proposed by C.Maugis, G.Celeux and M.-L. Martin-Magniette in [1] and [2], modifying the method of Raftery and Dean [3] and improving our SelvarClust algorithm [4]. The SRUW modeling takes into account the three possible roles: relevant, redundant and independent variables.

This software allows to study datasets where observations are described by quantitative variables. It returns a data clustering and the selected model composed of the number of clusters, the mixture form, the variance matrix form for the linear regression and the independent Gaussian density, and the variable partition.

Simple computation of spatial statistic functions of distance to characterize the spatial structures of mapped objects, including classical ones (Ripley's K and others) and more recent ones used by spatial economists (Duranton and Overman's Kd, Marcon and Puech's M). Relies on spatstat for some core calculation.

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

Motivation: The spatial conformation of the chromosome has a deep influence on gene regulation and expression. Hi-C technology allows the evaluation of the spatial proximity between any pair of loci along the genome. It results in a data matrix where blocks corresponding to (self-)interacting regions appear. The delimitation of such blocks is critical to better understand the spatial organization of the chromatin. From a computational point of view, it results in a 2D segmentation problem.

Results: We focus on the detection of cis-interacting regions, which appear to be prominent in observed data. We define a block-wise segmentation model for the detection of such regions. We prove that the maximization of the likelihood with respect to the block boundaries can be rephrased in terms of a 1D segmentation problem, for which the standard dynamic programming applies. The performance of the proposed methods is assessed by a simulation study on both synthetic and resampled data. A comparative study on public data shows good concordance with biologically confirmed regions.

Availability and implementation: The HiCseg R package is available from the Comprehensive R Archive Network and from the Web page of the corresponding author.

Détection de régions corrélées dans des données d’expression, prenant en compte des variations du nombre de copies .

Performs correlation matrix segmentation and applies a test procedure to detect highly correlated regions in gene expression.

Even if one of the major applications of two-color DNA microarray hybridizations is to detect differentially expressed genes using intensity log-ratios, single channel signals provide also useful information as absolute value measurements which allow the description of gene expression patterns. In this context, it becomes crucial to determine the set of probes that hybridize, that is for which the intensity signal is greater than a hybridization threshold to be fixed. Existing procedures are either an arbitrary thresholding or require the knowledge of a population of non-hybridized probes. In this work we present the MixThres method to determine an adaptive hybridization threshold from intensity levels of the complete set of probes hybridized on a chip. We define a hybridization threshold based on the histogram of the probe intensity values. Our procedure is divided into two steps. First the intensity distribution is estimated using mixture models. Second a hybridization threshold is defined from the components of the mixture. We validate our method on DNA tiling array and expression array data. We show that our method has a good reproducibility, its specificity is greater than 97 % and its precision of 88 %. The R package MixThres is available at http://www.agroparistech.fr/mia/outil.htm