Description Usage Arguments Value Examples
Fit linear models and/or contrasts to data from Figueroe et al. 2010.
1 |
eset |
an ExpressionSet of HELP data (e.g. data(DNAme)) |
design |
a formula for a linear model to fit to the data with limma |
... |
any other parameters to pass to lmFit (e.g. method="robust") |
1 | results from eBayes(lmFit(eset, design, ...))
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | data(DNAme, package="WorldsSimplestCodeReview")
covariates <- pData(DNAme) # pData == "pheno-data", a very old function
library(limma)
# let's fit a model with the specified bone marrow blast % (but beware...)
design <- with(covariates, model.matrix( ~ IDH:purity + TET2:purity + male))
fitWithPurityAndSex <- TET2(DNAme, design, method="robust")
# if you look at DNAme$purity, it doesn't make a lot of sense
# so let's compare against normal bone marrows (NBM) instead:
DNAme$NBM <- grepl("NBM", DNAme$title)
DNAme$AML <- !DNAme$NBM # the non-NBMs are all AMLs
design2 <- with(pData(DNAme), model.matrix( ~ IDH:AML + TET2:AML + AML))
fitAgainstNBMs <- TET2(DNAme, design2) # AML-specific hyper now in coef 4!
colnames(design2) # for reference below
message("IDH vs. non-mutant AML @ p_adj < 0.05:")
nrow(topTable(fitAgainstNBMs, coef=2, p.val=0.05, n=Inf)))
message("TET2 vs. non-mutant AML @ p_adj < 0.05:")
nrow(topTable(fitAgainstNBMs, coef=3, p.val=0.05, n=Inf))
message("AML vs. normal bone marrow @ p_adj < 0.05:")
nrow(topTable(fitAgainstNBMs, coef=4, p.val=0.05, n=Inf))
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