trelli_foldchange_boxplot {pmartR} | R Documentation |
Boxplot trelliscope building function for fold_changes
Description
Specify a plot design and cognostics for the fold_change boxplot trelliscope. Fold change must be grouped by an emeta column, which means both an omicsData object and statRes are required to make this plot.
Usage
trelli_foldchange_boxplot(
trelliData,
cognostics = "biomolecule count",
p_value_thresh = 0.05,
include_points = TRUE,
ggplot_params = NULL,
interactive = FALSE,
path = .getDownloadsFolder(),
name = "Trelliscope",
test_mode = FALSE,
test_example = 1,
single_plot = FALSE,
...
)
Arguments
trelliData |
A trelliscope data object with omicsData and statRes results. Required. |
cognostics |
A vector of cognostic options for each plot. Valid entries are "biomolecule count", "proportion significant", "mean fold change", and "sd fold change". Default is "biomolecule count". |
p_value_thresh |
A value between 0 and 1 to indicate significant biomolecules for the anova (MS/NMR) or diffexp_seq (RNA-seq) test. Default is 0.05. |
include_points |
Add points. Default is TRUE. |
ggplot_params |
An optional vector of strings of ggplot parameters to the backend ggplot function. For example, c("ylab(”)", "xlab(”)"). Default is NULL. |
interactive |
A logical argument indicating whether the plots should be interactive or not. Interactive plots are ggplots piped to ggplotly (for now). Default is FALSE. |
path |
The base directory of the trelliscope application. Default is Downloads. |
name |
The name of the display. Default is Trelliscope. |
test_mode |
A logical to return a smaller trelliscope to confirm plot and design. Default is FALSE. |
test_example |
A vector of plot indices to return for test_mode. Default is 1. |
single_plot |
A TRUE/FALSE to indicate whether 1 plot (not a trelliscope) should be returned. Default is FALSE. |
... |
Additional arguments to be passed on to the trelli builder |
Value
No return value, builds a trelliscope display of fold_change boxplots that is stored in 'path'
Author(s)
David Degnan, Lisa Bramer
Examples
if (interactive()) {
library(pmartRdata)
# Transform the data
omicsData <- edata_transform(omicsData = pep_object, data_scale = "log2")
# Group the data by condition
omicsData <- group_designation(omicsData = omicsData, main_effects = c("Phenotype"))
# Apply the IMD ANOVA filter
imdanova_Filt <- imdanova_filter(omicsData = omicsData)
omicsData <- applyFilt(filter_object = imdanova_Filt, omicsData = omicsData,
min_nonmiss_anova = 2)
# Normalize my pepData
omicsData <- normalize_global(omicsData, "subset_fn" = "all", "norm_fn" = "median",
"apply_norm" = TRUE, "backtransform" = TRUE)
# Implement the IMD ANOVA method and compute all pairwise comparisons
# (i.e. leave the `comparisons` argument NULL)
statRes <- imd_anova(omicsData = omicsData, test_method = 'combined')
# Generate the trelliData object
trelliData4 <- as.trelliData(omicsData = omicsData, statRes = statRes)
# Build fold_change box plot with statRes data grouped by edata_colname.
trelli_panel_by(trelliData = trelliData4, panel = "RazorProtein") %>%
trelli_foldchange_boxplot(test_mode = TRUE,
test_example = 1:10,
cognostics = c("biomolecule count",
"proportion significant",
"mean fold change",
"sd fold change"),
path = tempdir()
)
#####################
## RNA-SEQ EXAMPLE ##
#####################
# Build fold_change box plot with statRes data grouped by edata_colname.
trelli_panel_by(trelliData = trelliData_seq4, panel = "Gene") %>%
trelli_foldchange_boxplot(test_mode = TRUE,
test_example = c(16823, 16890, 17680, 17976, 17981, 19281),
cognostics = c("biomolecule count",
"proportion significant",
"mean fold change",
"sd fold change"),
path = tempdir()
)
}