Beadarray: R classes and methods for Illumina bead-based data

Mark J. Dunning; Mike L. Smith; Matthew E. Ritchie; Simon Tavaré

doi:10.1093/bioinformatics/btm311

Beadarray: R classes and methods for Illumina bead-based data

Mark J. Dunning
, Mike L. Smith
, Matthew E. Ritchie
, Simon Tavaré

Research output: Contribution to journal › Article › peer-review

392 Scopus citations

Abstract

Summary: The R/Bioconductor package beadarray allows raw data from Illumina experiments to be read and stored in convenient R classes. Users are free to choose between various methods of image processing, background correction and normalization in their analysis rather than using the defaults in Illumina's; proprietary software. The package also allows quality assessment to be carried out on the raw data. The data can then be summarized and stored in a format which can be used by other R/Bioconductor packages to perform downstream analyses. Summarized data processed by Illumina's; BeadStudio software can also be read and analysed in the same manner.

Original language	English
Pages (from-to)	2183-2184
Number of pages	2
Journal	Bioinformatics
Volume	23
Issue number	16
DOIs	https://doi.org/10.1093/bioinformatics/btm311
State	Published - 15 Aug 2007
Externally published	Yes

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10.1093/bioinformatics/btm311

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AU - Tavaré, Simon

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AB - Summary: The R/Bioconductor package beadarray allows raw data from Illumina experiments to be read and stored in convenient R classes. Users are free to choose between various methods of image processing, background correction and normalization in their analysis rather than using the defaults in Illumina's; proprietary software. The package also allows quality assessment to be carried out on the raw data. The data can then be summarized and stored in a format which can be used by other R/Bioconductor packages to perform downstream analyses. Summarized data processed by Illumina's; BeadStudio software can also be read and analysed in the same manner.

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Beadarray: R classes and methods for Illumina bead-based data

Abstract

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