R/trelliPlots.R
trelli_foldchange_boxplot.RdSpecify 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.
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,
...
)A trelliscope data object with omicsData and statRes results. Required.
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".
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.
Add points. Default is TRUE.
An optional vector of strings of ggplot parameters to the backend ggplot function. For example, c("ylab(”)", "xlab(”)"). Default is NULL.
A logical argument indicating whether the plots should be interactive or not. Interactive plots are ggplots piped to ggplotly (for now). Default is FALSE.
The base directory of the trelliscope application. Default is Downloads.
The name of the display. Default is Trelliscope.
A logical to return a smaller trelliscope to confirm plot and design. Default is FALSE.
A vector of plot indices to return for test_mode. Default is 1.
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
No return value, builds a trelliscope display of fold_change boxplots that is stored in `path`
# \donttest{
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()
)
DONTSHOW({closeAllConnections()})
}
# }