R/trelliPlots.R
trelli_abundance_histogram.RdSpecify a plot design and cognostics for the abundance histogram trelliscope. Main_effects grouping are ignored. Data must be grouped by edata_cname. For RNA-Seq data, use "trelli_rnaseq_histogram".
trelli_abundance_histogram(
trelliData,
cognostics = c("sample count", "mean abundance", "median abundance", "cv abundance",
"skew abundance"),
ggplot_params = NULL,
interactive = FALSE,
path = .getDownloadsFolder(),
name = "Trelliscope",
test_mode = FALSE,
test_example = 1,
single_plot = FALSE,
...
)A trelliscope data object made by as.trelliData or as.trelliData.edata, and grouped by edata_cname in trelli_panel_by. Required.
A vector of cognostic options for each plot. Valid entries are "sample count", "mean abundance", "median abundance", "cv abundance", and "skew abundance". All are included by default.
An optional vector of strings of ggplot parameters to the backend ggplot function. For example, c("ylab(”)", "ylim(c(1,2))"). 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 histograms that is stored in `path`
# \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 histogram with an edata file.
# Generate trelliData in as.trelliData.edata
trelli_panel_by(trelliData = trelliData1, panel = "Peptide") %>%
trelli_abundance_histogram(test_mode = TRUE, test_example = 1:10, path = tempdir())
# Build the abundance histogram with an omicsData object.
# Generate trelliData in as.trelliData
trelli_panel_by(trelliData = trelliData2, panel = "Peptide") %>%
trelli_abundance_histogram(test_mode = TRUE, test_example = 1:10, path = tempdir())
# Build the abundance histogram with an omicsData and statRes object.
# Generate trelliData in as.trelliData.
trelli_panel_by(trelliData = trelliData4, panel = "Peptide") %>%
trelli_abundance_histogram(
test_mode = TRUE, test_example = 1:10, cognostics = "sample count", path = tempdir())
# Users can modify the plotting function with ggplot parameters and interactivity,
# and can also select certain cognostics.
trelli_panel_by(trelliData = trelliData1, panel = "Peptide") %>%
trelli_abundance_histogram(test_mode = TRUE, test_example = 1:10,
ggplot_params = c("ylab('')", "xlab('Abundance')"), interactive = TRUE,
cognostics = c("mean abundance", "median abundance"), path = tempdir())
DONTSHOW({closeAllConnections()})
}
# }