Plot Calibration Curves — plot_calibrationcurves • midar

This function plots calibration curves of each feature where defined and displays QC samples with defined concentrations within the plot. Users can select a regression model (linear or quadratic) and apply weighting (none, "1/x", or "1/x^2"), either through function arguments or feature metadata.

Usage

plot_calibrationcurves(
  data = NULL,
  variable = "norm_intensity",
  qc_types = NA,
  overwrite_fit_param = FALSE,
  fit_model = c("linear", "quadratic"),
  fit_weighting = c(NA, "none", "1/x", "1/x^2"),
  show_confidence_interval = NA,
  log_axes = FALSE,
  filter_data = FALSE,
  include_qualifier = TRUE,
  include_istd = FALSE,
  include_feature_filter = NA,
  exclude_feature_filter = NA,
  output_pdf = FALSE,
  path = NA,
  return_plots = FALSE,
  point_size = 1.5,
  line_width = 0.7,
  point_color = NA,
  point_fill = NA,
  point_shape = NA,
  line_color = "#4575b4",
  ribbon_fill = "#91bfdb40",
  font_base_size = 7,
  rows_page = 4,
  cols_page = 5,
  specific_page = NA,
  page_orientation = "LANDSCAPE",
  show_progress = TRUE
)

Arguments

data: A MidarExperiment object containing the dataset.
variable: Variable to plot on the y-axis, usually intensity. Default is "intensity".
qc_types: A character vector specifying the QC types to plot. It must contain at least CAL, which represents calibration curve samples. Other QC types will be plotted as points when they have assigned concentrations (see QC-concentration metadata). These QC types need to be present in the data and defined in the analysis metadata. The default is NA, which means any of the QC types "CAL", "HQC", "MQC", "LQC", "EQA", "QC", will be plotted if present and have assigned concentrations.
overwrite_fit_param: If TRUE, the function will ignore any fit method and weighting settings defined in the metadata and use the provided fit_model and fit_weighting values for all analytes.
fit_model: A character string specifying the default regression fit method to use for the calibration curve. Must be one of "linear" or "quadratic". This method will be applied if no specific fit method is defined for a feature in the metadata, or when overwrite_fit_param = TRUE.
fit_weighting: A character string specifying the default weighting method for the regression points in the calibration curve. Must be one of "none", "1/x", or "1/x^2". This method will be applied if no specific weighting method is defined for a feature in the metadata, or when overwrite_fit_param = TRUE.
show_confidence_interval: Logical, if TRUE, displays the confidence interval as ribbon. Default is NA, in which case confidence intervals are plotted in a linear scale and ommitted in log-log scale.
log_axes: Logical. Determines whether the x and y axes are displayed in a logarithmic scale (log-log scale). Set to TRUE to enable logarithmic scaling; otherwise, set to FALSE for a linear scale. Note: If TRUE, any regression curves or standard error regions with negative values will be omitted from display.
filter_data: Logical, if TRUE, uses QC filtered data; otherwise uses raw data. Default is FALSE.
include_qualifier: Logical, whether to include qualifier features. Default is TRUE.
include_istd: Logical, whether to include internal standard (ISTD) features. Default is TRUE.
include_feature_filter: Regex pattern to include features. If omitted, considers all features.
exclude_feature_filter: Regex pattern to exclude features. If omitted, excludes none.
output_pdf: Logical, if TRUE, saves plots as a PDF file. Default is FALSE.
path: File path for saving the PDF. Default is an empty string.
return_plots: Logical, if TRUE, returns plots as a list of ggplot2 objects. Default is FALSE.
point_size: Size of points in the plot. Default is 1.5.
line_width: Width of regression lines. Default is 0.7.
point_color: A vector specifying the colors for points corresponding to different QC types. This can be either an unnamed vector or a named vector, with names corresponding to QC types. Unused colors will be ignored. Default is NA which corresponds to the default colors for QC types defined in the package.
point_fill: A vector specifying the fill colors for points corresponding to different QC types. This can be either an unnamed vector or a named vector, with names corresponding to QC types. Unused fill colors will be ignored. Default is NA which corresponds to the default fill colors for QC types defined in the package.
point_shape: A vector specifying the shapes for points corresponding to different QC types. This can be either an unnamed vector or a named vector, with names corresponding to QC types. Unused shapes will be ignored. Default is NA which corresponds to the default shapes for QC types defined in the package.
line_color: Color of the regression line. Default is "#4575b4".
ribbon_fill: Color for the confidence interval ribbon. Default is "#91bfdb40".
font_base_size: Base font size for text in plots. Default is 7.
rows_page: Number of plot rows. Default is 4.
cols_page: Number of plot columns. Default is 5.
specific_page: Show/save a specific page number only. NA plots/saves all pages.
page_orientation: Orientation of PDF, either "LANDSCAPE" or "PORTRAIT". Default is `"LANDSCAPE
show_progress: Logical. If TRUE, displays a progress bar during plot creation.

Details

Features for plotting can be filtered using QC filters defined via filter_features_qc() or through include_feature_filter and exclude_feature_filter arguments. The resulting plots offer extensive customization options, including point size, line width, point color, point fill, point shape, line color, ribbon fill, and font base size.

Plots will be divided into multiple pages if the number of features exceeds the product of rows_page and cols_page settings. The function supports both direct plotting within R and saving plots as PDF files. Additionally, plots can be returned as a list of ggplot2 objects for further manipulation or integration into other analyses.