Supervised learning (no date) [incomplete]
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Here are some documented examples of how to use supervised learning methods of the analysis of microarray data.
Supervised learning
The Bioconductor package has a sample data set, golubEsets, that is available on the web at
http://www-genome.wi.mit.edu/mpr/data_set_ALL_AML.html
and is based on the publication
Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh M, Downing JR, Caligiuri MA, Bloomfield CD, Lander ES. Science 1999: 286; 531-537. [PDF]
To get this data, use the following commands:
library("marray")
library("golubEsets")
data(golubTrain)
data(golubTest)
data(golubMerge)The phenotypic data for the training data set looks like
> pData(golubTrain)
Samples ALL.AML BM.PB T.B.cell ... Source
1 1 ALL BM B-cell ... DFCI
2 2 ALL BM T-cell ... DFCI
3 3 ALL BM T-cell ... DFCI
.
.
.
32 32 AML BM <NA> ... CALGB
33 33 AML BM <NA> ... CALGB
table(pData(golubTrain)$ALL.AML)
ALL AML
27 11
table(pData(golubTest)$ALL.AML)
ALL AML
20 14
table(pData(golubMerge)$ALL.AML)
ALL AML
47 25
x <- exprs(GolubTrain)
Supervised learning (class prediction)
Regression analysis, discriminant analysis, cart, neural net
support vector machines: http://www.acm.org/sigs/sigkdd/explorations/issue2-2/bennett.pdf