Nothing
## ---- echo=FALSE, message=FALSE, warning=FALSE--------------------------------
## ---- echo=TRUE---------------------------------------------------------------
# load divergence package
library(divergence)
# baseline cohort
mat.base = breastTCGA_Mat[, which(breastTCGA_Group == "NORMAL")]
# make SummarizedExperiment
seMat.base = SummarizedExperiment(assays=list(data=mat.base))
# data conort
mat.data = breastTCGA_Mat[, which(breastTCGA_Group == "TUMOR")]
# make SummarizedExperiment
seMat = SummarizedExperiment(assays=list(data=mat.data))
## ---- echo=TRUE---------------------------------------------------------------
# convert normal data to quantiles
assays(seMat.base)$quantile = computeQuantileMatrix(seMat.base)
# view
assays(seMat.base)$quantile[1:5, 1:4]
# convert tumor data to quantiles
assays(seMat)$quantile = computeQuantileMatrix(seMat)
# view
assays(seMat)$quantile[1:5, 1:4]
## ---- echo=TRUE---------------------------------------------------------------
baseline1 = computeUnivariateSupport(
seMat=seMat.base,
gamma=0.1,
beta=0.99,
parallel=FALSE
)
## ---- echo=TRUE---------------------------------------------------------------
head(baseline1$Ranges)
baseline1$Support[1:5, 1:4]
baseline1$alpha
## ---- echo=TRUE---------------------------------------------------------------
baseline2 = findUnivariateGammaWithSupport(
seMat=seMat.base,
gamma=1:9/10,
beta=0.9,
alpha=0.005,
parallel=FALSE
)
## ---- echo=TRUE---------------------------------------------------------------
# selected gamma
baseline2$gamma
# alpha value: i.e. number of divergent features per sample in the baseliine cohort
baseline2$alpha
# does the selected gamma meet the alpha threshold required by the user?
baseline2$optimal
# view the alpha values obtained for each of the gamma values searched through
baseline2$alpha_space
## ---- echo=TRUE---------------------------------------------------------------
head(baseline2$Ranges)
## ---- echo=TRUE---------------------------------------------------------------
assays(seMat)$div = computeUnivariateTernaryMatrix(
seMat = seMat,
Baseline = baseline2
)
assays(seMat)$div[1:5, 1:4]
## ---- echo=TRUE---------------------------------------------------------------
div.2 = computeUnivariateDigitization(
seMat = seMat,
seMat.base = seMat.base,
computeQuantiles = TRUE,
gamma = 1:9/10,
beta = 0.9,
alpha = 0.01,
parallel = FALSE
)
# digitized data matrix
dim(div.2$Mat.div)
div.2$Mat.div[1:5, 1:4]
# digitized baseline matrix
dim(div.2$baseMat.div)
div.2$baseMat.div[1:5, 1:4]
# divergent set size in each data matrix sample
head(div.2$div)
# divergence probability of features
head(div.2$features.div)
# baseline information
head(div.2$Baseline$Ranges)
div.2$Baseline$gamma
## ---- echo=TRUE---------------------------------------------------------------
# make a factor of the ER status phenotype
selection = which(colnames(breastTCGA_Mat) %in% colnames(seMat))
groups.ER = breastTCGA_ER[selection]
table(groups.ER, useNA='i')
# perform divergence
div.3 = computeUnivariateDigitization(
seMat = seMat,
seMat.base = seMat.base,
computeQuantiles = FALSE,
gamma = 1:9/10,
Groups = groups.ER,
classes = c("Positive", "Negative"),
parallel = FALSE
)
# divergence probability of features
head(div.3$features.div)
## ---- echo=TRUE, warning=FALSE------------------------------------------------
chi.1 = computeChiSquaredTest(
Mat = assays(seMat)$div,
Groups = groups.ER,
classes = c("Positive", "Negative")
)
head(chi.1, 10)
## ---- echo=TRUE---------------------------------------------------------------
length(msigdb_Hallmarks)
lapply(msigdb_Hallmarks[1:3], head)
## ---- echo=TRUE---------------------------------------------------------------
baseline3 = computeMultivariateSupport(
seMat=seMat.base,
FeatureSets = msigdb_Hallmarks,
gamma=0.1,
beta=0.99
)
## ---- echo=TRUE---------------------------------------------------------------
baseline4 = findMultivariateGammaWithSupport(
seMat = seMat.base,
FeatureSets = msigdb_Hallmarks,
gamma=1:9/10,
beta=0.9,
alpha = .01
)
# selected gamma
baseline4$gamma
# alpha value: i.e. number of divergent multivariate features per sample in the baseliine cohort
baseline4$alpha
# does the selected gamma meet the alpha threshold required by the user?
baseline4$optimal
# view the alpha values obtained for each of the gamma values searched through
baseline4$alpha_space
## ---- echo=TRUE---------------------------------------------------------------
mat.div.2 = computeMultivariateBinaryMatrix(
seMat = seMat,
Baseline = baseline4
)
dim(mat.div.2)
mat.div.2[1:5, 1:4]
## ---- echo=TRUE---------------------------------------------------------------
# perform divergence
div.4 = computeMultivariateDigitization(
seMat = seMat,
seMat.base = seMat.base,
FeatureSets = msigdb_Hallmarks,
computeQuantiles = TRUE,
gamma = 1:9/10,
Groups = groups.ER,
classes = c("Positive", "Negative")
)
# digitized data matrix
dim(div.4$Mat.div)
div.4$Mat.div[1:5, 1:4]
# digitized baseline matrix
dim(div.4$baseMat.div)
div.4$baseMat.div[1:5, 1:4]
# divergent set size in each data matrix sample
head(div.4$div)
# divergence probability of features
head(div.4$features.div)
# baseline information
div.2$Baseline$gamma
div.2$Baseline$alpha
div.2$Baseline$optimal
div.2$Baseline$alpha_space
## ---- echo=TRUE, warning=FALSE------------------------------------------------
chi.2 = computeChiSquaredTest(
Mat = div.4$Mat.div,
Groups = groups.ER,
classes = c("Positive", "Negative")
)
head(chi.2, 10)
## ---- echo=TRUE, warning=FALSE------------------------------------------------
sessionInfo()
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