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This document provides overview information for 32 healthy human fibroblast cell lines used in this project. Note that each cell line was each derived from a distinct donor, so we use the terms “line” and “donor” interchangeably.

Load libraries and data

library(readr)
library(dplyr)
library(scran)
library(scater)
library(viridis)
library(ggplot2)
library(ggforce)
library(ggridges)
library(SingleCellExperiment)
library(edgeR)
library(limma)
library(org.Hs.eg.db)
library(cowplot)
library(gplots)
library(ggrepel)
library(sigfit)
library(Rcpp)
library(deconstructSigs)
options(stringsAsFactors = FALSE)

Load donor level information.

donor_info <- as.data.frame(read_csv("data/donor_info_core.csv"))
# merge age bins
donor_info$age_decade <- ""
for (i in 1:nrow(donor_info)) {
  if (donor_info$age[i] %in% c("30-34", "35-39")) 
    donor_info$age_decade[i] <- "30-39"
  if (donor_info$age[i] %in% c("40-44", "45-49")) 
    donor_info$age_decade[i] <- "40-49"
  if (donor_info$age[i] %in% c("50-54", "55-59")) 
    donor_info$age_decade[i] <- "50-59"
  if (donor_info$age[i] %in% c("60-64", "65-69")) 
    donor_info$age_decade[i] <- "60-69"
  if (donor_info$age[i] %in% c("70-74", "75-79")) 
    donor_info$age_decade[i] <- "70-79"
}

Load exome variant sites.

exome_sites <- read_tsv("data/exome-point-mutations/high-vs-low-exomes.v62.ft.filt_lenient-alldonors.txt.gz",
                        col_types = "ciccdcciiiiccccccccddcdcll", comment = "#",
                        col_names = TRUE)
exome_sites <- dplyr::mutate(
  exome_sites,
  chrom = paste0("chr", gsub("chr", "", chrom)),
  var_id = paste0(chrom, ":", pos, "_", ref, "_", alt),
  chr_pos = paste0(chrom, "_", pos))

exome_sites <- as.data.frame(exome_sites)
## deduplicate sites list
exome_sites <- exome_sites[!duplicated(exome_sites[["var_id"]]),]
## calculate coverage at sites for each donor
donor_vars_coverage <- list()
for (i in unique(exome_sites$donor_short_id)) {
  exome_sites_subset <- exome_sites[exome_sites$donor_short_id == i, ]
  donor_vars_coverage[[i]] <- exome_sites_subset$nREF_fibro + exome_sites_subset$nALT_fibro
}

Load VEP annotations and show table with number of variants assigned to each functional annotation category.

vep_best <- read_tsv("data/exome-point-mutations/high-vs-low-exomes.v62.ft.alldonors-filt_lenient.all_filt_sites.vep_most_severe_csq.txt")
colnames(vep_best)[1] <- "Uploaded_variation"
## deduplicate dataframe
vep_best <- as.data.frame(vep_best[!duplicated(vep_best[["Uploaded_variation"]]),])
as.data.frame(table(vep_best[["Consequence"]]))
                                 Var1 Freq
1                 3_prime_UTR_variant  181
2                 5_prime_UTR_variant  121
3             downstream_gene_variant   13
4                  intergenic_variant   13
5                      intron_variant 1539
6                mature_miRNA_variant    3
7                    missense_variant 3648
8  non_coding_transcript_exon_variant  428
9           regulatory_region_variant    1
10            splice_acceptor_variant   41
11               splice_donor_variant   24
12              splice_region_variant  291
13                         start_lost    9
14                        stop_gained  227
15                          stop_lost    2
16              stop_retained_variant    5
17                 synonymous_variant 1923
18              upstream_gene_variant   34

Add consequences to exome sites.

vep_best[["var_id"]] <- paste0("chr", vep_best[["Uploaded_variation"]])
exome_sites <- inner_join(exome_sites, 
                          vep_best[, c("var_id", "Location", "Consequence")], 
                          by = "var_id")

Add donor level mutation information (aggregate across impacts) Not used in manuscript, but still calculated to store in donor_info table.

impactful_csq <- c("stop_lost", "start_lost", "stop_gained",
                   "splice_donor_variant", "splice_acceptor_variant",
                   "splice_region_variant", "missense_variant")
donor_info$num_mutations <- NA
donor_info$num_synonymous <- NA
donor_info$num_missense <- NA
donor_info$num_splice_region <- NA
donor_info$num_splice_acceptor <- NA
donor_info$num_splice_donor <- NA
donor_info$num_stop_gained <- NA
donor_info$num_start_lost <- NA
donor_info$num_stop_lost <- NA
for (i in unique(donor_info$donor_short)) {
  if (i %in% unique(exome_sites$donor_short_id)) {
    exome_sites_subset <- exome_sites[exome_sites$donor_short_id == i, ]
    donor_info$num_mutations[donor_info$donor_short == i] <- length(exome_sites_subset$Consequence)
    donor_info$num_synonymous[donor_info$donor_short == i] <- sum(exome_sites_subset$Consequence == "synonymous_variant")
    donor_info$num_missense[donor_info$donor_short == i] <- sum(exome_sites_subset$Consequence == "missense_variant")
    donor_info$num_splice_region[donor_info$donor_short == i] <- sum(exome_sites_subset$Consequence == "splice_region_variant")
    donor_info$num_splice_acceptor[donor_info$donor_short == i] <- sum(exome_sites_subset$Consequence == "splice_acceptor_variant")
    donor_info$num_splice_donor[donor_info$donor_short == i] <- sum(exome_sites_subset$Consequence == "splice_donor_variant")
    donor_info$num_stop_gained[donor_info$donor_short == i] <- sum(exome_sites_subset$Consequence == "stop_gained")
    donor_info$num_start_lost[donor_info$donor_short == i] <- sum(exome_sites_subset$Consequence == "start_lost")
    donor_info$num_stop_lost[donor_info$donor_short == i] <- sum(exome_sites_subset$Consequence == "stop_lost")
  }
}

