## ---- 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()