Last data update: 2014.03.03

 R: massi_cluster
massi_clusterR Documentation

massi_cluster

Description

The massi_cluster function predicts the sex of samples using k-medoids clustering.

Usage

massi_cluster(y_data)

Arguments

y_data

the y_data object is the data.frame returned from the massi_select function. This is a data.frame with sample names as column names and probe id's as row.names.

Details

This function clusters samples into two clusters using y chromosome probe values. K-medoids clustering is performed using the partitioning around medoids (pam) method implimented in the "fpc" package. The cluster with the highest probe values is determined to be the cluster of male samples and the cluster the lowest values as female samples.

Value

cluster data

Contains all of the results from the k-medoids clustering.

massi.results

Contains the results for each sample, including sample id, predicted sex, sample z-score and mean probe expression.

Author(s)

Sam Buckberry

References

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

See Also

massi_y, massi_select, massi_y_plot, massi_dip, massi_cluster_plot

Examples


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

# select the y chromosome probes using massi_select
massi_select_out <- 
massi_select(massi.test.dataset, massi.test.probes)

# cluster samples to predict sex using massi_cluster
massi_cluster_out <- 
massi_cluster(massi_select_out)

# get the results in a data.frame format
data.frame(massi_cluster_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_cluster.Rd_%03d_medium.png", width=480, height=480)
> ### Name: massi_cluster
> ### Title: massi_cluster
> ### Aliases: massi_cluster
> 
> ### ** Examples
> 
> 
> # load the test dataset
> data(massi.test.dataset, massi.test.probes)
> 
> # select the y chromosome probes using massi_select
> massi_select_out <- 
+ massi_select(massi.test.dataset, massi.test.probes)
> 
> # cluster samples to predict sex using massi_cluster
> massi_cluster_out <- 
+ massi_cluster(massi_select_out)
> 
> # get the results in a data.frame format
> data.frame(massi_cluster_out[[2]])
    ID mean_y_probes_value y_probes_sd     z_score    sex
1   S1            5.680165   0.4090441 -0.85585504 female
2  S10            6.170169   0.8394536  0.44782165   male
3  S11            5.603765   0.3702051 -0.81252502 female
4  S12            6.173442   0.7756386  0.62083674   male
5  S13            5.698972   0.4973658 -0.45498169 female
6  S14            5.671378   0.4943536 -0.58020581 female
7  S15            5.698598   0.6092190 -0.52876863 female
8  S16            6.204787   0.9358266  0.46209119   male
9  S17            5.643580   0.3950373 -0.68303870 female
10 S18            6.158790   0.7745952  0.49253628   male
11 S19            5.683547   0.3772903 -0.49106943 female
12  S2            5.725198   0.4202718 -0.41740176 female
13 S20            6.197443   0.8684864  0.52636778   male
14 S21            6.235593   0.9103953  0.69597729   male
15 S22            6.186751   0.8736938  0.59480439   male
16 S23            6.300154   0.9410008  0.76469585   male
17 S24            5.629225   0.4451357 -0.78678273 female
18 S25            6.247860   0.9653944  0.62357532   male
19 S26            6.180581   0.8510991  0.37554456   male
20 S27            5.620330   0.3004966 -0.63975338 female
21 S28            5.708171   0.4438601 -0.32440462 female
22 S29            6.156075   0.9390711  0.25273543   male
23  S3            6.115781   0.7561232  0.29708553   male
24 S30            5.656060   0.3813885 -0.54925196 female
25 S31            6.169508   0.9305175  0.35708098   male
26 S32            6.155072   0.8373847  0.34490811   male
27 S33            5.649092   0.4124183 -0.73064375 female
28 S34            6.142749   0.8462364  0.23406710   male
29 S35            6.212895   0.9091188  0.60203617   male
30 S36            6.166242   0.9208438  0.33055196   male
31 S37            5.695139   0.4255875 -0.22808642 female
32 S38            6.146158   0.8042844  0.62779880   male
33 S39            5.736517   0.5181531 -0.09019286 female
34  S4            6.158195   0.7622165  0.50511011   male
35 S40            5.651175   0.4587101 -0.70773294 female
36 S41            5.691771   0.5619696 -0.37876345 female
37 S42            6.238056   0.9162148  0.70391723   male
38 S43            6.235513   0.7535050  1.07926344   male
39 S44            5.675913   0.3643422 -0.46755050 female
40 S45            6.218716   0.8796017  0.67120180   male
41 S46            5.674705   0.4246131 -0.61329772 female
42 S47            5.691977   0.5917014 -0.62546100 female
43 S48            5.605190   0.2344730 -0.81781208 female
44 S49            6.252667   0.9541327  0.73467290   male
45  S5            6.124133   0.8023630  0.17459905   male
46 S50            6.032971   0.6670877  0.30287712   male
47 S51            6.100700   0.7833503  0.18172381   male
48 S52            5.735546   0.3397371 -0.38477975 female
49 S53            6.090985   0.6926929  0.30649661   male
50 S54            6.148481   0.7163452  0.65216371   male
51 S55            5.716399   0.4455840 -0.53489323 female
52 S56            6.094476   0.7517557  0.18900576   male
53 S57            6.059073   0.6616431  0.13405343   male
54 S58            5.869729   0.5446749  0.33492939 female
55 S59            5.734061   0.4127407 -0.50774683 female
56  S6            5.611190   0.4744621 -0.85597382 female
57 S60            5.800338   0.4242135 -0.09467263 female
58  S7            5.701315   0.4420199 -0.27489193 female
59  S8            6.160184   0.9019628  0.33816949   male
60  S9            5.630080   0.3119106 -0.52216125 female
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>


Created & Maintained by Osamu Ogasawara (osamu.ogasawara@gmail.com) and IMSBIO