Goodness of fit of neutral evolution models

Add donor level mutation selection info (Daniel Kunz). We produce a scatter plot of goodness of fit for each line for the cumulative mutations (Williams et al, 2016) and the negative binomial (Simons, 2016) models of neutral evolution. We label the example line joxm, which is analysed in more depth in other scripts.

ntrtestrPetr <- read.table("data/neutralitytestr-petr.tsv", 
                           stringsAsFactors = FALSE, header = TRUE)
negbinfitPetr = read.table("data/neg-bin-rsquared-petr.csv", 
                           stringsAsFactors = FALSE, header = TRUE, sep = ",")
negbinfitPetr$sampleID <- negbinfitPetr$fname
negbinfitPetr$sampleID <- gsub("petr-AF-", "", negbinfitPetr$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

dfrsq$candidatelabel <- NA
dfrsq$candidatelabel[dfrsq$sampleID == "joxm"] <- "joxm"

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"

colnames(dfrsq)[1] <- "donor_short"
donor_info <- merge(donor_info, dfrsq, by = "donor_short")
donor_info$selection_colour <- "#CCCCCC"
for (i in 1:nrow(donor_info)) {
  if (donor_info$selection[i] == "neutral") 
    donor_info$selection_colour[i] <- "dodgerblue"
  if (donor_info$selection[i] == "selected") 
    donor_info$selection_colour[i] <- "dodgerblue4"
}

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_filt <- dfrsq[(dfrsq$donor_short %in% donors),]

plt_scatter <- ggplot(dfrsq_filt, aes(x = rsq_negbinfit, y = rsq_ntrtestr)) +
  scale_fill_manual(values = c("neutral" = "dodgerblue",
                                 "selected" = "dodgerblue4",
                                 "undetermined" = "#CCCCCC")) +
  geom_point(aes(fill = selection), size = 3, shape = 21) +
  geom_label_repel(aes(label = candidatelabel), color = "black", size = 4,
                   fill = "gray90", box.padding = 0.35, point.padding = 0.5,
                   segment.color = "grey50") +
  # theme_bw() +
  # theme(text = element_text(size = 11, face = "plain"), 
  #       axis.text = element_text(size = 11, face = "plain"),
  #       axis.title = element_text(size = 12, face = "plain"),
  #       plot.title = element_text(size = 11, hjust = 0.5)) +
  labs(x = "Goodness of fit - negative binomial distribution", 
       y = "Goodness of fit - cumulative mutations") +
  theme_cowplot(font_size = 16) +
  theme(strip.background = element_blank()) +
  theme(legend.justification = c(1,0), legend.position = c(1,0)) +
  theme(legend.background = element_rect(fill = "white", linetype = 1, 
                                         colour = "black", size = 0.2),
        legend.key.size = unit(0.25, "cm")) +
  theme(panel.grid.major = element_line(colour = "gray90", size = 0.25)) +
  labs(fill = "Selection") #+
# coord_fixed()

ggsave("figures/overview_lines/neutral_selection_models_gof_scatter.png", 
       plot = plt_scatter, width = 12, height = 12, dpi = 300, units = "cm")

ggsave("figures/overview_lines/neutral_selection_models_gof_scatter_wide.png", 
       plot = plt_scatter, width = 6.5, height = 4.5, dpi = 300, units = "in")


plt_scatter

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Mutational signatures

Add donor level mutation signature exposures, using the sigfit package and 30 COSMIC signatures. We load the filtered exome variant sites and calculate the tri-nucleotide context for each variant (required for computing signature exposures), using a function from the deconstructSigs package.

data("cosmic_signatures", package = "sigfit")

##new input 
mutation_list <- read.table("data/exome-point-mutations/high-vs-low-exomes.v62.ft.filt_lenient-alldonors.txt.gz", header = TRUE)
mutation_list$chr_pos <- paste0("chr", mutation_list$chr, "_", mutation_list$pos)

mutation_donors <- unique(mutation_list$donor_short_id)
mutation_list_donors <- list()
for (i in mutation_donors) {
  cat("....reading ", i, "\n")
  mutation_list_donors[[i]] <- mutation_list[which(mutation_list$donor_short_id == i),]
  mutation_list_donors[[i]]$chr <- paste0("chr", mutation_list_donors[[i]]$chrom)
  mutation_list_donors[[i]]$chr_pos = paste0(mutation_list_donors[[i]]$chr, "_", mutation_list_donors[[i]]$pos)
}

## Calculate triNucleotide contexts for mutations using deconstructSigs command
mut_triNs <- list()
for (i in mutation_donors) {
  cat("....processing ", i, "\n")
  mut_triNs[[i]] <- mut.to.sigs.input(mutation_list_donors[[i]], sample.id = "donor_short_id", 
                                      chr = "chr", pos = "pos", ref = "ref", alt = "alt")
}

