Catálogo de publicaciones - libros

We describe software tools for creating, manipulating, and visualizing graphs in the Bioconductor project. We give the rationale for our design decisions and provide brief outlines of how to make use of these tools. The discussion mirrors that of Chapter 20 where the different mathematical constructs were described. It is worth differentiating between packages that are mainly infrastructure (sets of tools that can be used to create other pieces of software) and packages that are designed to provide an end-user application. The packages graph, RBGL, and Rgraphviz are infrastructure packages. Software developers may use these packages to construct tools aimed at specific applications areas, such as the GOstats package.

In this chapter we consider four specific data-analytic and inferential problems that can be addressed using graphs. We demonstrate the use of the software and methods described in Chapters 20 and 21 on real problems in computational biology.We will show how one can investigate relationships between gene expression and protein-protein interaction data, how GO annotations can be used to analyze gene sets, how literature citations can be related to experimental data, and how gene expression data can be mapped on pathways.

A survey is given of differential expression analyses using the linear modeling features of the limma package. The chapter starts with the simplest replicated designs and progresses through experiments with two or more groups, direct designs, factorial designs and time course experiments. Experiments with technical as well as biological replication are considered. Empirical Bayes test statistics are explained. The use of quality weights, adaptive background correction and control spots in conjunction with linear modelling is illustrated on the β7 data.

A survey is given of tasks related to the construction and evaluation of classifiers applied to a renal cell cancer data set. Balanced sample splitting, non-specific filtering, linear discriminant analysis, nearest-neighbor prediction, and support vector machines are all concretely illustrated using the MLInterfaces package. Evaluations based on single and multiple random splits of data are compared. The entire presentation is given in a very generic programming format, to facilitate the adaptation and variation, by other investigators, of the techniques used here.

One of the most popular applications of microarray technology is the identification of genes that are differentially expressed in two populations.With Affymetrix GeneChip technology, there are several steps between hybridization and the selection of interesting genes. The steps of preprocessing to improve signal to noise ratios, choosing a summary statistic for appropriate ranking of genes, and deciding on a final filter for candidate genes are largely statistical in nature. In this chapter, we demonstrate Bioconductor tools useful for creating such lists. We start from the raw probe level data (CEL files) and conclude with the creation of annotated reports.