Titlebook: Bioconductor Case Studies; Florian Hahne,Wolfgang Huber,Seth Falcon Book 2008 Springer-Verlag New York 2008 Annotation.Bioinformatics.Clus

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R and Bioconductor Introduction,cs, and working with . as hash tables. We introduce the . class as an example for a basic Bioconductor structure used for holding genomic data, in this case expression microarray data. And we explore some visualization techniques for gene expression data to get a feeling for R’s extensive graphical

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Easy Differential Expression,c filtering step to remove probes for genes that appear to be always unexpressed or at least not differentially expressed. Second, a probe-by-probe statistical test, and third, multiple testing correction to get an attenuated test statistic through the false discovery rate (FDR). There are many vari

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Annotation and Metadata,mapping them to their target genes, one will want to use the annotation of the genes and gene products to better interpret the experimental results. Often, it is beneficial to use gene annotation in the course of the primary analysis, in order to narrow down the set of data to be considered and amel

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Using Graphs for Interactome Data, In this chapter, we explore a curated dataset of protein interactions and perform a statistical analysis of the relationship between protein interaction and coexpression. We also show how to access large-scale protein–protein interaction datasets from the IntAct repository at the EBI.

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Hypergeometric Testing Used for Gene Set Enrichment Analysis, functional relationships among those genes that might help better elucidate the underlying biology. These methods typically rely on existing or predefined sets of genes. In this chapter we show how to carry out Hypergeometric tests to identify potentially interesting gene sets.

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Michael J?ckel,Jan Dietrich Reinhardtcs, and working with . as hash tables. We introduce the . class as an example for a basic Bioconductor structure used for holding genomic data, in this case expression microarray data. And we explore some visualization techniques for gene expression data to get a feeling for R’s extensive graphical capabilities.

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https://doi.org/10.1007/978-3-658-22970-2 In this chapter, we explore a curated dataset of protein interactions and perform a statistical analysis of the relationship between protein interaction and coexpression. We also show how to access large-scale protein–protein interaction datasets from the IntAct repository at the EBI.