metamorphr (version 0.2.0)

plot_volcano: Draws a Volcano Plot or performs calculations necessary to draw one manually

Description

Performs necessary calculations (i.e., calculate p-values and log2-fold changes) and creates a basic Volcano Plot. The plot is drawn with ggplot2 and can therefore be easily manipulated afterwards (e.g., changing the theme or the axis labels). Please note that the function is intended to be easy to use and beginner friendly and therefore offers limited ability to fine-tune certain parameters of the resulting plot. If you wish to draw the plot yourself, you can set return_tbl = TRUE. In this case, a tibble is returned instead of a ggplot2 object which you can use to create a plot yourself. A Volcano Plot is used to compare two groups. Therefore grouping information must be provided. See join_metadata for more information.

Usage

plot_volcano(
  data,
  group_column,
  name_column,
  groups_to_compare,
  batch_column = NULL,
  batch = NULL,
  log2fc_cutoff = 1,
  p_value_cutoff = 0.05,
  colors = list(sig_up = "darkred", sig_down = "darkblue", not_sig_up = "grey",
    not_sig_down = "grey", not_sig = "grey"),
  adjust_p = FALSE,
  log2_before = FALSE,
  return_tbl = FALSE,
  ...
)

Value

Either a Volcano Plot in the form of a ggplot2 object or a tibble.

Arguments

data

A tidy tibble created by read_featuretable.

group_column

Which column should be used for grouping? Usually group_column = Group. Uses args_data_masking.

name_column

Which column contains the feature names? Can for example be name_column = UID or name_column = Feature. Uses args_data_masking.

groups_to_compare

Names of the groups which should be compared as a character vector. Those are the group names in the group_column. They are usually provided in the form of a metadata tibble and joined via join_metadata.

batch_column

Which column contains the batch information? Usually grouping_column = Batch. Only relevant if data contains multiple batches. For example, if data contains 2 batches and each batch contains measurements of separate controls, group_column and batch arguments should be provided. Otherwise controls of both batches will be considered when calculating the p-value and log2 fold change. Uses args_data_masking.

batch

The names of the batch(es) that should be included when calculating p-value and log2 fold change.

log2fc_cutoff

A numeric. What cutoff should be used for the log2 fold change? Traditionally, this is set to 1 which corresponds to a doubling or halving of intensity or area compared to a control. This is only important for assignment to groups and colors defined in the colors argument.

p_value_cutoff

A numeric. What cutoff should be used for the p-value? Traditionally, this is set to 0.05. This is only important for assignment to groups and colors defined in the colors argument. Note that this is not the -log10 transformed value.

colors

A named list for coloring the dots in the Volcano Plot or NULL in case the points should not be colored. The list must contain colors for the following groups: sig_up, sig_down, not_sig_up, not_sig_down and not_sig.

adjust_p

Should the p-value be adjusted? Can be either FALSE, (the default) in case no adjustment should be made or any or the name from p.adjust.methods (e.g., adjust_p = "fdr").

log2_before

A logical. Should the data be log2 transformed prior to calculating the p-values?

return_tbl

A logical. If FALSE, returns a ggplot2 object, if TRUE returns a tibble which can be used to draw the plot manually to have more control.

...

Arguments passed on to t.test. If none are provided (the default), a Welch Two Sample t-test will be performed.

Examples

# returns a Volcano Plot in the form of a ggplot2 object
toy_metaboscape %>%
  impute_lod() %>%
  join_metadata(toy_metaboscape_metadata) %>%
  plot_volcano(
    group_column = Group,
    name_column = Feature,
    groups_to_compare = c("control", "treatment")
  )

# returns a tibble to draw the plot manually
toy_metaboscape %>%
  impute_lod() %>%
  join_metadata(toy_metaboscape_metadata) %>%
  plot_volcano(
    group_column = Group,
    name_column = Feature,
    groups_to_compare = c("control", "treatment"),
    return_tbl = TRUE
  )