GOLUB {cancerclass} | R Documentation |
Gene expression data from the leukemia microarray study of Golub et al. [1]. Dataset GOLUB has a dimention of 7129 genes in 72 tumors samples. Dataset GOLUB1 has a dimention of 3571 genes in 72 tumors samples. This dataset is filtered and preprocessed as described in [2].
data(GOLUB) data(GOLUB1)
Data and annotations are organized in a ExtressenSet of the package Biobase.
GOLUB |
ExpressionSet (7129 genes in 72 tumors) |
GOLUB1 |
ExpressionSet (3571 genes in 72 tumors) |
[1] Golub TR et al (1999), Molecular Classification of cancer: class Discovery and Class Prediction by gene expression monitoring, Science 286:531-7.
[2] Dudoit S, Fridlyand J (2002), A prediction-based resampling method for estimating the number of clusters in a dataset, Genome Biol. 3(7):RESEARCH0036.
### nvalidate data(GOLUB1) nval <- nvalidate(GOLUB1[1:200, ]) # Use only the first 200 genes for speed-up of the calculations plot(nval, type="xy") plot(nval, type="genes") plot(nval, type="samples") ### validate data(GOLUB1) val <- validate(GOLUB1[1:200, ]) # Use only the first 200 genes for speed-up of the calculations plot(val, type="xy") plot(val, type="genes") plot(val, type="samples") ### fit und predict data(GOLUB1) train <- GOLUB1[, 1:38] test <- GOLUB1[, 39:72] predictor <- fit(train, method="welch.test") prediction <- predict(predictor, test, positive="AML", ngenes=50, dist="cor") plot(prediction, type="histogram", score="zeta") plot(prediction, type="curves", score="zeta") plot(prediction, type="roc", score="zeta") summary(prediction) ### loo data(GOLUB1) cv <- loo(GOLUB1, positive="AML", ngenes=10, method="welch.test", dist="cor") plot(cv, type="histogram", score="zeta") plot(cv, type="samples", score="zeta") plot(cv, type="curves", score="zeta") plot(cv, type="roc", score="zeta")