Selection Models

Last updated: 2018-09-02

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Load libraries and data

knitr::opts_chunk$set(echo = TRUE)
library(ggplot2)
library(viridis)
library(ggrepel)
library(neutralitytestr)
library(cowplot)
library(plyr)
dir.create("figures/selection", showWarnings = FALSE, recursive = TRUE)

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

filteredAF = read.table("data/exome-point-mutations/high-vs-low-exomes.v62.ft.filt_lenient-alldonors.txt.gz", 
                        header = TRUE, stringsAsFactors = FALSE)

mut_list = data.frame("sampleID" = filteredAF$donor_short_id,
                      "af_fibro" = filteredAF$nALT_fibro/(filteredAF$nREF_fibro + filteredAF$nALT_fibro),
                      "af_ips" = filteredAF$nALT_ips/(filteredAF$nREF_ips + filteredAF$nALT_ips),
                      "chr" = filteredAF$chrom,
                      "pos" = filteredAF$pos,
                      "ref" = filteredAF$ref,
                      "mut" = filteredAF$alt,
                      "mutID" = paste(filteredAF$chrom, filteredAF$pos, filteredAF$ref, filteredAF$alt, sep = "_"))

mut_list = mut_list[order(mut_list$sampleID),]
write.table(mut_list, "data/selection/ips-fibro-AF.tsv", 
            row.names = FALSE, quote = FALSE, sep = "\t")

mut_list = data.frame("sampleID" = filteredAF$donor_short_id,
                      "af" = filteredAF$nALT_fibro/(filteredAF$nREF_fibro + filteredAF$nALT_fibro),
                      "chr" = filteredAF$chrom,
                      "pos" = filteredAF$pos,
                      "ref" = filteredAF$ref,
                      "mut" = filteredAF$alt)
mut_list = mut_list[order(mut_list$sampleID),]

write.table(mut_list, "data/selection/full-AF.tsv", row.names = FALSE, 
            quote = FALSE, sep = "\t")

dir.create("data/selection/AF", showWarnings = FALSE)

for (sampleID in unique(mut_list$sampleID)) {
  sub_mut_list = mut_list[mut_list$sampleID == sampleID,]
  sub_mut_list = sub_mut_list[sub_mut_list$af >= 0.03,]
  write.table(sub_mut_list, paste0("data/selection/AF/AF-", sampleID, ".tsv"),
              row.names = FALSE, quote = FALSE, sep = "\t")
}

Fit selection models

Please open the Mathematica notebook (code/selection/fit-dist.nb) and run it by hand (the outputs generated are used in subsequent cells). The notebook fits the negative binomial model for neutral evolution.

In case you do not have access to Mathematica to run the notebook, we provide its output files in data/selection/neg-bin-params-fit.csv and data/selection/neg-bin-rsquared-fit.csv.

The code below runs the neutralitytestr model.

fmin = 0.05
fmax = 0.45

petrAF = read.table("data/selection/full-AF.tsv", sep = "\t", header = T)
donors = unique(as.vector(petrAF$sampleID))

getSampleNtrtest <- function(afDF, sampleID, fmin, fmax){
  # run neutralitytestr on a single sample
  VAFsample = afDF[afDF$sampleID == sampleID, "af"]
  out = neutralitytest(VAFsample, fmin = fmin, fmax = fmax)
  results = c(sampleID,
              out$area$metric, out$area$pval,
              out$Dk$metric, out$Dk$pval,
              out$meanDist$metric, out$meanDist$pval,
              out$rsq$metric, out$rsq$pval,
              out$mutation.rate)
  names(results) = c("sampleID",
                     "area", "pval_area",
                     "Dk", "pval_Dk",
                     "meanDist", "pval_meanDist",
                     "rsq", "pval_rsq",
                     "mutrate")
  return(results)
}

ntrtestrPetrout = t(sapply(donors, 
                           function(sampleID) getSampleNtrtest(
                             petrAF, sampleID, fmin, fmax)))
write.table(ntrtestrPetrout, "data/selection/neutralitytestr.tsv", 
            sep = "\t", quote = FALSE, row.names = FALSE)

Plot selection classification

Plot the selection classification based on the goodness of fit results from neutrality testr and the negative binomial like fit.

