<<<<<<< Updated upstream

Plot modules (vertically)

=======

Plot modules (vertical layout)

>>>>>>> Stashed changes

Plot WGCNA modules' mean expression profiles and heatmaps, align vertically.

Different from running WGCNA, the input data should have the replicates merged, instead of having multiple samples per group, time and feature (gene).

If certain time points are missing in some groups, NA values are added.

Usage

plot_modules_v(
  module,
  se_obj_merged,
  scale = TRUE,
  assay = 2,
  ylabel = "Log2 abundance normalised to Time 0",
  suffix = "",
  device = "png",
  save = NULL,
  width = 12,
  height = 8,
  height_ratio = 2,
  fontsize = 8,
  res = 300
)

Arguments

module

A data frame with columns "Feature" and "Module"

se_obj_merged

A SummarizedExperiment object, with one value for each feature at each time point in each group (replicates merged). The colData of the object should contain columns "Sample", "Group", and "Time". The object can be produced by split_groups(), merge_replicates() and merge_group().

scale

Whether to scale the data (z-score) across samples for each feature (default is TRUE)

assay

The assay index in the SummarizedExperiment object to use (default is 2, time 0 normalised data)

ylabel

Y axis label prefix (default is "Abundance")

suffix

Suffix for the saved image file name (default is an empty string)

device

Image file format for saving (default is "png")

save

Directory to save the plot, no saving if is NULL (default is NULL)

width

Width of the saved image (default is 12 (cm))

height

Height of the saved image (default is 8 (cm))

height_ratio

Ratio of height of heatmap to the line plot (default is 2)

fontsize

Font size (default is 8)

res

Resolution of the saved image (except pdf format) (default is 300 (ppi))

Value

Two plots aligned vertically by groups: the top one is a line plot of module feature mean expression profiles, the bottom one is a heatmap of feature expression across time points.

Examples

library(dplyr)

data(example)
example_obj <- normalise_to_start(example_obj)
example_obj_list <- split_groups(example_obj)
example_obj_merged_list <- merge_replicates(example_obj_list)
example_obj_merged <- merge_groups(example_obj_merged_list)

data(example_net)
example_module <- data.frame(Module = as.factor(example_net$colors)) %>%
    tibble::rownames_to_column("Feature") %>% arrange(Module)

plot_modules_v(example_module %>% filter(Module != '0'),
    example_obj_merged, scale = TRUE,
    ylabel = "Z-score of log2 (expression)",
    height_ratio = 2,
    fontsize = 6)
#> Warning: Removed 159 rows containing non-finite outside the scale range
#> (`stat_summary()`).