heatmapPhenoTest {phenoTest} | R Documentation |
Show the associations between clusters that each sample belongs to and each phenotype in a heatmap and/or a Kaplan-Meier plot.
heatmapPhenoTest(x, signatures, vars2test, probes2genes = FALSE, filterVar, filteralpha = 0.05, distCol = "pearson", nClust = 2, distRow = "cor", p.adjust.method = "none", simulate.p.value = FALSE, B = 10^5, linkage = "average", equalize = FALSE, center = TRUE, col, survCol, heat.kaplan="both", ...)
x |
ExpressionSet with phenotype information stored in |
signatures |
Either character vector or list of character vectors
with gene sets to be used to draw heatmaps (gene names should match
those in |
vars2test |
list with components 'continuous', 'categorical',
'ordinal' and 'survival' indicating which phenotype variables should
be tested. 'continuous', 'categorical' and 'ordinal' must be character
vectors, 'survival' a matrix with columns named 'time' and
'event'. The names must match names in |
probes2genes |
If set to |
filterVar |
If specified, only genes with significant differences
in the variable |
filteralpha |
Significance level for the filtering based on |
distCol |
Distance metric used to cluster columns
(e.g. patients/samples). Can take any value accepted by
|
nClust |
Number of desired clusters. |
distRow |
Distance metric used to cluster rows (e.g. genes). Can
take any value accepted by |
p.adjust.method |
Method for P-value adjustment, passed on to
|
simulate.p.value |
If set to FALSE the chi-square test p-values are
computed using asymptotics, otherwise a simulation is used (see
|
B |
An integer specifying the number of replicates used in the
chi-square Monte Carlo test (passed on to |
linkage |
Linkage used for clustering. Must be either 'complete', 'average' or 'minimum'. |
equalize |
Should color codes be equalized between genes, i.e. all
genes present the same range of colors. Passed on to
|
center |
centering is done by subtracting the column means (omitting NAs). |
col |
Color scheme to be used for heatmap. Defaults to a green/red scheme designed to look nice for microarray data. |
survCol |
Colors for the Kaplan-Meier survival curves. |
heat.kaplan |
can be "heat" if we want to plot a heatmap, "kaplan" if we want to plot a kaplan-meier or "both" if we want both of them. |
... |
Other arguments for the survival plot, e.g. lty etc. |
Makes two clusters of samples based on the expression levels of the genes from the given signature and plots a heatmap and/or a Kaplan-Meier showing the association between belonging to one cluster or the other and each phenotype.
For variables in vars2test\$continuous and vars2test\$ordinal a Kruskal-Wallis Rank Sum test is
used; for vars2test\$categorical a chi-square test (with exact p-value
if simulate.p.value
is set to TRUE); for var2test\$survival a Cox proportional hazards likelihood-ratio test.
David Rossell
#load data data(eset) eset #construct vars2test survival <- matrix(c("Relapse","Months2Relapse"),ncol=2,byrow=TRUE) colnames(survival) <- c('event','time') vars2test <- list(survival=survival) vars2test #construct a signature sign <- sample(featureNames(eset))[1:20] #make plot heatmapPhenoTest(eset,sign,vars2test=vars2test,heat.kaplan='heat') heatmapPhenoTest(eset,sign,vars2test=vars2test,heat.kaplan='kaplan')