This dataset consists of gene expression profiles of the three most prevalent adult lymphoid malignancies: diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL), and B-cell chronic lymphocytic leukemia (B-CLL). The dataset consists of 4,682 genes in 62 mRNA samples: 42 samples of DLBCL, 9 samples of FL, and 11 samples. of B-CLL. Missing value are imputed and data are standardized as described in Dudoit, et al. (2002).
Usage
data(lymphoma)
Value
A list with the following elements:
data
Gene expression data. A matrix with 62 rows and 4,682 columns.
class
Class index. A vector with 62 elements.
References
Alizadeh AA, Eisen MB, Davis RE, et al.
Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 2000;403(6769):503-511.
Dudoit S, Fridlyand J, Speed TP.
Comparison of discrimination methods for the classification of tumors using gene expression data. J Am Stat Assoc 2002;97(417):77-87.
Examples
data(lymphoma)
class=1+as.numeric(as.factor(unlist(lymphoma$class)))
cc=cmdscale(dist(lymphoma$data))
plot(cc,pch=21,bg=class,xlab="First Component",ylab="Second Component");
# WARNING: The next example is high computational extensive
#
# kk=KODAMA(lymphoma$data,FUN=PLS.SVM.CV)
# cc=cmdscale(kk$dissimilarity)
# plot(cc,pch=21,bg=class,xlab="First Component",ylab="Second Component");
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)
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Type 'demo()' for some demos, 'help()' for on-line help, or
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> library(KODAMA)
Loading required package: e1071
Loading required package: plsgenomics
Loading required package: MASS
Loading required package: boot
Loading required package: parallel
Loading required package: class
Attaching package: 'KODAMA'
The following object is masked from 'package:plsgenomics':
transformy
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/KODAMA/lymphoma.Rd_%03d_medium.png", width=480, height=480)
> ### Name: lymphoma
> ### Title: Lymphoma Gene Expression Dataset
> ### Aliases: lymphoma
> ### Keywords: datasets
>
> ### ** Examples
>
> data(lymphoma)
> class=1+as.numeric(as.factor(unlist(lymphoma$class)))
> cc=cmdscale(dist(lymphoma$data))
> plot(cc,pch=21,bg=class,xlab="First Component",ylab="Second Component");
>
> # WARNING: The next example is high computational extensive
> #
> # kk=KODAMA(lymphoma$data,FUN=PLS.SVM.CV)
> # cc=cmdscale(kk$dissimilarity)
> # plot(cc,pch=21,bg=class,xlab="First Component",ylab="Second Component");
>
>
>
>
>
> dev.off()
null device
1
>
providing 
.