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This script allows you to perform PCA plot visualization using the results of the MetaProViz analysis

Source: R/VizPCA.R
viz_pca.Rd

PCA plot visualization

Usage

viz_pca(
  data,
  metadata_info = NULL,
  metadata_sample = NULL,
  color_palette = NULL,
  scale_color = "discrete",
  shape_palette = NULL,
  show_loadings = FALSE,
  scaling = TRUE,
  pcx = 1,
  pcy = 2,
  theme = NULL,
  plot_name = "",
  save_plot = "svg",
  print_plot = TRUE,
  path = NULL
)

Arguments

data

SummarizedExperiment (se) file including assay and colData. If se file is provided, metadata_sample is extracted from the colData of the se object. metadata_feature, if available, are extracted from the rowData. Alternatively provide a DF with unique sample identifiers as row names and metabolite numerical values in columns with metabolite identifiers as column names. Use NA for metabolites that were not detected.

metadata_info

Optional: NULL or Named vector including at least one of those three information : c(color="ColumnName_Plot_SettingsFile", shape= "ColumnName_Plot_SettingsFile"). Default = NULL

metadata_sample

Optional: Only required if you did not provide se file in parameter data. Provide DF which contains metadata information about the samples, which will be combined with your input data based on the unique sample identifiers used as rownames. Default = NULL

color_palette

Optional: Provide customiced color-palette in vector format. For continuous scale use e.g. scale_color_gradient(low = "#88CCEE", high = "red") and for discrete scale c("#88CCEE", "#DDCC77","#661100", "#332288")Default = NULL

scale_color

Optional: Either "continuous" or "discrete" colour scale. For numeric or integer you can choose either, for character you have to choose discrete. Default = NULL

shape_palette

Optional: Provide customiced shape-palette in vector format. Default = NULL

show_loadings

Optional: TRUE or FALSE for whether PCA loadings are also plotted on the PCA (biplot) Default = FALSE

scaling

Optional: TRUE or FALSE for whether a data scaling is used Default = TRUE

pcx

Optional: Numeric value of the PC that should be plotted on the x-axis Default = 1

pcy

Optional: Numeric value of the PC that should be plotted on the y-axis Default = 2

theme

Optional: Selection of theme for plot, e.g. theme_grey(). You can check for complete themes here: https://ggplot2.tidyverse.org/reference/ggtheme.html. If default=NULL we use theme_classic(). Default = "discrete"

plot_name

Optional: String which is added to the output files of the PCA Default = ""

save_plot

Optional: Select the file type of output plots. Options are svg, png, pdf or NULL. Default = svg

print_plot

Optional: TRUE or FALSE, if TRUE Volcano plot is saved as an overview of the results. Default = TRUE

path

Optional: Path to the folder the results should be saved at. default: NULL

Value

List with two elements: Plot and Plot_Sized

Examples

data(intracell_raw_se)
Res <- viz_pca(intracell_raw_se)
#> Warning: ggrepel: 36 unlabeled data points (too many overlaps). Consider increasing max.overlaps
#> Warning: ggrepel: 38 unlabeled data points (too many overlaps). Consider increasing max.overlaps


data(intracell_raw)
Intra <- intracell_raw[, -c(2:4)] %>% tibble::column_to_rownames("Code")
Res <- viz_pca(Intra)
#> Warning: ggrepel: 36 unlabeled data points (too many overlaps). Consider increasing max.overlaps
#> Warning: ggrepel: 38 unlabeled data points (too many overlaps). Consider increasing max.overlaps