inst/doc/mutation.R

In TCGAbiolinks: TCGAbiolinks: An R/Bioconductor package for integrative analysis with GDC data

## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_knit$set(progress = FALSE)

## ----message=FALSE, warning=FALSE, include=FALSE------------------------------
library(TCGAbiolinks)
library(SummarizedExperiment)
library(dplyr)
library(DT)

## ----results = 'hide', echo=TRUE, message=FALSE, warning=FALSE,eval=F---------
#  maf <- GDCquery_Maf("CHOL", pipelines = "muse")

## ----results = 'hide', echo=TRUE, message=FALSE, warning=FALSE,eval=T,include=F----
maf <- chol_maf@data

## ----echo = TRUE, message = FALSE, warning = FALSE----------------------------
# Only first 50 to make render faster
datatable(maf[1:20,],
          filter = 'top',
          options = list(scrollX = TRUE, keys = TRUE, pageLength = 5), 
          rownames = FALSE)

## ----results = 'hide', echo=TRUE, message=FALSE, warning=FALSE----------------
query.maf.hg19 <- GDCquery(project = "TCGA-CHOL", 
                           data.category = "Simple nucleotide variation", 
                           data.type = "Simple somatic mutation",
                           access = "open", 
                           legacy = TRUE)

## ----echo = TRUE, message = FALSE, warning = FALSE----------------------------
# Check maf availables
datatable(dplyr::select(getResults(query.maf.hg19),-contains("cases")),
          filter = 'top',
          options = list(scrollX = TRUE, keys = TRUE, pageLength = 10), 
          rownames = FALSE)

## ----results = 'hide', echo=TRUE, message=FALSE, warning=FALSE,eval=FALSE-----
#  query.maf.hg19 <- GDCquery(project = "TCGA-CHOL",
#                             data.category = "Simple nucleotide variation",
#                             data.type = "Simple somatic mutation",
#                             access = "open",
#                             file.type = "bcgsc.ca_CHOL.IlluminaHiSeq_DNASeq.1.somatic.maf",
#                             legacy = TRUE)
#  GDCdownload(query.maf.hg19)
#  maf <- GDCprepare(query.maf.hg19)

## ----message=FALSE, warning=FALSE, include=FALSE------------------------------
data <- bcgsc.ca_CHOL.IlluminaHiSeq_DNASeq.1.somatic.maf

## ----echo = TRUE, message = FALSE, warning = FALSE----------------------------
# Only first 50 to make render faster
datatable(maf[1:20,],
          filter = 'top',
          options = list(scrollX = TRUE, keys = TRUE, pageLength = 5), 
          rownames = FALSE)

## ----results = 'hide', echo=TRUE, message=FALSE, warning=FALSE,eval=FALSE-----
#  maf <- getMC3MAF()

## ----results = "hide",echo = TRUE, message = FALSE, warning = FALSE, eval=FALSE----
#  library(maftools)
#  library(dplyr)
#  maf <- GDCquery_Maf("CHOL", pipelines = "muse") %>% read.maf

## ----message=FALSE, warning=FALSE, include=FALSE------------------------------
library(maftools)
library(dplyr)
maf <- chol_maf

## ----results = "hide",echo = TRUE, message = FALSE, warning = FALSE-----------
datatable(getSampleSummary(maf),
          filter = 'top',
          options = list(scrollX = TRUE, keys = TRUE, pageLength = 5), 
          rownames = FALSE)
plotmafSummary(maf = maf, rmOutlier = TRUE, addStat = 'median', dashboard = TRUE)

## ----echo = TRUE, message = FALSE,eval = FALSE, warning = FALSE---------------
#  oncoplot(maf = maf, top = 10, removeNonMutated = TRUE)
#  titv = titv(maf = maf, plot = FALSE, useSyn = TRUE)
#  #plot titv summary
#  plotTiTv(res = titv)