trelli_foldchange_heatmap {pmartR}

Heatmap trelliscope building function for fold_change

Description

Specify a plot design and cognostics for the fold_change heatmap 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_heatmap(
  trelliData,
  cognostics = "biomolecule count",
  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 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.

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 heatmaps 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)

##########################
## MS/NMR OMICS EXAMPLE ##
##########################

# Build fold_change bar plot with statRes data grouped by edata_colname.
trelli_panel_by(trelliData = trelliData4, panel = "RazorProtein") %>% 
  trelli_foldchange_heatmap(test_mode = TRUE, 
                            test_example = 1:10,
                            path = tempdir())


}