This script allows you to perform PCA plot visualization using the results of the MetaProViz analysis 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: 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. includes experimental design and outlier column.
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: DF which contains information about the samples, which will be combined with your input data based on the unique sample identifiers used as rownames. Column "Conditions" with information about the sample conditions (e.g. "N" and "T" or "Normal" and "Tumor"), can be used for feature filtering and colour coding in the PCA. Column "AnalyticalReplicate" including numerical values, defines technical repetitions of measurements, which will be summarised. Column "BiologicalReplicates" including numerical values. Please use the following names: "Conditions", "Biological_Replicates", "Analytical_Replicates".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

Intra <- intracell_raw[,-c(1:3)]%>%tibble::column_to_rownames("Code")
#> Error in tibble::column_to_rownames(., "Code"): Can't find column `Code` in `.data`.
Res <- viz_pca(Intra)
#> Error: object 'Intra' not found