plot_PVCA {proBatch} | R Documentation

## Plot variance distribution by variable

### Description

Plot variance distribution by variable

### Usage

    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"
    )

### Arguments

`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

### Value

`ggplot` object with the plot

### See Also

`sample_annotation_to_colors`, `ggplot`

### Examples

    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)

[Package proBatch version 1.10.0 Index]