massiR-package: massiR: a microarry Gene Expression Sample Sex Identifier
In SamBuckberry/massiR: massiR: MicroArray Sample Sex Identifier

Description Details Author(s) References See Also Examples

massi uses y chromosome probe information to cluster samples and predict the sex of each sample in gene expression microarray datasets.

Package:	massi
Type:	Package
Version:	0.99.0
Date:	2014-01-27
License:	GPL-3

The massi analysis requires a typical normalized sample/probe values produced by a microarray experiment. The massi_y function will extract the y chromosome probe information and caculate y chromosome probe variance to allow the used to select the most informative probes. Using the massi_select function the used can select a probe variation threshold to reduce the number of probes used in the massi.cluster step. The massi_cluster function clusters samples into two clusters using the y chromosome probe values. Clustering is performed using the K-medoids method as implimented in the "fpc" package. There are two plotting fucntions, massi_y_plot and massi_cluster_plot, that allow the user to explore the data at various stages of the analysis. There is also a function, massi_dip, that can be used to test if there may be a sample sex-bias in the dataset.

Sam Buckberry

Maintainer: Sam Buckberry <sam.buckberry@adelaide.edu.au>

Christian Hennig (2013). fpc: Flexible procedures for clustering. R package version 2.1-6. http://CRAN.R-project.org/package=fpc

Martin Maechler (2013). diptest: Hartigan's dip test statistic for unimodality - corrected code. R package version 0.75-5. http://CRAN.R-project.org/package=diptest

Gregory R. Warnes, Ben Bolker, Lodewijk Bonebakker, Robert Gentleman, Wolfgang Huber Andy Liaw, Thomas Lumley, Martin Maechler, Arni Magnusson, Steffen Moeller, Marc Schwartz and Bill Venables (2013). gplots: Various R programming tools for plotting data. R package version 2.12.1. http://CRAN.R-project.org/package=gplots

massi_y, massi_select, massi_cluster, massi_y_plot, massi_dip, massi_cluster_plot

# load the test datasets
data(massi.test.dataset, massi.test.probes)

# use the massi.y function to calculate probe variation
massi_y_out <- massi_y(expression_data=massi.test.dataset, y_probes=massi.test.probes)

# plot probe variation to aid in deciding on the most informative subset of y chromosome probes
massi_y_plot(massi_y_out)

# Extract the informative probes for clustering 
massi_select_out <- massi_select(massi.test.dataset, massi.test.probes, threshold=4)

# cluster samples to predict the sex for each sample
massi_cluster_out <- massi_cluster(massi_select_out)

# get the predicted sex for each sample
data.frame(massi_cluster_out[[2]])