Volcano plot of expression values

plot_volcano() is a GGplot2 implementation for plotting the expression differences as foldchange ~ statistical significance. See also plot_proportion(). This function can take either a tidyproteomics data object or a table with the required headers.

Usage

plot_volcano(
  data = NULL,
  ...,
  log2fc_min = 1,
  log2fc_column = "log2_foldchange",
  significance_max = 0.05,
  significance_column = "adj_p_value",
  labels_column = "gene_name",
  show_pannels = TRUE,
  show_lines = TRUE,
  show_fc_scale = TRUE,
  show_title = TRUE,
  show_pval_1 = TRUE,
  point_size = NULL,
  color_positive = "dodgerblue",
  color_negative = "firebrick1",
  destination = "plot",
  height = 5,
  width = 8
)

Arguments

data

a tibble

...

two sample comparison

log2fc_min

a numeric defining the minimum log2 foldchange to highlight.

log2fc_column

a character defining the column name of the log2 foldchange values.

significance_max

a numeric defining the maximum statistical significance to highlight.

significance_column

a character defining the column name of the statistical significance values.

labels_column

a character defining the column name of the column for labeling.

show_pannels

a boolean for showing colored up/down expression panels.

show_lines

a boolean for showing threshold lines.

show_fc_scale

a boolean for showing the secondary foldchange scale.

show_title

input FALSE, TRUE for an auto-generated title or any charcter string.

show_pval_1

a boolean for showing expressions with pvalue == 1.

point_size

a character reference to a numerical value in the expression table

color_positive

a character defining the color for positive (up) expression.

color_negative

a character defining the color for negative (down) expression.

destination

a character string

height

a numeric

width

a numeric

Value

a ggplot2 object

Examples

library(dplyr, warn.conflicts = FALSE)
library(tidyproteomics)
hela_proteins %>%
   expression(knockdown/control) %>%
   plot_volcano(knockdown/control, log2fc_min = 0.5, significance_column = "p_value")
#> ℹ .. expression::t_test testing knockdown / control
#> ✔ .. expression::t_test testing knockdown / control [3.1s]
#> 
#> Warning: Removed 13 rows containing missing values or values outside the scale range
#> (`geom_text_repel()`).
#> Warning: ggrepel: 370 unlabeled data points (too many overlaps). Consider increasing max.overlaps


# generates the same out come
# hela_proteins %>%
#     expression(knockdown/control) %>%
#     export_analysis(knockdown/control, .analysis = "expression") %>%
#     plot_volcano(log2fc_min = 0.5, significance_column = "p_value")

# display the gene name instead
hela_proteins %>%
   expression(knockdown/control) %>%
   plot_volcano(knockdown/control, log2fc_min = 0.5, significance_column = "p_value", labels_column = "gene_name")
#> ℹ .. expression::t_test testing knockdown / control
#> ✔ .. expression::t_test testing knockdown / control [3.3s]
#> 
#> Warning: Removed 13 rows containing missing values or values outside the scale range
#> (`geom_text_repel()`).
#> Warning: ggrepel: 370 unlabeled data points (too many overlaps). Consider increasing max.overlaps