plot_sample_mean_or_boxplot {proBatch} | R Documentation

## Plot per-sample mean or boxplot (showing median and quantiles) vs order (if the real running order available)

### Description

Plot per-sample mean or boxplot (showing median and quantiles) vs order (if the real running order available)

### Usage

    plot_sample_mean(data_matrix, sample_annotation = NULL,
      sample_id_col = "FullRunName", order_col = "order",
      batch_col = "MS_batch", facet_col = NULL, color_by_batch = FALSE,
      color_scheme = "brewer", theme = "classic", plot_title = NULL,
      order_per_facet = FALSE, vline_color = "grey", ylimits = NULL)

    plot_boxplot(df_long, sample_annotation = NULL,
      sample_id_col = "FullRunName", measure_col = "Intensity",
      order_col = "order", batch_col = "MS_batch", facet_col = NULL,
      color_by_batch = TRUE, color_scheme = "brewer", theme = "classic",
      plot_title = NULL, order_per_facet = FALSE)

### Arguments

`data_matrix` | features (in rows) vs samples (in columns) matrix, with feature IDs in rownames and file/sample names as colnames. in most function,
`sample_annotation` | data matrix with 1) `sample_id_col` (this can be repeated as row names) 2) biological and 3) technical covariates (batches etc)
`sample_id_col` | name of the column in sample_annotation file, where the filenames (colnames of the data matrix are found)
`order_col` | column where running order is specified.
`batch_col` | column in `sample_annotation` that should be used for batch comparison
`facet_col` | recommended if more than one batch covariate is present. Faceting is most suited to examine instruments separately
`color_by_batch` | should the each batch be represented with its own color?
`color_scheme` | named vector, names corresponding to unique batch values as specified in `sample_annotation`
`theme` | ggplot theme, by default `classic`. Can be easily overriden (see examples)
`plot_title` | Title of the plot (usually, processing step + representation level (fragments, transitions, proteins))
`order_per_facet` | if order is defined ignoring facets (usually instrument), re-define order per-batch
`vline_color` | color of vertical lines, typically denoting different MS batches in ordered runs; should be `NULL` for experiments without intrinsic order
`ylimits` | range of y-axis to plot feature-level trends
`df_long` | data frame where each row is a single feature in a single sample, thus it has minimally, `sample_id_col`, `feature_id_col` and `measure_col`, but usually also `m_score` (in OpenSWATH output result file)
`measure_col` | if `df_long` is among the parameters, it is the column with expression/abundance/intensity, otherwise, it is used internally for consistency

### Details

functions for quick visual assessment of trends associated, overall or specific covariate-associated (see `batch_col` and `facet_col`)

### Value

ggplot2 class object. Thus, all aesthetics can be overriden

### See Also

`ggplot`

### Examples

    mean_plot <- plot_sample_mean(example_proteome_matrix, example_sample_annotation,
    order_col = 'order', batch_col = "MS_batch")

    boxplot <- plot_boxplot(example_proteome, example_sample_annotation,
    batch_col = "MS_batch")

[Package proBatch version 1.0.0 Index]