Alain Baccini

Clustering Time-Series Gene Expression Data Using Smoothing Spline Derivatives

Microarray data acquired during time-course experiments allow the temporal variations in gene expression to be monitored. An original postprandial fasting experiment was conducted in the mouse and the expression of 200 genes was monitored with a dedicated macroarray at 11 time points between 0 and 72 hours of fasting. The aim of this study was to provide a relevant clustering of gene expression temporal profiles. This was achieved by focusing on the shapes of the curves rather than on the absolute level of expression. Actually, we combined spline smoothing and first derivative computation with hierarchical and partitioning clustering. A heuristic approach was proposed to tune the spline smoothing parameter using both statistical and biological considerations. Clusters are illustrated a posteriori through principal component analysis and heatmap visualization. Most results were found to be in agreement with the literature on the effects of fasting on the mouse liver and provide promising directions for future biological investigations.

HIGHLIGHTING RELATIONSHIPS BETWEEN HETEROGENEOUS BIOLOGICAL DATA THROUGH GRAPHICAL DISPLAYS BASED ON REGULARIZED CANONICAL CORRELATION ANALYSIS

Biological data produced by high throughput technologies are becoming more and more abundant and are arousing many statistical questions. This paper addresses one of them; when gene expression data are jointly observed with other variables with the purpose of highlighting significant relationships between gene expression and these other variables. One relevant statistical method to explore these relationships is Canonical Correlation Analysis (CCA). Unfortunately, in the context of postgenomic data, the number of variables (gene expressions) is usually greater than the number of units (samples) and CCA cannot be directly performed: a regularized version is required. We applied regularized CCA on data sets from two different studies and show that its interpretation evidences both previously validated relationships and new hypothesis. From the first data sets (nutrigenomic study), we generated interesting hypothesis on the transcription factor pathways potentially linking hepatic fatty acids and gene expression. From the second data sets (pharmacogenomic study on the NCI-60 cancer cell line panel), we identified new ABC transporter candidate substrates which relevancy is illustrated by the concomitant identification of several known substrates. In conclusion, the use of regularized CCA is likely to be relevant to a number and a variety of biological experiments involving the generation of high throughput data. We demonstrated here its ability to enhance the range of relevant conclusions that can be drawn from these relatively expensive experiments.

Relationships between sensory and physicochemical measurements in meat of rabbit from three different breeding systems using canonical correlation analysis.

Meat from rabbits reared either according to a standard (STAND) or a high quality norm (LABEL) or a low growth breeding (RUSSE) system were submitted to a sensory evaluation and to a large set of physicochemical measurements (weight of retail cuts, colour parameters, ultimate pH, femur flexure test, Warner-Bratzler shear test, water holding capacities and cooking losses). STAND rabbit meat exhibited the most juicy meat in back and in leg (p<0.01). Leg tenderness significantly decreased (p<0.001) in the rank order STAND>LABEL>RUSSE. Canonical correlation analysis showed strong correlations between physicochemical and sensory variables (R(2)=0.73 and 0.68 between the two first pairs of canonical variates). Especially, sensory tenderness and WB shear test variables assessed on raw longissimus muscle (LL) were correlated. Fibrous attribute in back was correlated with cooking loss in LL. When analysed separately only RUSSE rabbits exhibited the same relations between variables as those calculated in whole dataset.

How many performance measures to evaluate information retrieval systems

Evaluating effectiveness of information retrieval systems is achieved by performing on a collection of documents, a search, in which a set of test queries are performed and, for each query, the list of the relevant documents. This evaluation framework also includes performance measures making it possible to control the impact of a modification of search parameters. The program trec_eval calculates a large number of measures, some being more used like the mean average precision or recall-precision curves. The motivation of our work is to compare all measures and to help the user to choose a small number of them when evaluating different information retrieval systems. In this paper, we present the study we carried out from a massive data analysis of TREC results. Relationships between the 130 measures calculated by trec_eval for individual queries are investigated, and we show that they can be clustered into homogeneous clusters.

Analyse des critères d'évaluation des systèmes de recherche d'information

Evaluating information retrieval implies a document collection on which search is carried out, a set of test queries and the lists of the relevant documents for each query. This evaluation framework also includes evaluation measures making it possible to control the impact of search parameters on the performance. Trec-eval calculates a large number of measures, some being used more widely, like the mean average precision or recall-precision curves. The aim of this paper is to choose the minimal set of measures necessary to compare different information retrieval systems. In this paper, we present the study we carried out on relationships between 27 measures among the most used in the literature. We show that a set of 7 measures is enough to represent 27 studied measures: ircl_prn.80, MAP, ircl_pr.20, recip_rank, P15, exact_precision, and exact_recall.