Statistics and Bioinformatics research group

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
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]

Presentamos la comunicación:

“Usando de forma segura R vía web con Tiki“. Xavier de Pedro y Àlex Sánchez. I Congreso de usuarios de R (II Jornadas). Mieres (Oviedo, España). 1-2 de Diciembre de 2010.

• Comunicación: RJ II Jornadas R ES XavierdePedro (pdf)
• Diapositivas: RJ II Jornadas R ES XavierdePedro_diapos_v3 (pdf)

We have a paper in the Genomics, Proteomics & Bioinformatics journal (2010 Sep; 8(3): 200-210). The paper is:

“Mining Gene Expression Profiles: An Integrated Implementation of Kernel Principal Component Analysis and Singular Value Decomposition” by Ferran Reverter, Esteban Vegas, and Pedro Sánchez.PDF: full text

Abstract
The detection of genes that show similar profiles under different experimental conditions is often an initial step in inferring the biological significance of such genes. Visualization tools are used to identify genes with similar profiles in microarray studies. Given the large number of genes recorded in microarray experiments, gene ex-pression data are generally displayed on a low dimensional plot, based on linear methods. However, microarray data show nonlinearity, due to high-order terms of interaction between genes, so alternative approaches, such as kernel methods, may be more appropriate. We introduce a technique that combines kernel principal component analysis (KPCA) and Biplot to visualize gene expression profiles. Our approach relies on the singular value decomposition of the input matrix and incorporates an additional step that involves KPCA. The main properties of our method are the extraction of nonlinear features and the preservation of the input variables (genes) in the output display. We apply this algorithm to colon tumor, leukemia and lymphoma datasets. Our approach reveals the underlying structure of the gene expression profiles and provides a more intuitive understanding of the gene and sample association.

Keywords:  kernel method, biplot, gene expression profile, dimension reduction

We have submitted a few communications to the Xth Spanish Symposium on Bioinformatics (JBI2010) (Malaga, Spain. 27-29 October 2010). One of them is:

“An extension of the Minimum Distance Probability Algorithm for Kernel-based Pattern Analysis“, by Ferran Reverter, Esteban Vegas, Josep M. Oller and Martin Ríos. Scheduled for October 28th, 2010. PDF: abstract , poster

We propose an algorithm for the allocation of an individual to one of several classes or subpopulations. We extend the Minimum Distance Probability algorithm (MDP) to kernel methodology.

We have submitted a few communications to the Xth Spanish Symposium on Bioinformatics (JBI2010) (Malaga, Spain. 27-29 October 2010). One of them is:

“Using R in Tiki for Bioinformatics“, by X. de Pedro and A. Sánchez. Scheduled for October 28th, 2010. PDF: Poster

The need to work with colleagues from other institutions is quite common in Science. Teams often look for tools which allow coordinating with others in web platforms which enhance collaboration across space and time. Although data analysis and visualization is getting very popular using the free statistical software R, it still seems to lack some easy complementary program to allow creating quickly a web interface to support the usual workflows in Bioinformatic tasks such as Microarray analysis. This communication focuses on analyzing the state of the art of web interfaces to R scripts in Life Sciences, with a new add-on developed for a mature general purpose and free software Wiki CMS/Groupware platform: PluginR for Tiki. A use case for Microarray analysis will be described. More information: htp://estbioinfo.stat.ub.es

Other accepted communications will be announced later.