Make plots to visually assess dropout trends — VisualizeDropout • EZbakR

Plots a measure of dropout (the ratio of -label to +label RPM) as a function of feature fraction new, with the model fit depicted. Use this function to qualitatively assess model fit and whether the modeling assumptions are met.

Usage

VisualizeDropout(
  obj,
  plot_type = c("grandR", "bakR"),
  grouping_factors = NULL,
  features = NULL,
  populations = NULL,
  fraction_design = NULL,
  repeatID = NULL,
  exactMatch = TRUE,
  n_min = 50
)

Arguments

obj

An EZbakRFractions object, which is an EZbakRData object on which you have run EstimateFractions().

plot_type

Which type of plot to make. Options are:

bakR: X-axis is fraction new (a.k.a. NTR) and Y-axis is dropout (no label n / label n)
grandR: X-axis is fraction new rank (a.k.a. NTR rank) and Y-axis is log(dropout)

grouping_factors

Which sample-detail columns in the metadf should be used to group -s4U samples by for calculating the average -s4U RPM? The default value of NULL will cause all sample-detail columns to be used.

features

Character vector of the set of features you want to stratify reads by and estimate proportions of each RNA population. The default of NULL will expect there to be only one fractions table in the EZbakRFractions object.

populations

Mutational populations that were analyzed to generate the fractions table to use. For example, this would be "TC" for a standard s4U-based nucleotide recoding experiment.

fraction_design

"Design matrix" specifying which RNA populations exist in your samples. By default, this will be created automatically and will assume that all combinations of the mutrate_populations you have requested to analyze are present in your data. If this is not the case for your data, then you will have to create one manually. See docs for EstimateFractions (run ?EstimateFractions()) for more details.

repeatID

If multiple fractions tables exist with the same metadata, then this is the numerical index by which they are distinguished.

exactMatch

If TRUE, then features must exactly match the features metadata for a given fractions table for it to be used. Means that you cannot specify a subset of features by default. Set this to FALSE if you would like to specify a feature subset.

n_min

Minimum raw number of reads to make it to plot

Value

A list of ggplot2 objects, one for each +label sample.

Examples


# Simulate data to analyze
simdata <- EZSimulate(30)

# Create EZbakR input
ezbdo <- EZbakRData(simdata$cB, simdata$metadf)

# Estimate Fractions
ezbdo <- EstimateFractions(ezbdo)
#> Estimating mutation rates
#> Summarizing data for feature(s) of interest
#> Averaging out the nucleotide counts for improved efficiency
#> Estimating fractions
#> Processing output

# Visualize Dropout
ezbdo <- VisualizeDropout(ezbdo)
#> Making plot for sample sample1...
#> Making plot for sample sample2...
#> Making plot for sample sample3...
#> Making plot for sample sample4...
#> Making plot for sample sample5...
#> Making plot for sample sample6...