In a recent original article at PLOS ONE Journal, the Immunology of Diabetes Research Group at Germans Trias i Pujol Research Institute, with the collaboration of the Immunology Group at Vall d’Hebron Institute of Research (VHIR), the Institut de Recerca Biomèdica de Lleida and the Statistics and Bioinformatics Research Group report a new experimental immunotherapy that prevents the onset of Type 1 Diabetes in mice predisposed to the disease.
We recently published a new paper where Multiple Factor Analysis as a technique for the integration of omics data is reviewed and is applied to a problem in the analysis of Insuline Resistance.
Lecture Notes in Computer Science Volume 6620, 2012, pp 29-41
Chapter: Kernel Methods for Dimensionality Reduction
Applied to the «Omics» Data.
Book: Principal Components Analysis. Multidisciplinary Applications.
Ed. by P. Sanguansat. Pages 1-20. InTech OpenAcces. ISBN:978-953-51-0129. 2012.
Authors: F.Reverter, E. Vegas and J.M Oller
Comparison of lists of genes based on functional profiles.
BMC Bioinformatics. 2011 Oct 16;12(1):401
Authors: Salicru M, Ocana J, Sanchez-Pla A
ABSTRACT: BACKGROUND: How to compare studies on the basis of their biological significance is a problem of central importance in high-throughput genomics. Many methods for performing such comparisons are based on the information in databases of functional annotation, such as those that form the Gene Ontology (GO). Typically, they consist of analyzing gene annotation frequencies in some pre-specified GO classes, in a class-by-class way, followed by p-value adjustment for multiple testing. Enrichment analysis, where a list of genes is compared against a wider universe of genes, is the most common example. RESULTS: A new global testing procedure and a method incorporating it are presented. Instead of testing separately for each GO class, a single global test for all classes under consideration is performed. The test is based on the distance between the functional profiles, defined as the joint frequencies of annotation in a given set of GO classes. These classes may be chosen at one or more GO levels. The new global test is more powerful and accurate with respect to type I errors than the usual class-by-class approach. When applied to some real datasets, the results suggest that the method may also provide useful information that complements the tests performed using a class-by-class approach if gene counts are sparse in some classes. An R library, goProfiles, implements these methods and is available from Bioconductor, http://bioconductor.org/packages/rel…oProfiles.html. CONCLUSIONS: The method provides an inferential basis for deciding whether two lists are functionally different. For global comparisons it is preferable to the global chi-square test of homogeneity. Furthermore, it may provide additional information if used in conjunction with class-by-class methods.
PMID: 21999355 [PubMed - as supplied by publisher]