## Convert to correct format
sig_triNs <- character()
for (j in 1:96) {
  c1 <- substr(colnames(mut_triNs[[1]])[j], 1,1)
  ref <- substr(colnames(mut_triNs[[1]])[j], 3,3)
  alt <- substr(colnames(mut_triNs[[1]])[j], 5,5)
  c3 <- substr(colnames(mut_triNs[[1]])[j], 7,7)
  triN_sigfit <- paste0(c1,ref,c3,">",c1,alt,c3)
  sig_triNs[j] <- triN_sigfit
}

for (i in mutation_donors) {
  colnames(mut_triNs[[i]]) <- sig_triNs
}

## Fit signatures using sigfit
mcmc_samples_fit <- list()
set.seed(1234)
for (i in mutation_donors) {
  mcmc_samples_fit[[i]] <- sigfit::fit_signatures(
    counts = mut_triNs[[i]], signatures = cosmic_signatures,
    iter = 2000, warmup = 1000, chains = 1, seed = 1)
}

## Estimate exposures using sigfit
exposures <- list()
for (i in mutation_donors) {
  exposures[[i]] <- sigfit::retrieve_pars(
    mcmc_samples_fit[[i]], par = "exposures", hpd_prob = 0.90)
}

Plot an exposure barchart for each line.

## Plot exposure bar charts
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")

signature_names <- c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", 
                     "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", 
                     "22", "23", "24", "25", "26", "27", "28", "29", "30")

for (j in donors) {
  cat("....plotting ", j, "\n")
   sigfit::plot_exposures(mcmc_samples_fit[[j]], 
                          signature_names = signature_names)
   png(paste0("figures/overview_lines/mutational_signatures/exposure_barchart_", 
             j, ".png"),
      units = "in", width = 12, height = 10, res = 500)
  sigfit::plot_exposures(mcmc_samples_fit[[j]], 
                         signature_names = signature_names)
  dev.off()
 
}
....plotting  euts 

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....plotting  fawm 

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....plotting  feec 

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....plotting  fikt 

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....plotting  garx 

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....plotting  gesg 

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....plotting  heja 

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....plotting  hipn 

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....plotting  ieki 

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....plotting  joxm 

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....plotting  kuco 

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....plotting  laey 

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....plotting  lexy 

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....plotting  naju 

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....plotting  nusw 

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....plotting  oaaz 

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....plotting  oilg 

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....plotting  pipw 

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....plotting  puie 

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....plotting  qayj 

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....plotting  qolg 

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....plotting  qonc 

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....plotting  rozh 

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....plotting  sehl 

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....plotting  ualf 

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....plotting  vass 

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....plotting  vils 

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....plotting  vuna 

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....plotting  wahn 

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....plotting  wetu 

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....plotting  xugn 

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....plotting  zoxy 

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Retrieve exposures for a given signal. Specifically, we will look an highest posterior density (HPD) intervals and mean exposures for Signature 7 (UV) and Signature 11, the only two that are significant across multiple lines. We add this information to the donor_info dataframe.

get_signature_df <- function(exposures, samples, signature) {
  signature_mat_mean <- matrix(NA, nrow = length(samples), 30)
  for (i in 1:length(samples)) {
    signature_mat_mean[i,] <- as.numeric(exposures[[i]]$mean)
  }
  rownames(signature_mat_mean) <- samples
  colnames(signature_mat_mean) <- colnames(exposures[[i]]$mean)
  signature_mat_lower90 <- matrix(NA, nrow = length(samples), 30)
  for (i in 1:length(samples)) {
    signature_mat_lower90[i,] <- as.numeric(exposures[[i]]$lower_90)
  }
  rownames(signature_mat_lower90) <- samples
  colnames(signature_mat_lower90) <- colnames(exposures[[i]]$lower_90)
  signature_mat_upper90 <- matrix(NA, nrow = length(samples), 30)
  for (i in 1:length(samples)) {
    signature_mat_upper90[i,] <- as.numeric(exposures[[i]]$upper_90)
  }
  rownames(signature_mat_upper90) <- samples
  colnames(signature_mat_upper90) <- colnames(exposures[[i]]$upper_90)
  signature_df <- cbind(
    as.data.frame(signature_mat_mean[,signature]),
    as.data.frame(signature_mat_lower90[,signature]),
    as.data.frame(signature_mat_upper90[,signature]))
  colnames(signature_df) <- c(paste0("Sig",signature,"_mean"),
                              paste0("Sig",signature,"_lower"),
                              paste0("Sig",signature,"_upper"))
  signature_df$donor <- rownames(signature_df)
  signature_df
}

## Get lower, mean and upper values for signatures 7 & 11
sig7_df <- get_signature_df(exposures, mutation_donors, 7)
sig11_df <- get_signature_df(exposures, mutation_donors, 11)

## Add Sigs 7/11 to donor info table
sig_subset_df <- merge(sig7_df,sig11_df, by = "donor")
sig_subset_df_means <- sig_subset_df[,c(1,grep("mean", colnames(sig_subset_df)))]
sig_subset_df_means_melt <- reshape2::melt(sig_subset_df_means)
sig_subset_df_means_melt$signature <- substr(sig_subset_df_means_melt$variable, 1, 5)
sig_subset_df_means_melt <- sig_subset_df_means_melt[,c("donor", "signature", "value")]

colnames(sig_subset_df)[1] <- "donor_short" 
donor_info <- merge(donor_info, sig_subset_df, by = "donor_short")

Expression data and cell assignments

Read in annotated SingleCellExperiment (SCE) objects and create a list of SCE objects containing all cells used for analysis and their assignment (using cardelino) to clones identified with Canopy from whole-exome sequencing data.

