Boxplot trelliscope building function for abundance data — trelli_abundance

Specify a boxplot design and cognostics for the abundance boxplot trelliscope. Each boxplot will have its own groups as specified by the first main effect in group_designation. Use "trelli_rnaseq_boxplot" for RNA-Seq data.

trelli_abundance_boxplot(
  trelliData,
  cognostics = c("count", "mean abundance"),
  ggplot_params = NULL,
  interactive = FALSE,
  include_points = TRUE,
  path = .getDownloadsFolder(),
  name = "Trelliscope",
  test_mode = FALSE,
  test_example = 1,
  single_plot = FALSE,
  ...
)

Arguments

trelliData: A trelliscope data object made by as.trelliData or as.trelliData.edata, and grouped by trelli_panel_by. Required.
cognostics: A vector of cognostic options for each plot. Valid entries are "count", "mean abundance", "median abundance", and "cv abundance". If data are paneled by a biomolecule, the count will be "sample count". If data are paneled by a sample or a biomolecule class, the count will be "biomolecule count". If statRes data is included, "anova p-value" and "fold change" data per comparisons may be added. If grouping information is included, only "sample count" and "mean abundance" will be calculated, along with "anova p-value" and "fold change" if specified. "anova p-value" will not be included if paneling a trelliscope display by a biomolecule class. Default is "sample count" and "mean abundance".
ggplot_params: An optional vector of strings of ggplot parameters to the backend ggplot function. For example, c("ylab(”)", "ylim(c(2,20))"). 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.
include_points: Add points as a geom_jitter. Default is TRUE.
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 boxplots that is stored in `path`

Author

David Degnan, Lisa Bramer

Examples

# \donttest{
if (interactive()) {
library(pmartRdata)

trelliData1 <- as.trelliData.edata(e_data = pep_edata,
                                   edata_cname = "Peptide",
                                   omics_type = "pepData")
# 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
trelliData2 <- as.trelliData(omicsData = omicsData)
trelliData4 <- as.trelliData(omicsData = omicsData, statRes = statRes)

# Build the abundance boxplot with an edata file where each panel is a biomolecule. 
trelli_panel_by(trelliData = trelliData1, panel = "Peptide") %>% 
   trelli_abundance_boxplot(test_mode = TRUE, test_example = 1:10, path = tempdir())

# Build the abundance boxplot wher each panel is a sample.
# Include all applicable cognostics. Remove points. 
trelli_panel_by(trelliData = trelliData1, panel = "Sample") %>% 
   trelli_abundance_boxplot(test_mode = TRUE, test_example = 1:10, 
                            include_points = FALSE,
                            cognostics = c("count", 
                                           "mean abundance", 
                                           "median abundance", 
                                           "cv abundance"),
                             path = tempdir()
                           )

# Build the abundance boxplot with an omicsData object.
# Let the panels be biomolecules. Here, grouping information is included.
trelli_panel_by(trelliData = trelliData2, panel = "Peptide") %>% 
   trelli_abundance_boxplot(test_mode = TRUE, test_example = 1:10, path = tempdir())

# Build the abundance boxplot with an omicsData object. The panel is a biomolecule class,
# which is proteins in this case.
trelli_panel_by(trelliData = trelliData2, panel = "RazorProtein") %>% 
   trelli_abundance_boxplot(test_mode = TRUE, test_example = 1:10, path = tempdir())

# Build the abundance boxplot with an omicsData and statRes object.
# Panel by a biomolecule, and add statistics data to the cognostics
trelli_panel_by(trelliData = trelliData4, panel = "Peptide") %>%
   trelli_abundance_boxplot(test_mode = TRUE, test_example = 1:10, path = tempdir(),
                            cognostics = c("mean abundance", "anova p-value", "fold change"))

# Other options include modifying the ggplot  
trelli_panel_by(trelliData = trelliData1, panel = "Peptide") %>% 
   trelli_abundance_boxplot(test_mode = TRUE, test_example = 1:10, path = tempdir(),
     ggplot_params = c("ylab('')", "ylim(c(20,30))"))

# Or making the plot interactive 
trelli_panel_by(trelliData = trelliData4, panel = "RazorProtein") %>% 
    trelli_abundance_boxplot(
     interactive = TRUE, test_mode = TRUE, test_example = 1:10, path = tempdir())

DONTSHOW({closeAllConnections()})
}
# }