plot_PVCA: Plot variance distribution by variable
In proBatch: Tools for Diagnostics and Corrections of Batch Effects in Proteomics

Description Usage Arguments Value See Also Examples

View source: R/proteome_wide_diagnostics.R

Plot variance distribution by variable

plot_PVCA(
  data_matrix,
  sample_annotation,
  feature_id_col = "peptide_group_label",
  sample_id_col = "FullRunName",
  technical_factors = c("MS_batch", "instrument"),
  biological_factors = c("cell_line", "drug_dose"),
  fill_the_missing = -1,
  pca_threshold = 0.6,
  variance_threshold = 0.01,
  colors_for_bars = NULL,
  filename = NULL,
  width = NA,
  height = NA,
  units = c("cm", "in", "mm"),
  plot_title = NULL,
  theme = "classic"
)

`data_matrix`	features (in rows) vs samples (in columns) matrix, with feature IDs in rownames and file/sample names as colnames. See "example_proteome_matrix" for more details (to call the description, use `help("example_proteome_matrix")`)
`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).
`technical_factors`	vector `sample_annotation` column names that are technical covariates
`biological_factors`	vector `sample_annotation` column names, that are biologically meaningful covariates
`fill_the_missing`	numeric value determining how missing values should be substituted. If `NULL`, features with missing values are excluded. If `NULL`, features with missing values are excluded.
`pca_threshold`	the percentile value of the minimum amount of the variabilities that the selected principal components need to explain
`variance_threshold`	the percentile value of weight each of the covariates needs to explain (the rest will be lumped together)
`colors_for_bars`	four-item color vector, specifying colors for the following categories: c('residual', 'biological', 'biol:techn', 'technical')
`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
`width`	option determining the output image width
`height`	option determining the output image width
`units`	units: 'cm', 'in' or 'mm'
`plot_title`	title of the plot (e.g., processing step + representation level (fragments, transitions, proteins) + purpose (meanplot/corrplot etc))
`theme`	ggplot theme, by default `classic`. Can be easily overriden

ggplot object with the plot

sample_annotation_to_colors, ggplot

matrix_test <- example_proteome_matrix[1:150, ]
pvca_plot <- plot_PVCA(matrix_test, example_sample_annotation, 
technical_factors = c('MS_batch', 'digestion_batch'),
biological_factors = c("Diet", "Sex", "Strain"))

## Not run: 
pvca_plot <- plot_PVCA(matrix_test, example_sample_annotation, 
technical_factors = c('MS_batch', 'digestion_batch'),
biological_factors = c("Diet", "Sex", "Strain"), 
filename = 'test_PVCA.png', width = 28, height = 22, units = 'cm')

## End(Not run)