params <- list()
params$callset <- "filt_lenient.cell_coverage_sites"
fls <- list.files("data/sces")
fls <- fls[grepl(params$callset, fls)]
donors <- gsub(".*ce_([a-z]+)_.*", "\\1", fls)
sce_unst_list <- list()
for (don in donors) {
    sce_unst_list[[don]] <- readRDS(file.path("data/sces", 
        paste0("sce_", don, "_with_clone_assignments.", params$callset, ".rds")))
    cat(paste("reading", don, ":   ", ncol(sce_unst_list[[don]]), "cells.\n"))
}
reading euts :    79 cells.
reading fawm :    53 cells.
reading feec :    75 cells.
reading fikt :    39 cells.
reading garx :    70 cells.
reading gesg :    105 cells.
reading heja :    50 cells.
reading hipn :    62 cells.
reading ieki :    58 cells.
reading joxm :    79 cells.
reading kuco :    48 cells.
reading laey :    55 cells.
reading lexy :    63 cells.
reading naju :    44 cells.
reading nusw :    60 cells.
reading oaaz :    38 cells.
reading oilg :    90 cells.
reading pipw :    107 cells.
reading puie :    41 cells.
reading qayj :    97 cells.
reading qolg :    36 cells.
reading qonc :    58 cells.
reading rozh :    91 cells.
reading sehl :    30 cells.
reading ualf :    89 cells.
reading vass :    37 cells.
reading vils :    37 cells.
reading vuna :    71 cells.
reading wahn :    82 cells.
reading wetu :    77 cells.
reading xugn :    35 cells.
reading zoxy :    88 cells.
## Calculate single cell data metrics
sc_metrics_summary <- list()
sc_metrics_summary_df <- data.frame()
for (don in donors) {
  sc_metrics_summary[[don]]$num_unst_cells <- ncol(sce_unst_list[[don]])
  sc_metrics_summary[[don]]$num_assignable <- sum(sce_unst_list[[don]]$assignable)
  sc_metrics_summary[[don]]$num_unassignable <- 
    (sc_metrics_summary[[don]]$num_unst_cells -
       sc_metrics_summary[[don]]$num_assignable)
  sc_metrics_summary[[don]]$num_clones_with_cells <-
    length(unique(sce_unst_list[[don]]$assigned[
      which(sce_unst_list[[don]]$assigned != "unassigned")]))
  sc_metrics_summary[[don]]$donor <- don
  sc_metrics_summary_df <-
    rbind(sc_metrics_summary_df, as.data.frame(sc_metrics_summary[[don]]))
}

colnames(sc_metrics_summary_df) <- c("total_unst_cells", "assigned_unst_cells",
                                     "unassigned_unst_cells", 
                                     "num_clones_with_cells", "donor_short")

## Merge with donor info table
donor_info <- merge(donor_info, sc_metrics_summary_df, by = "donor_short", 
                    all.x = TRUE)
donor_info$percent_assigned_cells <-
  donor_info$assigned_unst_cells / donor_info$total_unst_cells

Canopy clone inference information

First, we read in the canopy output for each line we analyse.

canopy_files <- list.files("data/canopy")
canopy_files <- canopy_files[grepl(params$callset, canopy_files)]
canopy_list <- list()
for (don in donors) {
  canopy_list[[don]] <- readRDS(
    file.path("data/canopy", 
              paste0("canopy_results.", don, ".", params$callset, ".rds")))
}

Second, summarise the number of mutations for each clone, for each line. Form these results into a dataframe.

clone_mut_list <- list()
for (don in donors) {
  cat(paste("summarising clones and mutations for", don, '\n'))
  clone_mut_list[[don]] <- colSums(canopy_list[[don]]$tree$Z)
  cat(colSums(canopy_list[[don]]$tree$Z))
  cat('\n')
}
summarising clones and mutations for euts 
0 39 29
summarising clones and mutations for fawm 
0 17 16
summarising clones and mutations for feec 
0 19 9 6
summarising clones and mutations for fikt 
0 13 23
summarising clones and mutations for garx 
0 79 57
summarising clones and mutations for gesg 
0 37 23
summarising clones and mutations for heja 
0 16 20
summarising clones and mutations for hipn 
0 8 8
summarising clones and mutations for ieki 
0 7 10
summarising clones and mutations for joxm 
0 41 98
summarising clones and mutations for kuco 
0 9
summarising clones and mutations for laey 
0 45 49
summarising clones and mutations for lexy 
0 9 6
summarising clones and mutations for naju 
0 13
summarising clones and mutations for nusw 
0 3 13
summarising clones and mutations for oaaz 
0 17 19
summarising clones and mutations for oilg 
0 2 37
summarising clones and mutations for pipw 
0 34 36
summarising clones and mutations for puie 
0 13 15
summarising clones and mutations for qayj 
0 11 7
summarising clones and mutations for qolg 
0 23
summarising clones and mutations for qonc 
0 17 7
summarising clones and mutations for rozh 
0 11 10 12
summarising clones and mutations for sehl 
0 2 11 23
summarising clones and mutations for ualf 
0 29 39
summarising clones and mutations for vass 
0 98 35
summarising clones and mutations for vils 
0 1 2 8
summarising clones and mutations for vuna 
0 33
summarising clones and mutations for wahn 
0 52 114
summarising clones and mutations for wetu 
0 8 17
summarising clones and mutations for xugn 
0 16 12
summarising clones and mutations for zoxy 
0 14 8
clone_mut_df <- data.frame(clone1 = numeric(), clone2 = numeric(), 
                           clone3 = numeric(), clone4 = numeric())
for (i in 1:length(donors)) {
  num_clones <- length(clone_mut_list[[i]])
  num_NAs <- 4 - num_clones
  temp_row  <- c(clone_mut_list[[i]], rep(NA, num_NAs))
  clone_mut_df[i,] <- temp_row
}
rownames(clone_mut_df) <- donors
clone_mut_df$donor_short <- donors

