plot_CV_distr: Plot CV distribution to compare various steps of the analysis
In proBatch: Tools for Diagnostics and Corrections of Batch Effects in Proteomics

Description Usage Arguments Value Examples

Plot CV distribution to compare various steps of the analysis

plot_CV_distr(
  df_long,
  sample_annotation = NULL,
  feature_id_col = "peptide_group_label",
  sample_id_col = "FullRunName",
  measure_col = "Intensity",
  biospecimen_id_col = "EarTag",
  batch_col = NULL,
  unlog = TRUE,
  log_base = 2,
  offset = 1,
  plot_title = NULL,
  filename = NULL,
  theme = "classic"
)

`df_long`	as in `df_long` for the rest of the package, but, when it has entries for intensity, represented in `measure_col` for several steps, e.g. raw, normalized, batch corrected data, as seen in column `Step`, then multi-step CV comparison can be carried out.
`sample_annotation`	data frame with: `sample_id_col` (this can be repeated as row names) biological covariates technical covariates (batches etc) . See `help("example_sample_annotation")`
`feature_id_col`	name of the column with feature/gene/peptide/protein ID used in the long format representation `df_long`. In the wide formatted representation `data_matrix` this corresponds to the row names.
`sample_id_col`	name of the column in `sample_annotation` table, where the filenames (colnames of the `data_matrix` are found).
`measure_col`	if `df_long` is among the parameters, it is the column with expression/abundance/intensity; otherwise, it is used internally for consistency.
`biospecimen_id_col`	column in `sample_annotation` that defines a unique bio ID, which is usually a combination of conditions or groups. Tip: if such ID is absent, but can be defined from several columns, create new `biospecimen_id` column
`batch_col`	column in `sample_annotation` that should be used for batch comparison (or other, non-batch factor to be mapped to color in plots).
`unlog`	(logical) whether to reverse log transformation of the original data
`log_base`	base of the logarithm for transformation
`offset`	small positive number to prevent 0 conversion to `-Inf`
`plot_title`	title of the plot (e.g., processing step + representation level (fragments, transitions, proteins) + purpose (meanplot/corrplot etc))
`filename`	path where the results are saved. If null the object is returned to the active window; otherwise, the object is save into the file. Currently only pdf and png format is supported
`theme`	ggplot theme, by default `classic`. Can be easily overriden

ggplot object with the boxplot of CVs on one or several steps

CV_plot = plot_CV_distr(example_proteome, 
sample_annotation = example_sample_annotation, 
measure_col = 'Intensity', batch_col = 'MS_batch', 
plot_title = NULL, filename = NULL, theme = 'classic')