| Chapter title |
Working with Oligonucleotide Arrays
|
|---|---|
| Chapter number | 7 |
| Book title |
Statistical Genomics
|
| Published in |
Methods in molecular biology, January 2016
|
| DOI | 10.1007/978-1-4939-3578-9_7 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-3576-5, 978-1-4939-3578-9
|
| Authors |
Benilton S. Carvalho, Carvalho, Benilton S, Carvalho, Benilton S. |
| Editors |
Ewy Mathé, Sean Davis |
| Abstract |
Preprocessing microarray data consists of a number of statistical procedures that convert the observed intensities into quantities that represent biological events of interest, like gene expression and allele-specific abundances. Here, we present a summary of the theory behind microarray data preprocessing for expression, whole transcriptome and SNP designs and focus on the computational protocol used to obtain processed data that will be used on downstream analyses. We describe the main features of the oligo Bioconductor package, an application designed to support oligonucleotide microarrays using the R statistical environment and the infrastructure provided by Bioconductor, allowing the researcher to handle probe-level data and interface with advanced statistical tools under a simplified framework. We demonstrate the use of the package by preprocessing data originated from three different designs. |
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Mendeley readers
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| Unknown | 1 | 20% |