Next, summarise the number of unique mutations tagging each clone identified by Canopy (that is, the number of variants for each clone that distinguish it from other clones in the line). Produce a dataframe with this information as well.

clone_mut_unique_list <- list()
for (don in donors) {
        cat(paste("summarising clones and mutations for", don, '\n'))
        clone_mut_unique_list[[don]] <-
          colSums(canopy_list[[don]]$tree$Z[
            (rowSums(canopy_list[[don]]$tree$Z) == 1),])
        cat(colSums(canopy_list[[don]]$tree$Z[
          (rowSums(canopy_list[[don]]$tree$Z) == 1),]))
        cat('\n')
}
summarising clones and mutations for euts 
0 20 10
summarising clones and mutations for fawm 
0 3 2
summarising clones and mutations for feec 
0 16 4 1
summarising clones and mutations for fikt 
0 3 13
summarising clones and mutations for garx 
0 41 19
summarising clones and mutations for gesg 
0 20 6
summarising clones and mutations for heja 
0 8 12
summarising clones and mutations for hipn 
0 6 6
summarising clones and mutations for ieki 
0 6 9
summarising clones and mutations for joxm 
0 14 71
summarising clones and mutations for kuco 
0 9
summarising clones and mutations for laey 
0 16 20
summarising clones and mutations for lexy 
0 5 2
summarising clones and mutations for naju 
0 13
summarising clones and mutations for nusw 
0 0 10
summarising clones and mutations for oaaz 
0 8 10
summarising clones and mutations for oilg 
0 2 37
summarising clones and mutations for pipw 
0 17 19
summarising clones and mutations for puie 
0 4 6
summarising clones and mutations for qayj 
0 7 3
summarising clones and mutations for qolg 
0 23
summarising clones and mutations for qonc 
0 13 3
summarising clones and mutations for rozh 
0 7 0 2
summarising clones and mutations for sehl 
0 0 3 15
summarising clones and mutations for ualf 
0 15 25
summarising clones and mutations for vass 
0 65 2
summarising clones and mutations for vils 
0 0 1 7
summarising clones and mutations for vuna 
0 33
summarising clones and mutations for wahn 
0 1 63
summarising clones and mutations for wetu 
0 2 11
summarising clones and mutations for xugn 
0 6 2
summarising clones and mutations for zoxy 
0 7 1
clone_mut_unique_df <- data.frame(clone1 = numeric(), clone2 = numeric(), 
                                  clone3 = numeric(),
                           clone4 = numeric(), min_unique_muts = numeric())
for (i in 1:length(donors)) {
  num_clones <- length(clone_mut_unique_list[[i]])
  num_NAs <- 4 - num_clones
  temp_row  <- c(clone_mut_unique_list[[i]], rep(NA, num_NAs), 
                 min(clone_mut_unique_list[[i]][2:num_clones]))
  clone_mut_unique_df[i,] <- temp_row
}
rownames(clone_mut_df) <- donors
clone_mut_df$donor_short <- donors
rownames(clone_mut_unique_df) <- donors
clone_mut_unique_df$donor_short <- donors
donor_info <- merge(donor_info, clone_mut_unique_df, by = "donor_short", all.x = T)

Finally, calculate the minimum Hamming distance between pairs of clones for each line. In general, assignment of cells to clones will be easier/more successful for lines with larger numbers of variants distinguishes between clones (that is, a high minimum Hamming distance).

We add all of this information to the donor_info dataframe and then have the data prepared to make some overview plots across lines.

## Calculate Hamming distance
clone_mut_list_hamming <- list()
for (don in donors) {
  Config <- canopy_list[[don]]$tree$Z
  unique_sites_paired <- c()
  for (i in seq_len(ncol(Config) - 1)) {
    for (j in seq(i + 1, ncol(Config))) {
      n_sites <- sum(rowSums(Config[, c(i,j)]) == 1)
      unique_sites_paired <- c(unique_sites_paired, n_sites)
    }
  }
  clone_mut_list_hamming[[don]] <- unique_sites_paired  
  cat("....hamming distances for ", don, ": ", unique_sites_paired, "\n") 
}
....hamming distances for  euts :  39 29 30 
....hamming distances for  fawm :  17 16 5 
....hamming distances for  feec :  19 9 6 22 19 5 
....hamming distances for  fikt :  13 23 16 
....hamming distances for  garx :  79 57 60 
....hamming distances for  gesg :  37 23 26 
....hamming distances for  heja :  16 20 20 
....hamming distances for  hipn :  8 8 12 
....hamming distances for  ieki :  7 10 15 
....hamming distances for  joxm :  41 98 85 
....hamming distances for  kuco :  9 
....hamming distances for  laey :  45 49 36 
....hamming distances for  lexy :  9 6 7 
....hamming distances for  naju :  13 
....hamming distances for  nusw :  3 13 10 
....hamming distances for  oaaz :  17 19 18 
....hamming distances for  oilg :  2 37 39 
....hamming distances for  pipw :  34 36 36 
....hamming distances for  puie :  13 15 10 
....hamming distances for  qayj :  11 7 10 
....hamming distances for  qolg :  23 
....hamming distances for  qonc :  17 7 16 
....hamming distances for  rozh :  11 10 12 13 15 2 
....hamming distances for  sehl :  2 11 23 9 21 18 
....hamming distances for  ualf :  29 39 40 
....hamming distances for  vass :  98 35 67 
....hamming distances for  vils :  1 2 8 1 7 8 
....hamming distances for  vuna :  33 
....hamming distances for  wahn :  52 114 64 
....hamming distances for  wetu :  8 17 13 
....hamming distances for  xugn :  16 12 8 
....hamming distances for  zoxy :  14 8 8 
min_hamming_distance <- data.frame("donor_short" = donors,
                                   "min_hamming_dist" = 0)
for (i in 1:length(donors)) {
  min_hamming_distance$min_hamming_dist[i] <- min(clone_mut_list_hamming[[i]])
}

