Biomedical subjects

Stéphane Robin

Publications and source records attributed to Stéphane Robin.

5 recordsLinked to original sources

PubMed2005

AnovArray: a set of SAS macros for the analysis of variance of gene expression data.

BACKGROUND: Analysis of variance is a powerful approach to identify differentially expressed genes in a complex experimental design for microarray and macroarray data. The advantage of the anova model is the possibility to evaluate multiple sources of variation in an experiment. RESULTS: AnovArray is a package implementing ANOVA for gene expression data using SAS statistical software. The originality of the package is 1) to quantify the different sources of variation on all genes together, 2) to provide a quality control of the model, 3) to propose two models for a gene's variance estimation and to perform a correction for multiple comparisons. CONCLUSION: AnovArray is freely available at http://www-mig.jouy.inra.fr/stat/AnovArray and requires only SAS statistical software.

Algorithms↗

PubMed2004

VarMixt: efficient variance modelling for the differential analysis of replicated gene expression data.

MOTIVATION: Identifying differentially regulated genes in experiments comparing two experimental conditions is often a key step in the microarray data analysis process. Many different approaches and methodological developments have been put forward, yet the question remains open. RESULTS: Varmixt is a powerful and efficient novel methodology for this task. It is based on a flexible and realistic variance modelling strategy. It compares favourably with other popular techniques (standard t-test, SAM and Cyber-T). The relevance of the approach is demonstrated with real-world and simulated datasets. The analysis strategy was successfully applied to both a 'two-colour' cDNA microarray and an Affymetrix Genechip. Strong control of false positive and false negative rates is proven in large simulation studies. AVAILABILITY: The R package is freely available at http://www.inapg.inra.fr/ens_rech/mathinfo/recherche/mathematique/outil.html CONTACT: delmar@inapg.inra.fr SUPPLEMENTARY INFORMATION: http://www.inapg.inra.fr/ens_rech/mathinfo/recherche/mathematique/outil.html.

Algorithms↗

PubMed2004

Spotting effect in microarray experiments.

BACKGROUND: Microarray data must be normalized because they suffer from multiple biases. We have identified a source of spatial experimental variability that significantly affects data obtained with Cy3/Cy5 spotted glass arrays. It yields a periodic pattern altering both signal (Cy3/Cy5 ratio) and intensity across the array. RESULTS: Using the variogram, a geostatistical tool, we characterized the observed variability, called here the spotting effect because it most probably arises during steps in the array printing procedure. CONCLUSIONS: The spotting effect is not appropriately corrected by current normalization methods, even by those addressing spatial variability. Importantly, the spotting effect may alter differential and clustering analysis.

Animals↗

PubMed2003

Classification and identification of Arabidopsis cell wall mutants using Fourier-Transform InfraRed (FT-IR) microspectroscopy.

We have developed a novel procedure for the rapid classification and identification of Arabidopsis mutants with altered cell wall architecture based on Fourier-Transform Infrared (FT-IR) microspectroscopy. FT-IR transmission spectra were sampled from native 4-day-old dark-grown hypocotyls of 46 mutants and the wild type treated with various drugs. The Mahalanobis distance between mutants, calculated from the spectral information after compression with the Discriminant Variables Selection procedure, was used for alpha hierarchical cluster analysis. Despite the completely unsupervised nature of the classification procedure, we show that all mutants with cellulose defects appeared in the same cluster. In addition, mutant alleles of similar strength for several unrelated loci were also clustered, which demonstrates the sensitivity of the method to detect a wide array of cell wall defects. Comparing the cellulose-deficient cluster with the cluster that contained wild-type controls led to the identification of wave numbers that were diagnostic for altered cellulose content in the context of an intact cell wall. The results show that FT-IR spectra can be used to identify different classes of mutants and to characterize cell wall changes at a microscopic level in unknown mutants. This procedure significantly accelerates the identification and classification of cell wall mutants, which makes cell wall polysaccharides more accessible to functional genomics approaches.

Alleles↗

PubMedDate not supplied

Checking homogeneity of motifs' distribution in heterogenous sequences.

Studying the distribution of a motif along sequences may help in the understanding of its biological function, or to detect regions of interest. A statistical model is needed to assess the significance of the observed distribution. We propose a heterogenous compound Poisson process to model the possibility of overlap between occurrences and some heterogeneity of the sequence known a priori. The estimation procedure of the parameters is described and tests of homogenous sub-models are proposed. We also consider the detection of rich regions using either cumulated distances or moving intervals, via a homogenization technique. Illustrations of the method are given with applications to bacterial genomes.

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