Generates a Principal Component Analysis (PCA) plot for visualizing QC samples, with options for filtering, transformations, and customizable visual elements.
Usage
plot_pca(
data = NULL,
variable,
filter_data,
pca_dim = c(1, 2),
qc_types = c("SPL", "BQC", "TQC", "NIST", "LTR"),
label_mad_threshold = 3,
log_transform = TRUE,
exclude_istds = TRUE,
min_median_signal = NA,
point_size = 2,
point_alpha = 0.7,
ellipse_alpha = 0.8,
font_base_size = 8,
hide_text_from_labels = NA
)Arguments
- data
MidarExperiment object containing the QC data
- variable
Variable to use for PCA analysis. Must be one of: "area", "height", "intensity", "response", "conc", "conc_raw", "rt", "fwhm"
- filter_data
Use all (default) or qc-filtered data
- pca_dim
PCA dimensions to plot as a vector of length 2. Default is c(1,2)
- qc_types
QC types to include in the plot. Default is c("SPL", "BQC", "TQC", "NIST", "LTR")
- label_mad_threshold
Show analysis_id label for points outside k * MAD of the selected PCA dimensions. Default is 3. Set NULL to suppress labels
- log_transform
Whether to log-transform data before PCA. Default is TRUE
- exclude_istds
Whether to exclude internal standards. Default is TRUE
- min_median_signal
Minimum median signal across all samples from selected QC types. NA (default) will not filter any features
- point_size
Size of plot points. Default is 2
- point_alpha
Transparency of plot points (0-1). Default is 0.7
- ellipse_alpha
Transparency of ellipse fill (0-1). Default is 0.8
- font_base_size
Base font size for plot text elements. Default is 8
- hide_text_from_labels
Hide shared text (case-sensitive) in labels. If resulting in non-unique analysis_id's, an error will be raised