massi_dip

Description

The massi_dip function applies the dip test to the subset of y chromosome probe values returned from the massi_select function. This can be used to indicate if there may be either a male or female bias in the dataset. This function returns a message indicating if the dataset may have a sex bias. The results for massi_dip are not relaible for datasets with 10 or less samples.

Usage

massi_dip(y_subset_values)

Arguments

Details

This function caclulates z-scores for the y.chromosome probe values returned from the massi_select function and then checks if the average z-scores for each sample show a unimodal or multi-modal distribution by applying the dip test. If the proportion of male and female samples in the dataset is relatively balanced, the distribution of average z-scores should be bi-modal. If the distribution looks unimodal, the dataset likely contains a high proportion of one sex. By testing with empirical datasets and randomly generating data subsets with different male/female proportions, guideline values were developed to provide an indication if there is a potential sex bias in the dataset. If the dip statistic is > 0.08 then the dataset is highly likely to have a porportions of male and female samples that will allow the massi_cluster function to predict the sex of samples with a high degree of accuracy. The results of this test should only be used as a guide and the results should be interpreted in light of the massi_cluster results. For more details see the massi package vignette.

Value

This function returns a list containing

Author(s)

Sam Buckberry

References

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

See Also

massi_y, massi_select, massi_cluster, massi_y_plot, massi_cluster_plot

Examples

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

massi_select_out <- massi_select(expression_data=massi.test.dataset, y_probes=massi.test.probes, threshold=4)

# Use the list returned from massi.select to calculate dip statistics and z-scores.
massi_dip_out <- massi_dip(y_subset_values=massi_select_out)

# view a density plot
plot(massi_dip_out[[3]])

# view a histogram of z-scores
hist(x=massi_dip_out[[2]])

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(massiR)
Loading required package: cluster
Loading required package: gplots

Attaching package: 'gplots'

The following object is masked from 'package:stats':

    lowess

Loading required package: diptest
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/massiR/massi_dip.Rd_%03d_medium.png", width=480, height=480)
> ### Name: massi_dip
> ### Title: massi_dip
> ### Aliases: massi_dip
> 
> ### ** Examples
> 
> 
> # load the test dataset
> data(massi.test.dataset, massi.test.probes)
> 
> massi_select_out <- massi_select(expression_data=massi.test.dataset, y_probes=massi.test.probes, threshold=4)
> 
> # Use the list returned from massi.select to calculate dip statistics and z-scores.
> massi_dip_out <- massi_dip(y_subset_values=massi_select_out)
dip test statistic is >0.08. This suggests that the proportion of male and female samples in this dataset is relatively balanced.
> 
> # view a density plot
> plot(massi_dip_out[[3]])
> 
> # view a histogram of z-scores
> hist(x=massi_dip_out[[2]])
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>