Sébastien Lê

A new unsupervised gene clustering algorithm based on the integration of biological knowledge into expression data.

BackgroundGene clustering algorithms are massively used by biologists when analysing omics data. Classical gene clustering strategies are based on the use of expression data only, directly as in Heatmaps, or indirectly as in clustering based on coexpression networks for instance. However, the classical strategies may not be sufficient to bring out all potential relationships amongst genes.ResultsWe propose a new unsupervised gene clustering algorithm based on the integration of external biological knowledge, such as Gene Ontology annotations, into expression data. We introduce a new distance between genes which consists in integrating biological knowledge into the analysis of expression data. Therefore, two genes are close if they have both similar expression profiles and similar functional profiles at once. Then a classical algorithm (e.g. K-means) is used to obtain gene clusters. In addition, we propose an automatic evaluation procedure of gene clusters. This procedure is based on two indicators which measure the global coexpression and biological homogeneity of gene clusters. They are associated with hypothesis testing which allows to complement each indicator with a p-value.Our clustering algorithm is compared to the Heatmap clustering and the clustering based on gene coexpression network, both on simulated and real data. In both cases, it outperforms the other methodologies as it provides the highest proportion of significantly coexpressed and biologically homogeneous gene clusters, which are good candidates for interpretation.ConclusionOur new clustering algorithm provides a higher proportion of good candidates for interpretation. Therefore, we expect the interpretation of these clusters to help biologists to formulate new hypothesis on the relationships amongst genes.

DMFA: Dual Multiple Factor Analysis

In this article, we propose a new method called Dual Multiple Factor Analysis (DMFA), which is an extension of DMFA in the case where individuals are structured according to a partition. The heart of the method rests on a factor analysis known as internal, in reference to the internal correspondence analysis, for which data are systematically centered by group. This analysis is an internal PCA when all the variables are quantitative. DMFA provides the classic results of a PCA as well as additional outputs induced by the consideration of a partition on individuals, such as the superimposed representation of the L scatter plots of variables associated with the L groups of individuals and the representation of the scatter plot of the correlations matrices associated each one with a group of individuals.

Napping and sorted Napping as a sensory profiling technique

Can (sorted) Napping be considered a rapid method? Can it be considered as an alternative to descriptive analysis? The main objective of this chapter is to shed new light on Napping and sorted Napping in order to provide some clues for answering these two questions. In particular, we will stress the intrinsic nature of the data collected when using Napping, and on the link between Napping and sorted Napping. We will see how the information provided by Napping is unique, and how different it can be from that provided by descriptive analysis. We will see that, at a subject level, Napping is certainly a rapid method, but in order to be used as an alternative to descriptive analysis, a relatively high number of subjects need to be considered.

Holos: A collaborative environment for similarity-based holistic approaches

Through this article, we aim to introduce Holos—a new collaborative environment that allows researchers to carry out experiments based on similarity assessments between stimuli, such as in projective-mapping and sorting tasks. An important feature of Holos is its capacity to assess real-time individual processes during the task. Within the Holos environment, researchers can design experiments on its platform, which can handle four kinds of stimuli: concepts, images, sounds, and videos. In addition, researchers can share their study resources within the scientific community, including stimuli, experimental protocols, and/or the data collected. With a dedicated Android application combined with a tactile human–machine interface, subjects can perform experiments using a tablet to obtain similarity measures between stimuli. On the tablet, the stimuli are displayed as icons that can be dragged with one finger to position them, depending on the ways they are perceived. By recording the x,y coordinates of the stimuli while subjects move the icons, the obtained data can reveal the cognitive processes of the subjects during the experiment. Such data, named digit-tracking data, can be analyzed with the SensoMineR package. In this article, we describe how researchers can design an experiment, how subjects can perform the experiment, and how digit-tracking data can be statistically analyzed within the Holos environment. At the end of the article, a short exemplary experiment is presented.

Multidimensional Scaling Versus Multiple Correspondence Analysis When Analyzing Categorization Data

Categorization is a cognitive process in which subjects are asked to group a set of object according to their similarities. This task was used for the first time in psychology and is becoming now more and more popular in sensory analysis. Categorization data are usually analyzed by multidimensional scaling (MDS). In this article we propose an original approach based on multiple correspondence analysis (MCA); this new methodology which provides new insights on the data will be compared to one specified procedure of MDS.