missMethyl-package: Introduction to the missMethyl package
In ChuanJ/test: Analysing Illumina HumanMethylation BeadChip Data
Description
Details
Author(s)
References
Description
missMethyl is a library for the analysis of Illumina's 450K human methylation BeadChip. Specifically, functions for SWAN normalisation and differential variability analysis are provided. SWAN normalisation uses probe specific information, and the differential variability procedure uses linear models which can handle any designed experiment.
Details
Package: missMethyl
Type: Package
Version: 0.99.0
Date: 2014-06-30
License: GPL-2
Normalisation of the 450K arrays can be performed using the function SWAN.
Differential variability analysis can be performed by calling varFit followed by topVar for a list of the top ranked differentially variable CpGs between conditions.
More detailed help documentation is provided in each function's help page.
Author(s)
Belinda Phipson and Jovana Maksimovic
Maintainer: Belinda Phipson <belinda.phipson@mcri.edu.au>, Jovana Maksimovic <jovana.maksimovic@mcri.edu.au>
References
Maksimovic, J., Gordon, L., Oshlack, A. (2012). SWAN: Subset-quantile within array normalization for illumina infinium HumanMethylation450 BeadChips. Genome Biology, 13:R44.
Phipson, B., and Oshlack, A. (2014). DiffVar: A new method for detecting differential variability with application to methylation in cancer and aging. Genome Biology, 15:465.
ChuanJ/test documentation built on Oct. 30, 2019, 5:43 a.m.
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Description Details Author(s) References
Description
missMethyl is a library for the analysis of Illumina's 450K human methylation BeadChip. Specifically, functions for SWAN normalisation and differential variability analysis are provided. SWAN normalisation uses probe specific information, and the differential variability procedure uses linear models which can handle any designed experiment.
Details
| Package: | missMethyl |
| Type: | Package |
| Version: | 0.99.0 |
| Date: | 2014-06-30 |
| License: | GPL-2 |
Normalisation of the 450K arrays can be performed using the function SWAN.
Differential variability analysis can be performed by calling varFit followed by topVar for a list of the top ranked differentially variable CpGs between conditions.
More detailed help documentation is provided in each function's help page.
Author(s)
Belinda Phipson and Jovana Maksimovic
Maintainer: Belinda Phipson <belinda.phipson@mcri.edu.au>, Jovana Maksimovic <jovana.maksimovic@mcri.edu.au>
References
Maksimovic, J., Gordon, L., Oshlack, A. (2012). SWAN: Subset-quantile within array normalization for illumina infinium HumanMethylation450 BeadChips. Genome Biology, 13:R44.
Phipson, B., and Oshlack, A. (2014). DiffVar: A new method for detecting differential variability with application to methylation in cancer and aging. Genome Biology, 15:465.
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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