Description Usage Arguments Value Author(s) References See Also Examples
Predict to which class the sample is most likely to belong, using a modified LPS model.
1 |
peaks |
A |
model |
A |
plot |
Single logical value, whether to plot a visual representation of the prediction or not. |
Returns a list :
score |
The raw score used to make the prediction. |
p |
The probability to belong to each of the two groups. |
class |
The final prediction, as a group name. May be |
Sylvain Mareschal
Radmacher MD, McShane LM, Simon R. A paradigm for class prediction using gene expression profiles. J Comput Biol. 2002;9(3):505-11.
Wright G, Tan B, Rosenwald A, Hurt EH, Wiestner A, Staudt LM. A gene expression-based method to diagnose clinically distinct subgroups of diffuse large B cell lymphoma. Proc Natl Acad Sci U S A. 2003 Aug 19;100(17):9991-6.
Bohers E, Mareschal S, Bouzelfen A, Marchand V, Ruminy P, Maingonnat C, Menard AL, Etancelin P, Bertrand P, Dubois S, Alcantara M, Bastard C, Tilly H, Jardin F. Targetable activating mutations are very frequent in GCB and ABC diffuse large B-cell lymphoma. Genes Chromosomes Cancer. 2014 Feb;53(2):144-53.
read.fsa
, peaks.fsa
, model
, GEP.process
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | # Example FSA file provided
fsa <- read.fsa(system.file("extdata/fsa/A5918.fsa", package="MLPA"))
fsa <- align.fsa(fsa)
# Build model from design file
design <- designFile(system.file("extdata/design.conf", package="MLPA"))
design$model$disable <- NULL
model <- do.call("model", design$model)
# Get peak heights
peaks <- peaks.fsa(fsa, ranges=design$PEAKS$ranges)
# Classify sample
p <- classify(peaks, model, plot=TRUE)
print(p)
# Observe sample in model
plot(model)
abline(v=p$score)
|
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