## ----message=FALSE------------------------------------------------------------
library(sesame)
library(dplyr)
library(tidyr)
library(SummarizedExperiment)
library(ggplot2)

## ----message=FALSE------------------------------------------------------------
se = sesameDataGet("MM285.10.tissues")[1:1000,] # use a random 1000 probes
colData(se)

## -----------------------------------------------------------------------------
se_ok = (checkLevels(assay(se), colData(se)$sex) &
    checkLevels(assay(se), colData(se)$tissue))
sum(se_ok)
se = se[se_ok,]

## -----------------------------------------------------------------------------
colData(se)$tissue <- relevel(factor(colData(se)$tissue), "Colon")
colData(se)$sex <- relevel(factor(colData(se)$sex), "Female")

## -----------------------------------------------------------------------------
smry = DML(se, ~tissue + sex)
smry

## -----------------------------------------------------------------------------
test_result = summaryExtractTest(smry)
colnames(test_result) # the column names, show four groups of statistics
head(test_result)

## -----------------------------------------------------------------------------
test_result %>% dplyr::filter(FPval_sex < 0.05, Eff_sex > 0.1) %>%
    select(Pval_sexMale, Eff_sex)

## -----------------------------------------------------------------------------
test_result %>%
    mutate(sex_specific =
        ifelse(FPval_sex < 0.05 & Eff_sex > 0.1, TRUE, FALSE)) %>%
    mutate(tissue_specific =
        ifelse(FPval_tissue < 0.05 & Eff_tissue > 0.1, TRUE, FALSE)) %>%
    select(sex_specific, tissue_specific) %>% table

## -----------------------------------------------------------------------------
ggplot(test_result) + geom_point(aes(Est_sexMale, -log10(Pval_sexMale)))

## -----------------------------------------------------------------------------
ggplot(test_result) + geom_point(aes(Est_tissueFat, -log10(Pval_tissueFat)))

## -----------------------------------------------------------------------------
cf_list = summaryExtractCfList(smry)
cf_list = DMR(se, cf_list$sexMale)
topSegments(cf_list) %>% dplyr::filter(Seg.Pval.adj < 0.05)

## ----message=FALSE, warning=FALSE, include=FALSE------------------------------
library(sesame)
library(dplyr)
options(rmarkdown.html_vignette.check_title = FALSE)

## ---- message=FALSE, fig.width=6, fig.height=5--------------------------------
betas <- sesameDataGet('HM450.10.TCGA.PAAD.normal')
visualizeGene('DNMT1', betas, platform='HM450')

## ---- message=FALSE, fig.width=6, fig.height=5--------------------------------
visualizeRegion(
    'chr19',10260000,10380000, betas, platform='HM450',
    show.probeNames = FALSE)

## ---- message=FALSE, fig.width=6----------------------------------------------
visualizeProbes(c("cg02382400", "cg03738669"), betas, platform='HM450')