donor_info <- merge(donor_info, min_hamming_distance, by = "donor_short", 
                    all.x = TRUE)

## Number of clones
donor_info$num_clones_total <- 0
for (i in donors) {
  num_clones <- length(clone_mut_unique_list[[i]])
  donor_info$num_clones_total[which(donor_info$donor_short == i)] <- num_clones
}

Plot line metrics

We make a large combined plot stitching together individual plots showing:

  • donor age;
  • number of somatic mutations;
  • signature 7 (UV) exposure; and
  • number of mutations per clone

for each line.

donor_info_filt <- donor_info[which(donor_info$donor_short %in% donors), ]
donor_filt_order <- dplyr::arrange(donor_info_filt, desc(num_mutations))
donor_filt_order <- donor_filt_order$donor_short
rownames(donor_info_filt) <- donor_info_filt$donor_short

# Plot age
selected_vars <- c("donor_short", "age_decade", "selection_colour")
donor_info_filt_melt <- reshape2::melt(donor_info_filt[,selected_vars],
                                       "donor_short")
donor_info_filt_melt <- donor_info_filt[,selected_vars]

age_plot_filt <- ggplot(
  donor_info_filt_melt, 
  aes(x = "Age", y = factor(donor_short, levels = rev(donor_filt_order)), 
      fill = age_decade)) + 
  geom_tile() +
  labs(x = "", y = "Donor", fill = "") + 
  scale_fill_manual(values = rev(c(magma(8)[-c(1,8)])))  +
  guides(fill = guide_legend(nrow = 3,byrow = TRUE)) +
  theme(axis.text.x = element_text(size = 32, face = "plain"), 
        axis.line = element_blank(), 
        legend.position = "top", legend.direction = "horizontal", 
        legend.text = element_text(size = 28, face = "plain"), 
        legend.key.size = unit(0.3,"in"),
        axis.ticks.x = element_blank(),
        legend.margin = margin(unit(0.01, "cm")),
        axis.text.y = element_text(size = 30, face = "plain"), 
        axis.title.y = element_text(size = 36, face = "plain"), 
        axis.title.x = element_text(size = 36, face = "plain"))

# Plot number of mutations
selected_vars <- c("donor_short", "num_mutations")
donor_info_filt_melt <- reshape2::melt(donor_info_filt[,selected_vars],
                                       "donor_short")
num_mut_plot_filt <- ggplot(
  donor_info_filt_melt, 
  aes(x = value, y = factor(donor_short, levels = rev(donor_filt_order)))) + 
  geom_point(size = 4, alpha = 0.7) + 
  scale_shape_manual(values = c(4, 19)) + 
  scale_x_log10(breaks = c(50, 100, 500)) +
  theme_bw(16) + 
  ggtitle(" ") + 
  labs(x = "Number of mutations", y = "") + 
  theme(axis.text.y = element_blank(), 
        axis.ticks.y = element_blank(), 
        axis.line = element_blank(), 
        legend.position = "top", 
        axis.text.x = element_text(size = 32, face = "plain"), 
        title = element_text(colour="black",size = 30, face = "plain"), 
        axis.title.x = element_text(size = 36, face = "plain"))

# Plot signature 7 
donor_info_filt_melt <- reshape2::melt(
  donor_info_filt[,c(1,grep("Sig7_mean",colnames(donor_info_filt)))])
donor_info_filt_melt$signature <- substr(donor_info_filt_melt$variable, 1, 5)
donor_info_filt_melt <- 
  donor_info_filt_melt[, c("donor_short", "signature", "value")]
sig_decomp_plot_filt <- ggplot(
  donor_info_filt_melt, 
  aes(y = value, x = factor(donor_short, levels = rev(donor_filt_order)), 
      fill = factor(signature))) + 
  geom_bar(stat = "identity") + 
  coord_flip() + 
  scale_fill_manual(values = rev(c(magma(10)[-c(1,10)]))) +
  theme_bw(16) + 
  ggtitle(" ") + 
  labs(x = "",y = "Signature 7 exposure", fill = "") +
  theme(axis.text.y = element_blank(), 
        axis.ticks.y = element_blank(), 
        axis.line = element_blank(), 
        axis.text.x = element_text(size = 32, face = "plain"),
        title = element_text(colour = "black", size = 30, face = "plain"),
        axis.title.x = element_text(size = 36, face = "plain")) +
  guides(fill = FALSE)

