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Rmd 0590541 davismcc 2018-08-25 Adding selection models analysis from Daniel Kunz

## Load libraries and data

Load the call set and extract the allele frequencies which are used for the fits of the selection models.

## Fit selection models

For the selection analysis SubConalSelection (http://dx.doi.org/10.1038/s41588-018-0128-6) was used. To reproduce the analysis please run the Julia code (code/selection/subclonal-bayesian-ABC.jl).

Since the simulations take in the order of days/weeks we provide the outputfiles of the simulation in data/subclonal-output-1/ and a summary in data/p1-selection.csv.

## Plot selection classification Plot the selection classification from SubConalSelection. The grey background indicates results with high uncertainty due to low numbers of mutations (< 100).

## Plot fit of selection models Plot the fit of the models to the allele frequency distribution.

selection_class = read.csv("data/p1-selection.csv", stringsAsFactors=F)

# histograms
library(ggplot2)

fout = list.files(path = "data/subclonal-output-1", pattern="*histogram-clone1[.]*",
                  full.names=T, recursive=T)
donors_neutral = selection_class[selection_class$selection == "neutral", "donor"]

for (donor in donors_neutral){
  fout[grepl(donor, fout)] = gsub("clone1", "clone0", fout[grepl(donor, fout)])
}


modelstats = read.csv(fout[1])
modelstats$donor = strsplit(basename(fout[1]), "-")[[1]][3]
modelstats$selection_model = gsub(".csv", "" , strsplit(basename(fout[1]), "-")[[1]][5])

for (i in 2:length(fout)){
  modelstats_donor = read.csv(fout[i])
  modelstats_donor$donor = strsplit(basename(fout[i]), "-")[[1]][3]
  modelstats_donor$selection_model = gsub(".csv", "" , strsplit(basename(fout[i]), "-")[[1]][5])
  modelstats = rbind(modelstats, modelstats_donor)
}

modelstats$selection_model = gsub("clone0", "neutral", modelstats$selection_model)
modelstats$selection_model = gsub("clone1", "selected", modelstats$selection_model)

selection_class$donor_class = paste0(selection_class$donor, " (", selection_class$selection, ")")

# add to modelstats
modelstats$donor_class = NA
modelstats$selection = NA

for (donor in selection_class$donor){
  modelstats[modelstats$donor == donor, "donor_class"] = selection_class[selection_class$donor == donor, "donor_class"]
  modelstats[modelstats$donor == donor, "selection"] = selection_class[selection_class$donor == donor, "selection"]
}


fmin = 0.05
fmax = 0.45


# remove model fit for undetermined donors
modelstats[modelstats$selection == "undetermined", c("mean", "lowerq95", "upperq95")] = NA

plt_hist = ggplot(modelstats, aes(x=VAF, y=truecounts)) +
                facet_wrap(~donor_class, ncol=4, scales = "free_y") +
                geom_bar(stat="identity") +
                geom_line(aes(x=VAF, y=mean, colour=selection_model)) +
                geom_ribbon(aes(x=VAF, ymax=upperq95, ymin=lowerq95, fill=selection_model), alpha=0.2) +
                geom_vline(xintercept=c(fmin, fmax), colour="#808080") +
                # scale_x_continuous(limits = c(0, 0.47), breaks=c(0, fmin, 0.1, 0.2, 0.3, 0.4, fmax), expand=c(0,0)) +
                coord_cartesian(xlim = c(0, 0.47), ylim=c(0, max(modelstats$truecounts)), expand=0) +
                scale_colour_manual(values = c(neutral="#1283FF", selected="#144E7B", undetermined="#CACACA"), guide=FALSE) +
                scale_fill_manual(values = c(neutral="#1283FF", selected="#144E7B", undetermined="#CACACA")) +
                scale_x_continuous(breaks=c(0, fmin, 0.1, 0.2, 0.3, 0.4, fmax), labels=c("0", paste0(fmin), "0.1", "0.2", "0.3", "0.4", paste0(fmax))) +
                theme_bw() +
                theme(text=element_text(size=7), axis.text=element_text(size=6), axis.title=element_text(size=7), plot.title=element_text(size=7, hjust=0.5)) +
                labs(x="VAF", y="# Mutations") +
                # remove unnecessary facet
                theme(strip.background = element_blank()) +
                theme(legend.position="bottom") +
                labs(title="") +
                labs(fill="Selection Model")
                # coord_fixed()

ppath = paste0("figures/selection-hist-SubClonalSelection.png")
ggsave(ppath, plot=plt_hist, width=18.3, height=20, dpi=300, units = "cm")
Warning: Removed 100 rows containing missing values (geom_path).
Warning: Removed 100 rows containing missing values (geom_path).
Warning: Removed 100 rows containing missing values (geom_path).


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