## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## -----------------------------------------------------------------------------
library(maftools)

## ----eval=FALSE---------------------------------------------------------------
# remotes::install_github(repo = 'VanLoo-lab/ascat/ASCAT')

## ----eval=FALSE---------------------------------------------------------------
# #Matched normal BAM files are strongly recommended
# counts = maftools::gtMarkers(t_bam = "tumor.bam",
#                              n_bam = "normal.bam",
#                              build = "hg19")

## ----eval=FALSE---------------------------------------------------------------
# library(ASCAT)
# ascat.bc = maftools::prepAscat(t_counts = "tumor_nucleotide_counts.tsv",
#                                n_counts = "normal_nucleotide_counts.tsv",
#                                sample_name = "tumor")
# 
# # Library sizes:
# # Tumor:  1830168947
# # Normal: 1321201848
# # Library size difference: 1.385
# # ------
# # Counts file: tumor_nucleotide_counts.tsv
# # Markers: 932148
# # Removed 2982 duplicated loci
# # Markers > 15: 928607
# # ------
# # Counts file: normal_nucleotide_counts.tsv
# # Markers: 932148
# # Removed 2982 duplicated loci
# # Markers > 15: 928311
# # ------
# # Final number SNPs: 928107
# # Generated following files:
# # tumor_nucleotide_counts.tumour.BAF.txt
# # tumor_nucleotide_counts.tumour.logR.txt
# # tumor_nucleotide_counts.normal.BAF.txt
# # tumor_nucleotide_counts.normal.logR.txt
# # ------

## ----eval=FALSE---------------------------------------------------------------
# 
# ascat.bc = ASCAT::ascat.loadData(
#   Tumor_LogR_file = "tumor_nucleotide_counts.tumour.logR.txt",
#   Tumor_BAF_file = "tumor_nucleotide_counts.tumour.BAF.txt",
#   Germline_LogR_file = "tumor_nucleotide_counts.normal.logR.txt",
#   Germline_BAF_file = "tumor_nucleotide_counts.normal.BAF.txt",
#   chrs = c(1:22, "X", "Y"),
#   sexchromosomes = c("X", "Y")
# )
# 
# ASCAT::ascat.plotRawData(ASCATobj = ascat.bc, img.prefix = "tumor")
# ascat.bc = ASCAT::ascat.aspcf(ascat.bc)
# ASCAT::ascat.plotSegmentedData(ascat.bc)
# ascat.output = ASCAT::ascat.runAscat(ascat.bc)

## ----eval=FALSE---------------------------------------------------------------
# ascat.bc = maftools::prepAscat_t(t_counts = "tumor_nucleotide_counts.tsv", sample_name = "tumor_only")
# 
# # Library sizes:
# # Tumor: 1830168947
# # Counts file: tumor_nucleotide_counts.tsv
# # Markers: 932148
# # Removed 2982 duplicated loci
# # Markers > 15: 928607
# # Median depth of coverage (autosomes): 76
# # ------
# # Generated following files:
# # tumor_only.tumour.BAF.txt
# # tumor_only.tumour.logR.txt
# # ------

## ----eval=FALSE---------------------------------------------------------------
# ascat.bc = ASCAT::ascat.loadData(
#   Tumor_LogR_file = "tumor_only.tumour.logR.txt",
#   Tumor_BAF_file = "tumor_only.tumour.BAF.txt",
#   chrs = c(1:22, "X", "Y"),
#   sexchromosomes = c("X", "Y")
# )
# 
# ASCAT::ascat.plotRawData(ASCATobj = ascat.bc, img.prefix = "tumor_only")
# ascat.gg = ASCAT::ascat.predictGermlineGenotypes(ascat.bc)
# ascat.bc = ASCAT::ascat.aspcf(ascat.bc, ascat.gg=ascat.gg)
# ASCAT::ascat.plotSegmentedData(ascat.bc)
# ascat.output = ASCAT::ascat.runAscat(ascat.bc)

## ----eval=FALSE---------------------------------------------------------------
# maftools::segmentLogR(tumor_logR = "tumor.tumour.logR.txt", sample_name = "tumor")
# 
# # Analyzing: tumor
# #   current chromosome: 1
# #   current chromosome: 2
# #   current chromosome: 3
# #   current chromosome: 4
# #   current chromosome: 5
# #   current chromosome: 6
# #   current chromosome: 7
# #   current chromosome: 8
# #   current chromosome: 9
# #   current chromosome: 10
# #   current chromosome: 11
# #   current chromosome: 12
# #   current chromosome: 13
# #   current chromosome: 14
# #   current chromosome: 15
# #   current chromosome: 16
# #   current chromosome: 17
# #   current chromosome: 18
# #   current chromosome: 19
# #   current chromosome: 20
# #   current chromosome: 21
# #   current chromosome: 22
# #   current chromosome: MT
# #   current chromosome: X
# #   current chromosome: Y
# # Segments are written to: tumor_only.tumour_cbs.seg
# # Segments are plotted to: tumor_only.tumour_cbs.png

## ----eval=FALSE---------------------------------------------------------------
# plotMosdepth(
#   t_bed = "tumor.regions.bed.gz",
#   n_bed = "normal.regions.bed.gz",
#   segment = TRUE,
#   sample_name = "tumor"
# )
# 
# # Coverage ratio T/N: 1.821
# # Running CBS segmentation:
# # Analyzing: tumor01
# #   current chromosome: 1
# #   current chromosome: 2
# #   current chromosome: 3
# #   current chromosome: 4
# #   current chromosome: 5
# #   current chromosome: 6
# #   current chromosome: 7
# #   current chromosome: 8
# #   current chromosome: 9
# #   current chromosome: 10
# #   current chromosome: 11
# #   current chromosome: 12
# #   current chromosome: 13
# #   current chromosome: 14
# #   current chromosome: 15
# #   current chromosome: 16
# #   current chromosome: 17
# #   current chromosome: 18
# #   current chromosome: 19
# #   current chromosome: 20
# #   current chromosome: 21
# #   current chromosome: 22
# #   current chromosome: X
# #   current chromosome: Y
# # Segments are written to: tumor01_cbs.seg
# # Plotting

## ----eval=FALSE---------------------------------------------------------------
# plotMosdepth_t(bed = "tumor.regions.bed.gz")

## -----------------------------------------------------------------------------
sessionInfo()