# Plot total number of mutations per clone
donor_info_filt_subset <-
  donor_info_filt[,c(1,grep("clone",colnames(donor_info_filt)))]
# Remove columns that do not relate to number of mutations per clone: 
donor_info_filt_subset <- donor_info_filt_subset[,c(-2,-7)]
donor_info_filt_melt <- reshape2::melt(donor_info_filt_subset, "donor_short")
donor_info_filt_melt$variable <- substr(donor_info_filt_melt$variable, 6, 6)
num_clones_plot_filt <- ggplot(
  donor_info_filt_melt, 
  aes(variable, factor(donor_short, levels = rev(donor_filt_order)))) +
  geom_tile(aes(fill = value), colour = "white") + 
  scale_fill_distiller(palette = "BuPu", values = c(0,0.05,0.1,0.15,0.25,0.5,1),
                       na.value = "white", breaks = c(0,25,50,75), 
                       direction = 1) +
  labs(x = "Clone", y = "", fill = "Number of mutations per clone") +  
  theme(axis.ticks.y = element_blank(), 
        axis.line = element_blank(), 
        legend.position = "top",
        legend.key.size = unit(0.5,"in"), 
        axis.title.x = element_text(size = 36, face = "plain"),
        axis.text.x = element_text(size = 32, face = "plain"),
        axis.text.y = element_blank(), 
        legend.justification = "center", 
        legend.text = element_text(size = 30, face = "plain"), 
        legend.title = element_text(size = 30, face = "plain"))

## Combine above plots into Fig 2a
fig_2a <- cowplot::plot_grid(age_plot_filt, num_mut_plot_filt,
                             sig_decomp_plot_filt, num_clones_plot_filt, 
                             nrow = 1, rel_widths = c(3, 4, 4, 6), align = "h",
                             axis = "t", scale =  c(1, 0.988, 0.988, 0.988))
ggsave("figures/overview_lines/overview_lines_BuPu.png", 
       plot = fig_2a, width = 30, height = 14)

fig_2a

Expand here to see past versions of donor-info-summary-plot-1.png:
Version Author Date
f0ed980 davismcc 2018-08-31
090c1b9 davismcc 2018-08-24

Plot cell assignment rate vs Hamming Distance

Finally, we plot the cell assignment rate (from cardelino) against the minimum Hamming distance (minimum number of variants distinguishing a pair of clones) for each line.

fig_2c_no_size <- ggplot(
  donor_info_filt, 
  aes(y = as.numeric(percent_assigned_cells), x = as.numeric(min_hamming_dist), 
      fill = total_unst_cells)) + 
  geom_point(pch = 21, colour = "gray50", size = 4) +
  scale_shape_manual(values = c(4, 19)) + 
  ylim(c(0, 1)) + 
  scale_x_log10() + 
  scale_fill_viridis(option = "magma") +
  ylab("Proportion cells assigned") + 
  xlab("Minimum number of variants distinguishing clones") + 
  labs(title = "") +
  labs(fill = "Number of cells") + 
  theme_bw() +
  theme(text = element_text(size = 9,face = "bold"), 
        axis.text = element_text(size = 9, face = "bold"),
        axis.title = element_text(size = 10, face = "bold"),
        plot.title = element_text(size = 9, hjust = 0.5)) +
  theme(strip.background = element_blank()) +
  theme(legend.justification = c(1,0), 
        legend.position = c(1,0), 
        legend.direction = "horizontal",
        legend.background = element_rect(fill = "white", linetype = 1, 
                                         colour = "black", size = 0.2),
        legend.key.size = unit(0.25, "cm"))

ggsave("figures/overview_lines/cell_assignment_vs_min_hamming_dist.png",
       plot = fig_2c_no_size, width = 12, height = 12, dpi = 300, units = "cm")
fig_2c_no_size

Expand here to see past versions of plot-cell-assignment-vs-hamming-1.png:
Version Author Date
090c1b9 davismcc 2018-08-24

Save data to file

Save the donor info dataframe to output/line_info.tsv.

write_tsv(donor_info_filt, path = "output/line_info.tsv", col_names = TRUE)

