# load test data
x <- get.test.data(data.types = c("mRNA.T", "mRNA.N", "CNA", "ann"));
# list of features to be assessed for differential expression
feature.ids <- rownames(x$mRNA.T$BLCA);
# get differentially expressed features
DE.results <- find.DE.features(
exp.data.T = x$mRNA.T,
exp.data.N = x$mRNA.N,
feature.ids = feature.ids,
test.name = "t.test"
);
# get top features
top.features <- get.top.features(
DE.features = cbind("FC" = DE.results[, 1], "P" = DE.results[, 2]),
cna.data.fractions = x$CNA.fractions$BLCA,
mRNA.FC.up = 0.25,
mRNA.FC.down = 0.25,
mRNA.p = 0.05,
mRNA.top.n = NULL,
cna.fractions.gain = 0.2,
cna.fractions.loss = 0.2
);
# temporary output directory
tmp.output.dir <- tempdir();
# estimate mRNA and CNA correlation using the pre-selected top features
correlated.features <- estimate.expression.cna.correlation(
exp.data = x$mRNA.T$BLCA,
cna.data.log2 = x$CNA.log2$BLCA,
corr.threshold = 0.3,
corr.direction = "two.sided",
subtypes.metadata = list(
"subtype.samples.list" = list("All" = colnames(x$mRNA.T$BLCA))
),
feature.ids = top.features,
cancer.type = "BLCA",
data.dir = paste(tmp.output.dir, "/data/BLCA/", sep = ""),
graphs.dir = paste(tmp.output.dir, "/graphs/BLCA/", sep = "")
);
Run the code above in your browser using DataLab