Plots PCA and performs PERMANOVA

This function performs PCA analysis and generates a PCA plot with optional filtering of features and groups. It also conducts PERMANOVA and saves the results to CSV files.

Usage

PCAplot(
  mmo,
  color = NULL,
  outdir = "PCA",
  normalization = "Z",
  filter_id = FALSE,
  id_list = NULL,
  filter_group = FALSE,
  group_list = NULL,
  label = TRUE,
  save_output = TRUE
)

Arguments

mmo

The mmo object with feature data and metadata

color

A vector of colors for the groups in the plot. Make sure the names correspond to the group names in metadata

outdir

The output file path for the PCA plot (default: 'PCA')

normalization

The normalization method to use for feature data. Options are 'None', 'Log', 'Meancentered', or 'Z' (default: 'Z')

filter_id

Boolean to filter features by id_list (default: FALSE)

id_list

A vector of feature names to filter (default: NULL)

filter_group

Boolean to filter groups by group_list (default: FALSE)

group_list

A vector of group names to filter (default: NULL)

label

Boolean to indicate whether to label points with sample names (default: TRUE)

save_output

Boolean; if TRUE (default) write plot (.pdf) and PERMANOVA tables using outdir as prefix. If FALSE, nothing is written.

Value

A list with elements plot (ggplot), df (raw data to generate plots), and permanova (results from permanova_stat).

Examples

if (FALSE) {
PCAplot(
 mmo, color = c("Control" = "blue", "Treatment1" = "red", "Treatment2" = "green"),
 outdir = 'PCA_plot', normalization = 'None',
 filter_id = FALSE, filter_group = FALSE, label = FALSE
)
PCAplot(
 mmo, color = c("Control" = "blue", "Treatment1" = "red"),
 outdir = 'PCA_plot', normalization = 'Z',
 filter_id = TRUE, id_list = Glucosinolates,
 filter_group = TRUE, group_list = c("Control", "Treatment1"), label = TRUE
)
}