Session information

devtools::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                  

 package                     * version   date      
 AnnotationDbi               * 1.42.1    2018-05-08
 assertthat                    0.2.0     2017-04-11
 backports                     1.1.2     2017-12-13
 base                        * 3.5.1     2018-07-05
 beeswarm                      0.2.3     2016-04-25
 bindr                         0.1.1     2018-03-13
 bindrcpp                    * 0.2.2     2018-03-29
 Biobase                     * 2.40.0    2018-05-01
 BiocGenerics                * 0.26.0    2018-05-01
 BiocParallel                * 1.14.2    2018-07-08
 Biostrings                    2.48.0    2018-05-01
 bit                           1.1-14    2018-05-29
 bit64                         0.9-7     2017-05-08
 bitops                        1.0-6     2013-08-17
 blob                          1.1.1     2018-03-25
 BSgenome                      1.48.0    2018-05-01
 BSgenome.Hsapiens.UCSC.hg19   1.4.0     2018-08-20
 caTools                       1.17.1.1  2018-07-20
 clue                          0.3-55    2018-04-23
 cluster                       2.0.7-1   2018-04-13
 coda                          0.19-1    2016-12-08
 codetools                     0.2-15    2016-10-05
 colorspace                    1.3-2     2016-12-14
 compiler                      3.5.1     2018-07-05
 cowplot                     * 0.9.3     2018-07-15
 crayon                        1.3.4     2017-09-16
 data.table                    1.11.4    2018-05-27
 datasets                    * 3.5.1     2018-07-05
 DBI                           1.0.0     2018-05-02
 deconstructSigs             * 1.8.0     2016-07-29
 DelayedArray                * 0.6.5     2018-08-15
 DelayedMatrixStats            1.2.0     2018-05-01
 devtools                      1.13.6    2018-06-27
 digest                        0.6.16    2018-08-22
 dplyr                       * 0.7.6     2018-06-29
 DT                            0.4       2018-01-30
 dynamicTreeCut                1.63-1    2016-03-11
 edgeR                       * 3.22.3    2018-06-21
 evaluate                      0.11      2018-07-17
 FNN                           1.1.2.1   2018-08-10
 gdata                         2.18.0    2017-06-06
 GenomeInfoDb                * 1.16.0    2018-05-01
 GenomeInfoDbData              1.1.0     2018-04-25
 GenomicAlignments             1.16.0    2018-05-01
 GenomicRanges               * 1.32.6    2018-07-20
 ggbeeswarm                    0.6.0     2017-08-07
 ggforce                     * 0.1.3     2018-07-07
 ggplot2                     * 3.0.0     2018-07-03
 ggrepel                     * 0.8.0     2018-05-09
 ggridges                    * 0.5.0     2018-04-05
 git2r                         0.23.0    2018-07-17
 glue                          1.3.0     2018-07-17
 gplots                      * 3.0.1     2016-03-30
 graphics                    * 3.5.1     2018-07-05
 grDevices                   * 3.5.1     2018-07-05
 grid                          3.5.1     2018-07-05
 gridExtra                     2.3       2017-09-09
 gtable                        0.2.0     2016-02-26
 gtools                        3.8.1     2018-06-26
 hms                           0.4.2     2018-03-10
 htmltools                     0.3.6     2017-04-28
 htmlwidgets                   1.2       2018-04-19
 httpuv                        1.4.5     2018-07-19
 igraph                        1.2.2     2018-07-27
 inline                        0.3.15    2018-05-18
 IRanges                     * 2.14.11   2018-08-24
 KernSmooth                    2.23-15   2015-06-29
 knitr                         1.20      2018-02-20
 labeling                      0.3       2014-08-23
 later                         0.7.3     2018-06-08
 lattice                       0.20-35   2017-03-25
 lazyeval                      0.2.1     2017-10-29
 limma                       * 3.36.3    2018-08-25
 locfit                        1.5-9.1   2013-04-20
 magrittr                      1.5       2014-11-22
 MASS                          7.3-50    2018-04-30
 Matrix                        1.2-14    2018-04-13
 matrixStats                 * 0.54.0    2018-07-23
 memoise                       1.1.0     2017-04-21
 methods                     * 3.5.1     2018-07-05
 mime                          0.5       2016-07-07
 munsell                       0.5.0     2018-06-12
 org.Hs.eg.db                * 3.6.0     2018-05-15
 parallel                    * 3.5.1     2018-07-05
 pillar                        1.3.0     2018-07-14
 pkgconfig                     2.0.2     2018-08-16
 plyr                          1.8.4     2016-06-08
 promises                      1.0.1     2018-04-13
 purrr                         0.2.5     2018-05-29
 R.methodsS3                   1.7.1     2016-02-16
 R.oo                          1.22.0    2018-04-22
 R.utils                       2.7.0     2018-08-27
 R6                            2.2.2     2017-06-17
 RColorBrewer                  1.1-2     2014-12-07
 Rcpp                        * 0.12.18   2018-07-23
 RCurl                         1.95-4.11 2018-07-15
 readr                       * 1.1.1     2017-05-16
 reshape2                      1.4.3     2017-12-11
 rhdf5                         2.24.0    2018-05-01
 Rhdf5lib                      1.2.1     2018-05-17
 rjson                         0.2.20    2018-06-08
 rlang                         0.2.2     2018-08-16
 rmarkdown                     1.10      2018-06-11
 rprojroot                     1.3-2     2018-01-03
 Rsamtools                     1.32.3    2018-08-22
 RSQLite                       2.1.1     2018-05-06
 rstan                         2.17.3    2018-01-20
 rtracklayer                   1.40.5    2018-08-20
 S4Vectors                   * 0.18.3    2018-06-08
 scales                        1.0.0     2018-08-09
 scater                      * 1.9.12    2018-08-03
 scran                       * 1.8.4     2018-08-07
 shiny                         1.1.0     2018-05-17
 sigfit                      * 1.1.0     2018-05-08
 SingleCellExperiment        * 1.2.0     2018-05-01
 StanHeaders                   2.17.2    2018-01-20
 statmod                       1.4.30    2017-06-18
 stats                       * 3.5.1     2018-07-05
 stats4                      * 3.5.1     2018-07-05
 stringi                       1.2.4     2018-07-20
 stringr                       1.3.1     2018-05-10
 SummarizedExperiment        * 1.10.1    2018-05-11
 tibble                        1.4.2     2018-01-22
 tidyselect                    0.2.4     2018-02-26
 tools                         3.5.1     2018-07-05
 tweenr                        0.1.5     2016-10-10
 tximport                      1.8.0     2018-05-01
 units                         0.6-0     2018-06-09
 utils                       * 3.5.1     2018-07-05
 vipor                         0.4.5     2017-03-22
 viridis                     * 0.5.1     2018-03-29
 viridisLite                 * 0.3.0     2018-02-01
 whisker                       0.3-2     2013-04-28
 withr                         2.1.2     2018-03-15
 workflowr                     1.1.1     2018-07-06
 XML                           3.98-1.16 2018-08-19
 xtable                        1.8-3     2018-08-29
 XVector                       0.20.0    2018-05-01
 yaml                          2.2.0     2018-07-25
 zlibbioc                      1.26.0    2018-05-01
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This reproducible R Markdown analysis was created with workflowr 1.1.1