ntrtestrPetr = read.table("data/selection/neutralitytestr.tsv", 
                          stringsAsFactors = FALSE, header = TRUE)

negbinfitPetr = read.table("data/selection/neg-bin-rsquared-fit.csv",
                           stringsAsFactors = FALSE, header = TRUE, sep = ",")
negbinfitPetr$sampleID = negbinfitPetr$fname
negbinfitPetr$sampleID = gsub("AF-", "", negbinfitPetr$sampleID)
negbinfitPetr$sampleID = gsub(".tsv", "", negbinfitPetr$sampleID)
rownames(negbinfitPetr) = negbinfitPetr$sampleID

dfrsq = data.frame(sampleID = ntrtestrPetr$sampleID,
                   rsq_ntrtestr = ntrtestrPetr$rsq,
                   rsq_negbinfit = negbinfitPetr[ntrtestrPetr$sampleID, "rsq"])

cutoff_selection_cummut = 0.85
cutoff_selection_negbin = 0.25
cutoff_neutral_cummut = 0.9
cutoff_neutral_negbin = 0.55


donors = c("euts", "fawm", "feec", "fikt", "garx", "gesg", "heja", "hipn", "ieki",
           "joxm", "kuco", "laey", "lexy", "naju", "nusw", "oaaz", "oilg", "pipw",
           "puie", "qayj", "qolg", "qonc", "rozh", "sehl", "ualf", "vass", "vils",
           "vuna", "wahn", "wetu", "xugn", "zoxy")

dfrsq = dfrsq[(dfrsq$sampleID %in% donors),]

dfrsq$candidatelabel = NA
dfrsq$candidatelabel[dfrsq$sampleID == "puie"] = "puie"

filter_selection = (dfrsq$rsq_ntrtestr < cutoff_selection_cummut) & (dfrsq$rsq_negbinfit < cutoff_selection_negbin)
filter_neutral = (dfrsq$rsq_ntrtestr > cutoff_neutral_cummut) & (dfrsq$rsq_negbinfit > cutoff_neutral_negbin)

dfrsq$selection = "undetermined"
dfrsq$selection[filter_selection] = "selected"
dfrsq$selection[filter_neutral] = "neutral"


plt_scatter = ggplot(dfrsq, aes(x = rsq_negbinfit, y = rsq_ntrtestr)) +
  scale_colour_manual(values = c("neutral" = "#007536", "selected" = "#5EF288",
                               "undetermined" = "#CCCCCC")) +
  geom_point(aes(colour = selection)) +
  geom_label_repel(aes(label = candidatelabel), color = "white",
                   size = 2.5,
                   fill = "black", box.padding = 0.35, point.padding = 0.5,
                   segment.color = 'grey50') +
  theme_bw() +
  theme(text = element_text(size = 9), axis.text = element_text(size = 8),
        axis.title = element_text(size = 9), 
        plot.title = element_text(size = 9, hjust = 0.5)) +
  labs(x = "Goodness of Fit - Negative Binomial Distribution", 
       y = "Goodness of Fit - Cumulative Mutations") +
  theme(strip.background = element_blank()) +
  labs(title = "") +
  theme(legend.justification = c(1,0), legend.position = c(1,0)) +
  theme(legend.background = element_rect(fill = "transparent", 
                                         colour = "transparent"), 
        legend.key.size = unit(0.25, "cm")) +
  labs(colour = "Selection")

plt_scatter

Expand here to see past versions of plot-prev-1.png:

Version	Author	Date
7888ad3	davismcc	2018-08-26

Visualise negative binomial model

Results from the negative binomial model for neutral evolution.

dfAFpetr = read.table("data/selection/full-AF.tsv", sep = "\t", 
                      stringsAsFactors = FALSE, header = TRUE)
dfAFpetr = dfAFpetr[dfAFpetr$sampleID %in% donors,]

fitparamspetr = read.csv("data/selection/neg-bin-params-fit.csv", 
                         stringsAsFactors = FALSE, header = TRUE)
fitparamspetr$sampleID = gsub("AF-", "", fitparamspetr$fname)
fitparamspetr$sampleID = gsub(".tsv", "", fitparamspetr$sampleID)

a = 1
b = 1
fun.1 <- function(x, a=1, b=1) 1/(a*x)*exp(-x/b)
args = list(mean = 2, sd = .5)

dd <- data.frame(
  predicted = rnorm(72, mean = 2, sd = 2),
  state = rep(c("A", "B", "C"), each = 24)
)

