trelli_foldchange_bar {pmartR}

Bar chart trelliscope building function for fold_change

Description

Specify a plot design and cognostics for the fold_change barchart trelliscope. Fold change must be grouped by edata_cname.

Usage

trelli_foldchange_bar(
  trelliData,
  cognostics = c("fold change", "p-value"),
  p_value_thresh = 0.05,
  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 statRes results. Required.

cognostics

A vector of cognostic options for each plot. Valid entries and the defaults are "fold change" and "p-value". If the omics data is MS/NMR, p-value will be the results from the ANOVA test. If the omics data is sedData, the p-value will be the results from the function "diffexp_seq".

p_value_thresh

A value between 0 and 1 to indicate significant biomolecules for p_value_test. Default is 0.05.

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 bar plots 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
trelliData3 <- as.trelliData(statRes = statRes)
trelliData4 <- as.trelliData(omicsData = omicsData, statRes = statRes)

# Build fold_change bar plot with statRes data grouped by edata_colname.
trelli_panel_by(trelliData = trelliData3, panel = "Peptide") %>% 
  trelli_foldchange_bar(test_mode = TRUE, test_example = 1:10, path = tempdir())


}