Training dataset. A matrix, data.frame,
ExpressionSet or
marrayRaw-class.
If the input is gene expression data in a matrix or data.frame. The
rows and columns are expected to contain the variables (genes) and cases (array samples)
respectively.
supdata
Test or blind dataset. A matrix, data.frame,
ExpressionSet or
marrayRaw-class.
If the input is gene expression data in a matrix or data.frame. The
rows and columns are expected to contain the variables (genes) and cases (array samples)
respectively. The test dataset supdata and the training dataset dataset must contain
the same number of variables (genes).
classvec
A factor or vector which describes the classes in the
training data dataset.
supvec
A factor or vector which describes the classes in the
test dataset supdata.
suponly
Logical indicating whether the returned output should contain
the test class assignment results only. The default value is FALSE, that
is the training coordinates, test coordinates and class assignments
will all be returned.
type
Character, "coa", "pca" or "nsc" indicating which data
transformation is required. The default value is type="coa".
...
further arguments passed to or from other methods.
Details
bga.suppl calls bga to perform between group analysis (bga) on the training dataset,
then it calls suppl to project the test dataset onto the bga axes.
It returns the coordinates and class assignment of the cases (microarray samples) in the test dataset as
described by Culhane et al., 2002.
The test dataset must contain the same number of variables (genes) as the training dataset.
The input format of both the training dataset and test dataset are verified using array2ade4.
Use plot.bga to plot results from bga.
Value
If suponly is FALSE (the default option) bga.suppl returns a list of length 4 containing
the results of the bga of the training dataset and the results of the projection of the test dataset onto the bga axes-
ord
Results of initial ordination. A list of class "dudi" (see dudi).
bet
Results of between group analysis. A list of class "dudi"
(see dudi),"between" (see bca),and
"dudi.bga"(see bga)
fac
The input classvec, the factor or vector which described the classes in the input dataset
suppl
An object returned by suppl
If suponly is TRUE only the results from suppl will be returned.
Author(s)
Aedin Culhane
References
Culhane AC, et al., 2002 Between-group analysis of microarray data. Bioinformatics. 18(12):1600-8.
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(made4)
Loading required package: ade4
Loading required package: RColorBrewer
Loading required package: gplots
Attaching package: 'gplots'
The following object is masked from 'package:stats':
lowess
Loading required package: scatterplot3d
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/made4/bga.suppl.Rd_%03d_medium.png", width=480, height=480)
> ### Name: bga.suppl
> ### Title: Between group analysis with supplementary data projection
> ### Aliases: bga.suppl
> ### Keywords: manip multivariate
>
> ### ** Examples
>
> data(khan)
> #khan.bga<-bga(khan$train, khan$train.classes)
> if (require(ade4, quiet = TRUE)) {
+ khan.bga<-bga.suppl(khan$train, supdata=khan$test,
+ classvec=khan$train.classes, supvec=khan$test.classes)
+
+ khan.bga
+ plot.bga(khan.bga, genelabels=khan$annotation$Symbol)
+ khan.bga$suppl
+ }
projected.Axis1 projected.Axis2 projected.Axis3 closest.centre
TEST.9 0.144217695 -0.03756710 -0.26853022 RMS
TEST.11 -0.136017937 -0.16866019 0.07956366 EWS
TEST.5 -0.060161794 -0.12139790 0.08610628 EWS
TEST.8 0.041624485 -0.09381448 0.71659297 NB
TEST.10 0.457662199 0.15530992 -0.32800744 RMS
TEST.13 0.347330526 0.17379130 -0.31684727 RMS
TEST.3 -0.072552995 -0.07504645 0.11764633 EWS
TEST.1 -0.044879003 -0.08947335 0.59286679 NB
TEST.2 -0.365523095 0.15193627 0.09126039 EWS
TEST.4 0.905928581 0.16243275 -0.39133749 RMS
TEST.7 -0.472635365 -0.88353241 -0.48829697 BL-NHL
TEST.12 -0.309195706 0.43920807 -0.08800957 EWS
TEST.24 0.498252242 0.08902262 -0.27111726 RMS
TEST.6 -0.394941698 0.40777605 -0.07627262 EWS
TEST.21 0.001601680 0.25057279 -0.28492924 EWS
TEST.20 -0.044736965 -0.01576323 -0.26285126 EWS
TEST.17 0.645753685 0.12345539 -0.28434590 RMS
TEST.18 -0.427844894 -0.73785093 -0.21056339 BL-NHL
TEST.22 0.569007217 0.05188448 -0.29784705 RMS
TEST.16 0.031688008 -0.03913595 0.64855728 NB
TEST.23 0.082386157 -0.04208780 0.32229789 NB
TEST.14 0.001664744 -0.08643769 0.60558519 NB
TEST.25 -0.001839899 -0.06478855 0.63870222 NB
TEST.15 -0.481244259 -0.82846001 -0.43606444 BL-NHL
TEST.19 -0.475725042 0.46393156 -0.04042476 EWS
predicted true.class
TEST.9 RMS Normal
TEST.11 EWS Normal
TEST.5 EWS Normal
TEST.8 NB NB
TEST.10 RMS RMS
TEST.13 RMS Normal
TEST.3 EWS Normal
TEST.1 NB NB
TEST.2 EWS EWS
TEST.4 RMS RMS
TEST.7 BL-NHL BL-NHL
TEST.12 EWS EWS
TEST.24 RMS RMS
TEST.6 EWS EWS
TEST.21 EWS EWS
TEST.20 EWS EWS
TEST.17 RMS RMS
TEST.18 BL-NHL BL-NHL
TEST.22 RMS RMS
TEST.16 NB NB
TEST.23 NB NB
TEST.14 NB NB
TEST.25 NB NB
TEST.15 BL-NHL BL-NHL
TEST.19 EWS EWS
>
>
>
>
>
> dev.off()
null device
1
>