# save generate plotting points
grid = seq(0.01, 0.6, length = 500)

normaldens <- ddply(fitparamspetr, "sampleID", function(df) {
  data.frame(
    predicted = grid,
    density = fun.1(grid, df$a, df$b)
  )
})

normaldens = normaldens[normaldens$sampleID %in% donors,]

dfAFpetr$id = sapply(dfAFpetr$sampleID, 
                     function(sampleID) paste0(
                       sampleID, " (rsq ",
                       round(fitparamspetr$rsq[fitparamspetr$sampleID ==
                                                 sampleID],2),")"))
normaldens$id = sapply(normaldens$sampleID, 
                       function(sampleID) dfAFpetr$id[dfAFpetr$sampleID ==
                                                        sampleID][1])

plt_hist = ggplot(dfAFpetr, aes(x = af)) +
  geom_vline(xintercept = fmin, colour = "grey") +
  geom_vline(xintercept = fmax, colour = "grey") +
  geom_histogram(binwidth = (fmax - fmin)/40) +
  geom_line(aes(x = predicted, y = density), data = normaldens, colour = "red") +
  facet_wrap(~ id, scales = "free_y", ncol = 4) +
  theme_bw() +
  theme(text = element_text(size = 9), axis.text = element_text(size = 8), axis.title = element_text(size = 9), plot.title = element_text(size = 9, hjust = 0.5)) +
  labs(x = paste0("Allele Frequency"), y = "# Mutations") +
  theme(legend.position = "none") +
  labs(colour = "") +
  theme(strip.background = element_blank()) +
  labs(title = "")


pname = paste0("selection-neg-bin-fit")
ppath = paste0("figures/selection/", pname, ".pdf")
ggsave(ppath, plot = plt_hist, width = 17, height = 20, units  =  "cm")
ppath  =  paste0("figures/selection/", pname, ".png")
ggsave(ppath, plot = plt_hist, width = 17, height = 20, dpi = 300, units = "cm", limitsize = FALSE)
plt_hist

Expand here to see past versions of plot-neg-bin-fit-1.png:

Version	Author	Date
7888ad3	davismcc	2018-08-26

Visualise results from neutralitytestr

Results from the cumulative mutations model for neutral evolution.

dfntrtestr = read.table("data/selection/neutralitytestr.tsv", 
                        stringsAsFactors = FALSE, header = TRUE)

plotSampleCumMut <- function(afDF, dfntrtestr, sampleID, fmin, fmax) {
  # plot cummulative mutations as per Sottoriva & Graham
  afDFsample = afDF[afDF$sampleID == sampleID, ]
  rsq = dfntrtestr$rsq[dfntrtestr$sampleID == sampleID]
  # cumsum with decreasing frequency
  afDFsample = afDFsample[order(afDFsample$af, decreasing = TRUE), ]
  afDFsample$cumsum = 1:length(afDFsample$af)
  afDFsample$inverse_af = 1/afDFsample$af
  plt_cummut = ggplot(afDFsample, aes(x = inverse_af, y = cumsum)) +
    geom_vline(xintercept = c(1/fmin, 1/fmax), colour = "darkgrey") +
    geom_point(size = 0.5) +
    geom_line(data = subset(afDFsample, 
                            (inverse_af < 1/fmin) & (inverse_af > 1/fmax)),
              stat = "smooth", method = 'lm', formula = y ~ x, se = FALSE, 
              colour = "red", alpha = 0.5, size = 0.8) +
    coord_cartesian(xlim = c(0, 1/0.01)) +
    theme_bw() +
    theme(text = element_text(size = 9), axis.text = element_text(size = 8),
          axis.title = element_text(size = 9), 
          plot.title = element_text(size = 9, hjust = 0.5)) +
    labs(x = paste0("Inverse AF"), y = "# Cum Mut") +
    theme(legend.position = "none") +
    # remove unnecessary facet
    theme(strip.background = element_blank()) +
    labs(title = paste0(sampleID, " (rsq ", round(rsq,2), ")"))
  return(plt_cummut)
}


