Find metabolites with high variability across total pool samples

Find metabolites with high variability across total pool samples

Usage

pool_estimation(
  data,
  metadata_sample = NULL,
  metadata_info = NULL,
  cutoff_cv = 30,
  save_plot = "svg",
  save_table = "csv",
  print_plot = TRUE,
  path = NULL
)

Arguments

data

DF which contains 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. Can be either a full dataset or a dataset with only the pool samples.

metadata_sample

Optional: DF which contains information about the samples when a full dataset is inserted as Input_data. Column "Conditions" with information about the sample conditions (e.g. "N" and "T" or "Normal" and "Tumor"), has to exist.Default = NULL

metadata_info

Optional: NULL or Named vector including the Conditions and PoolSample information (Name of the Conditions column and Name of the pooled samples in the Conditions in the Input_SettingsFile) : c(Conditions="ColumnNameConditions, PoolSamples=NamePoolCondition. If no Conditions is added in the Input_metadata_info, it is assumed that the conditions column is named 'Conditions' in the Input_SettingsFile. ). Default = NULL

cutoff_cv

Optional: Filtering cutoff for high variance metabolites using the Coefficient of Variation. Default = 30

save_plot

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

save_table

Optional: File types for the analysis results are: "csv", "xlsx", "txt", ot NULL default: "csv"

print_plot

Optional: If TRUE prints an overview of resulting plots. Default = TRUE

path

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

Value

List with two elements: DF (including input and output table) and Plot (including all plots generated)

Examples

Intra <- intracell_raw %>%tibble::column_to_rownames("Code")
Res <- pool_estimation(data=Intra[ ,-c(1:3)],
                                metadata_sample=Intra[ , c(1:3)],
                                metadata_info = c(PoolSamples = "Pool", Conditions="Conditions"))
#> Warning: ggrepel: 36 unlabeled data points (too many overlaps). Consider increasing max.overlaps