Diagnostic plots from demultiplexing

Diagnostic plots for determining the effect of the barcode cutoff. freq_plot() shows a histogram or distribution plot of the number of reads for each barcode combination, whereas knee_plot() shows the cumulative fraction of reads ranked by the frequency of the barcode combinations in descending order.

Usage

freq_plot(
  freq_table,
  cutoff = NULL,
  type = "histogram",
  log_scale_x = TRUE,
  log_scale_y = FALSE,
  scale_by_reads = FALSE
)

knee_plot(freq_table, cutoff = NULL)

Arguments

freq_table: The frequency table from create_freq_table().
cutoff: Optional scalar numeric, the x-coordinate for drawing a vertical dashed line in the plots in order to indicate the cutoff. Please note that this argument is interpreted literally, meaning that in order to correctly display the same cutoff on both type of plots, the cutoff value has to be transformed. In order to safely convert between the two types of cutoffs, use the functions bc_to_freq_cutoff() and freq_to_bc_cutoff().
type: The type of frequency plot to make, either 'histogram' or 'density'.
log_scale_x: Logical: Should a log scale be applied to the x-axis of the frequency plot?
log_scale_y: Logical: Should a log scale be applied to the y-axis of the frequency plot?
scale_by_reads: Logical: Should the y-axis of the plot be scaled by the number of reads on the x-axis?

Value

A ggplot object which can be displayed immediately or further modified.

Examples

library(purrr)
library(Biostrings)
input_fastq <- system.file(
    "extdata", "PETRI-seq_forward_reads.fq.gz", package = "posDemux")
reads <- readDNAStringSet(input_fastq, format = "fastq")
barcode_files <- system.file(
    "extdata/PETRI-seq_barcodes", 
    c(bc1 = "bc1.fa", bc2 = "bc2.fa", bc3 = "bc3.fa"),
    package = "posDemux"
    )
names(barcode_files) <- paste0("bc", 1L:3L)
barcode_index <- map(barcode_files, readDNAStringSet)
barcodes <- barcode_index[c("bc3", "bc2", "bc1")]
sequence_annotation <- c(UMI = "P", "B", "A", "B", "A", "B", "A")
segment_lengths <- c(7L, 7L, 15L, 7L, 14L, 7L, NA_integer_)
demultiplex_res <- posDemux::combinatorial_demultiplex(
    reads, barcodes = barcodes, segments = sequence_annotation,
    segment_lengths = segment_lengths)
filtered_res <- filter_demultiplex_res(demultiplex_res, allowed_mismatches = 1L)
freq_table <- create_freq_table(filtered_res$demultiplex_res$assigned_barcodes)

bc_cutoff <- 500L
# Notice the bend (knee) of the curve
knee_plot(freq_table, cutoff = bc_cutoff)


# Note that we must convert the cutoff when constructing the frequency plot
freq_cutoff <- bc_to_freq_cutoff(freq_table, bc_cutoff)

# This is the most basic type of frequency plot which can be made,
# but it is a bit hard to interpret whether a selected cutoff is sensible
freq_plot(
    freq_table, cutoff = freq_cutoff, type = "histogram",
    log_scale_x = FALSE, log_scale_y = FALSE, scale_by_reads = FALSE)
#> `stat_bin()` using `bins = 30`. Pick better value `binwidth`.


# For most practical purposes, this is the most informative version
freq_plot(freq_table, cutoff = freq_cutoff, type = "density",
    log_scale_x = TRUE, log_scale_y = FALSE,
    scale_by_reads = TRUE)

Usage

Arguments

Value

See also

Examples