pltsCumMut = cowplot::plot_grid(
  plotSampleCumMut(dfAFpetr, dfntrtestr, donors[1], fmin, fmax),
  plotSampleCumMut(dfAFpetr, dfntrtestr, donors[2], fmin, fmax),
  plotSampleCumMut(dfAFpetr, dfntrtestr, donors[3], fmin, fmax),
  plotSampleCumMut(dfAFpetr, dfntrtestr, donors[4], fmin, fmax),
  plotSampleCumMut(dfAFpetr, dfntrtestr, donors[5], fmin, fmax),
  plotSampleCumMut(dfAFpetr, dfntrtestr, donors[6], fmin, fmax),
  plotSampleCumMut(dfAFpetr, dfntrtestr, donors[7], fmin, fmax),
  plotSampleCumMut(dfAFpetr, dfntrtestr, donors[8], fmin, fmax),
  plotSampleCumMut(dfAFpetr, dfntrtestr, donors[9], fmin, fmax),
  plotSampleCumMut(dfAFpetr, dfntrtestr, donors[10], fmin, fmax),
  plotSampleCumMut(dfAFpetr, dfntrtestr, donors[11], fmin, fmax),
  plotSampleCumMut(dfAFpetr, dfntrtestr, donors[12], fmin, fmax),
  plotSampleCumMut(dfAFpetr, dfntrtestr, donors[13], fmin, fmax),
  plotSampleCumMut(dfAFpetr, dfntrtestr, donors[14], fmin, fmax),
  plotSampleCumMut(dfAFpetr, dfntrtestr, donors[15], fmin, fmax),
  plotSampleCumMut(dfAFpetr, dfntrtestr, donors[16], fmin, fmax),
  plotSampleCumMut(dfAFpetr, dfntrtestr, donors[17], fmin, fmax),
  plotSampleCumMut(dfAFpetr, dfntrtestr, donors[18], fmin, fmax),
  plotSampleCumMut(dfAFpetr, dfntrtestr, donors[19], fmin, fmax),
  plotSampleCumMut(dfAFpetr, dfntrtestr, donors[20], fmin, fmax),
  plotSampleCumMut(dfAFpetr, dfntrtestr, donors[21], fmin, fmax),
  plotSampleCumMut(dfAFpetr, dfntrtestr, donors[22], fmin, fmax),
  plotSampleCumMut(dfAFpetr, dfntrtestr, donors[23], fmin, fmax),
  plotSampleCumMut(dfAFpetr, dfntrtestr, donors[24], fmin, fmax),
  plotSampleCumMut(dfAFpetr, dfntrtestr, donors[25], fmin, fmax),
  plotSampleCumMut(dfAFpetr, dfntrtestr, donors[26], fmin, fmax),
  plotSampleCumMut(dfAFpetr, dfntrtestr, donors[27], fmin, fmax),
  plotSampleCumMut(dfAFpetr, dfntrtestr, donors[28], fmin, fmax),
  plotSampleCumMut(dfAFpetr, dfntrtestr, donors[29], fmin, fmax),
  plotSampleCumMut(dfAFpetr, dfntrtestr, donors[30], fmin, fmax),
  plotSampleCumMut(dfAFpetr, dfntrtestr, donors[31], fmin, fmax),
  plotSampleCumMut(dfAFpetr, dfntrtestr, donors[32], fmin, fmax),
  ncol = 4)

pname = paste0("selection-neutralitytestr")
ppath = paste0("figures/selection/", pname, ".pdf")
ggsave(ppath, plot = pltsCumMut, width = 17, height = 25, units = "cm")
ppath = paste0("figures/selection/", pname, ".png")
ggsave(ppath, plot = pltsCumMut, width = 17, height = 25, dpi = 300, 
       units = "cm", limitsize = FALSE)
pltsCumMut

Expand here to see past versions of plot-neutralitytestr-1.png:

Version	Author	Date
7888ad3	davismcc	2018-08-26

Session information

devtools::session_info()

Session info -------------------------------------------------------------

 setting  value                       
 version  R version 3.5.1 (2018-07-02)
 system   x86_64, darwin15.6.0        
 ui       X11                         
 language (EN)                        
 collate  en_GB.UTF-8                 
 tz       Europe/London               
 date     2018-09-02

