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Plot standardized feature intensities grouped by QC type

Source: R/plot_qc_matrixeffects.R
plot_qc_matrixeffects.Rd

This function creates a grouped beeswarm plot of standardized feature intensities, where the y-axis represents intensity standardized such that the mean across all features is 100%. Points are grouped by qc_type and spread using quasirandom jitter.

Usage

plot_qc_matrixeffects(
  data,
  variable = "intensity",
  qc_types = c("SPL", "TQC", "PBLK", "BQC"),
  include_qualifier = FALSE,
  include_istd = TRUE,
  include_feature_filter = NA,
  exclude_feature_filter = NA,
  min_median_value = NA,
  point_size = 0.5,
  dodge_width = 0.6,
  point_alpha = 0.3,
  box_alpha = 0.3,
  box_linewidth = 0.5,
  font_base_size = 8,
  angle_x = 45
)

Arguments

data

A MidarExperiment object

variable

A character string indicating the variable to use for PCA analysis. Must be one of: "area", "height", "intensity", "norm_intensity", "response", "conc", "conc_raw", "rt", "fwhm".

qc_types

A character vector specifying the QC types to plot. It must contain at least one element. The default is NA, which means any of the non-blank QC types ("SPL", "TQC", "BQC", "HQC", "MQC", "LQC", "NIST", "LTR") will be plotted if present in the dataset.

include_qualifier

A logical value indicating whether to include qualifier features. Default is TRUE.

include_istd

A logical value indicating whether to include internal standard (ISTD) features. Default is TRUE.

include_feature_filter

A character or regex pattern used to filter features by feature_id. If NA or an empty string ("") is provided, the filter is ignored. When a vector of length > 1 is supplied, only features with exactly these names are selected (applied individually as OR conditions).

exclude_feature_filter

A character or regex pattern used to exclude features by feature_id. If NA or an empty string ("") is provided, the filter is ignored. When a vector of length > 1 is supplied, only features with exactly these names are excluded (applied individually as OR conditions).

min_median_value

Minimum median feature value (as determined by the variable) across all samples from selected QC types that must be met for a feature to be included in the PCA analysis. NA (default) means no filtering will be applied. This parameter provides an fast way to exclude noisy features from the analysis. However, it is recommended to use filter_data with filter_features_qc().

point_size

A numeric value indicating the size of points in millimeters. Default is 2.

dodge_width

Numeric. Width used to dodge overlapping points by qc_type. Default is 0.6.

point_alpha

Numeric. Transparency of the plotted points. Default is 0.7.

box_alpha

Numeric. Transparency of the boxplot. Default is 0.3.

box_linewidth

Numeric. Width of the boxplot lines. Default is 0.5.

font_base_size

A numeric value indicating the base font size for plot text elements. Default is 8.

angle_x

Numeric. Angle of the x-axis text labels. Default is 45.

Value

A ggplot object showing the grouped standardized beeswarm plot.

Examples

if (FALSE) { # \dontrun{
library(ggbeeswarm)
example_df <- tibble::tibble(
  feature_id = rep(c("F1", "F2", "F3"), each = 10),
  qc_type = rep(c("QC1", "QC2"), each = 5, times = 3),
  intensity = runif(30, 500, 1500)
)
plot_standardized_qc_beeswarm(example_df)
} # }