Adjastment of UMI collisions on inDrop

Data loading

holder <- readRDS(paste0(kDataPath, 'SCG71.rds'))

umi_distribution <- GetUmisDistribution(holder$reads_per_umi_per_cell)
umi_probs <- umi_distribution / sum(umi_distribution)
max_umi_per_gene <- 4^nchar(names(umi_probs)[1])

Distribution of UMIs:

PlotUmisDistribution(holder$reads_per_umi_per_cell, bins=70)

Modeling of collisions

total_umi_nums <- seq(0, 25000, 20)
n_reps <- 10
uniq_umi_samples <- sapply(1:10, function(i) 
  sapply(total_umi_nums, function(n) SampleNames(umi_probs, n) %>% unique() %>% length()))

collisions_info <- FillCollisionsAdjustmentInfo(umi_probs, max_umi_per_gene)

umis_df <- tibble(Unique=as.vector(uniq_umi_samples), 
                  Total=rep(total_umi_nums, n_reps))

fig_width <- 3.5
fig_height <- 4

collisions_df <- tibble(Unique=1:max_umi_per_gene, Empirical=collisions_info) %>%
  mutate(Uniform=sapply(Unique, AdjustGeneExpressionUniform, umis_number=max_umi_per_gene))

gg_collisions <- ggplot(collisions_df, aes(x=Unique, y=Total - Unique)) + 
  geom_point_rast(aes(color='Sampling'), data=umis_df, size=1, alpha=0.1, 
                  width=fig_width, height=fig_height) +
  geom_vline(aes(xintercept=max_umi_per_gene), linetype='dashed') +
  geom_line(aes(y=Empirical - Unique, color='Empirical'), size=1) +
  geom_line(aes(y=Uniform - Unique, color='Uniform'), size=1) +
  scale_color_hue(l=55) +
  scale_y_continuous(expand=c(0, 0), name='#Underestimated UMIs', limits=c(0, 7500)) +
  xlab('#Observed UMIs') +
  scale_color_manual(values=c(Empirical='#e02323', Uniform='#194aea', Sampling='black'), 
                     name='Estimation type') +
  theme_pdf(legend.pos=c(0, 1)) +
  guides(color=guide_legend(override.aes=list(size=c(1.0, 1.5, 1.0), shape=c(NA, 16, NA), 
                                              linetype=c(1,0,1), alpha=1)))

gg_collisions

rm(holder, umi_distribution, umi_probs, collisions_info, umis_df, collisions_df)
invisible(gc())

Aberrant mononucleotide stretches

kDataPath <- '../../data/dropest/10x/aml035_post_transplant/est_10_20_umi_quality/'
reads_per_umi <- readRDS(paste0(kDataPath, 'reads_per_umi_per_cell.rds'))

umi_distribution <- GetUmisDistribution(reads_per_umi, smooth=0)
umi_distribution <- umi_distribution[umi_distribution > 0]

tail_threshold <- quantile(umi_distribution, 0.995)
umi_distribution_tail <- umi_distribution[umi_distribution > tail_threshold]

rm(reads_per_umi)
invisible(gc())

prefix_lengths <- seq(2, 10, 2)
distrs_by_length <- mclapply(prefix_lengths, MaxFreqDistribution, umi_distribution_tail, 
                             mc.cores=5)
plot_df <- lapply(1:length(prefix_lengths), function(i) 
  cbind(melt(distrs_by_length[[i]], id.vars = 'fracs'), PrefixLength=prefix_lengths[i])) %>% 
  bind_rows()

gl <- guide_legend(title='Distribution')
gg_stretches <- ggplot(plot_df, aes(x = fracs, y = as.factor(PrefixLength), height = value,
                                    fill=variable, linetype=variable)) + 
  geom_density_ridges(stat = "identity", scale = 0.95, alpha=0.7) +
  scale_x_continuous(expand = c(0.005, 0.005), limits=c(0.2, 1.0)) +
  scale_y_discrete(expand = c(0.02, 0.02)) +
  labs(x='Fraction of the most\nfrequent nucleotide', y='UMI prefix length') +
  theme_pdf(legend.pos=c(0.01, 0)) +
  guides(fill=gl, linetype=gl) + 
  scale_fill_npg()

gg_stretches

Complete figure

gg_fig <- cowplot::plot_grid(gg_stretches, gg_collisions, 
                             labels="AUTO", align="h")

gg_fig

ggsave(paste0(kPlotsFolder, 'supp_umi_collision_modeling.pdf'), gg_fig, 
       width=8, height=4)

Session information