Packages -----------------------------------------------------------------

 package         * version date       source        
 assertthat        0.2.0   2017-04-11 CRAN (R 3.5.0)
 backports         1.1.2   2017-12-13 CRAN (R 3.5.0)
 base            * 3.5.1   2018-07-05 local         
 bindr             0.1.1   2018-03-13 CRAN (R 3.5.0)
 bindrcpp        * 0.2.2   2018-03-29 CRAN (R 3.5.0)
 colorspace        1.3-2   2016-12-14 CRAN (R 3.5.0)
 compiler          3.5.1   2018-07-05 local         
 cowplot         * 0.9.3   2018-07-15 CRAN (R 3.5.0)
 crayon            1.3.4   2017-09-16 CRAN (R 3.5.0)
 datasets        * 3.5.1   2018-07-05 local         
 devtools          1.13.6  2018-06-27 CRAN (R 3.5.0)
 digest            0.6.16  2018-08-22 CRAN (R 3.5.0)
 dplyr             0.7.6   2018-06-29 CRAN (R 3.5.1)
 evaluate          0.11    2018-07-17 CRAN (R 3.5.0)
 ggplot2         * 3.0.0   2018-07-03 CRAN (R 3.5.0)
 ggrepel         * 0.8.0   2018-05-09 CRAN (R 3.5.0)
 git2r             0.23.0  2018-07-17 CRAN (R 3.5.0)
 glue              1.3.0   2018-07-17 CRAN (R 3.5.0)
 graphics        * 3.5.1   2018-07-05 local         
 grDevices       * 3.5.1   2018-07-05 local         
 grid              3.5.1   2018-07-05 local         
 gridExtra         2.3     2017-09-09 CRAN (R 3.5.0)
 gtable            0.2.0   2016-02-26 CRAN (R 3.5.0)
 htmltools         0.3.6   2017-04-28 CRAN (R 3.5.0)
 knitr             1.20    2018-02-20 CRAN (R 3.5.0)
 labeling          0.3     2014-08-23 CRAN (R 3.5.0)
 lazyeval          0.2.1   2017-10-29 CRAN (R 3.5.0)
 magrittr          1.5     2014-11-22 CRAN (R 3.5.0)
 memoise           1.1.0   2017-04-21 CRAN (R 3.5.0)
 methods         * 3.5.1   2018-07-05 local         
 munsell           0.5.0   2018-06-12 CRAN (R 3.5.0)
 neutralitytestr * 0.0.2   2018-05-21 CRAN (R 3.5.0)
 pillar            1.3.0   2018-07-14 CRAN (R 3.5.0)
 pkgconfig         2.0.2   2018-08-16 CRAN (R 3.5.0)
 plyr            * 1.8.4   2016-06-08 CRAN (R 3.5.0)
 pracma            2.1.5   2018-08-25 CRAN (R 3.5.1)
 purrr             0.2.5   2018-05-29 CRAN (R 3.5.0)
 R.methodsS3       1.7.1   2016-02-16 CRAN (R 3.5.0)
 R.oo              1.22.0  2018-04-22 CRAN (R 3.5.0)
 R.utils           2.7.0   2018-08-27 CRAN (R 3.5.0)
 R6                2.2.2   2017-06-17 CRAN (R 3.5.0)
 Rcpp              0.12.18 2018-07-23 CRAN (R 3.5.0)
 rlang             0.2.2   2018-08-16 CRAN (R 3.5.0)
 rmarkdown         1.10    2018-06-11 CRAN (R 3.5.0)
 rprojroot         1.3-2   2018-01-03 CRAN (R 3.5.0)
 scales            1.0.0   2018-08-09 CRAN (R 3.5.0)
 stats           * 3.5.1   2018-07-05 local         
 stringi           1.2.4   2018-07-20 CRAN (R 3.5.0)
 stringr           1.3.1   2018-05-10 CRAN (R 3.5.0)
 tibble            1.4.2   2018-01-22 CRAN (R 3.5.0)
 tidyselect        0.2.4   2018-02-26 CRAN (R 3.5.0)
 tools             3.5.1   2018-07-05 local         
 utils           * 3.5.1   2018-07-05 local         
 viridis         * 0.5.1   2018-03-29 CRAN (R 3.5.0)
 viridisLite     * 0.3.0   2018-02-01 CRAN (R 3.5.0)
 whisker           0.3-2   2013-04-28 CRAN (R 3.5.0)
 withr             2.1.2   2018-03-15 CRAN (R 3.5.0)
 workflowr         1.1.1   2018-07-06 CRAN (R 3.5.0)
 yaml              2.2.0   2018-07-25 CRAN (R 3.5.1)

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