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outlier_detection

Source: R/Processing.R
outlier_detection.Rd

outlier_detection

Usage

outlier_detection(
  data,
  metadata_sample,
  metadata_info,
  core = FALSE,
  hotellins_confidence = 0.99
)

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 and consider converting any zeros to NA unless they are true zeros.

metadata_sample

DF which contains information about the samples, which will be combined with the input data based on the unique sample identifiers used as rownames.

metadata_info

Named vector containing the information about the names of the experimental parameters. c(Conditions="ColumnName_Plot_SettingsFile", Biological_Replicates="ColumnName_Plot_SettingsFile", core_media = "Columnname_Input_SettingsFile"). Column "Conditions" with information about the sample conditions, Column "BiologicalReplicates" including numerical values and Column "Columnname_Input_SettingsFile" is used to specify the name of the media controls in the Conditions.

core

Optional: If TRUE, a consumption-release experiment has been performed. If not normalised yet, provide information about control media sample names via metadata_info "core_media" samples. Default = FALSE

hotellins_confidence

Optional: Confidence level for Hotellin's T2 test. Default = 0.99

Value

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

This function can be run independently, but PCA-based outlier detection is recommended on already preprocessed data such as filtered, imputed, and where appropriate normalized input.

Examples

data(intracell_raw)
Intra <- intracell_raw %>% tibble::column_to_rownames("Code")
Res <- outlier_detection(
    data =
        Intra[-c(49:58), -c(1:3)] %>%
        dplyr::mutate_all(
            ~ ifelse(grepl("^0*(\\.0*)?$", as.character(.)), NA, .)
        ),
    metadata_sample = Intra[-c(49:58), c(1:3)],
    metadata_info = c(
        Conditions = "Conditions",
        Biological_Replicates = "Biological_Replicates"
    )
)
#> Outlier detection: Identification of outlier samples is performed using Hotellin's T2 test to define sample outliers in a mathematical way (Confidence = 0.99 ~ p.val < 0.01) (REF: Hotelling, H. (1931), Annals of Mathematical Statistics. 2 (3), 360-378, doi:https://doi.org/10.1214/aoms/1177732979). hotellins_confidence value selected: 0.99
#> No sample outliers were found
#> NA values are included in data that were set to 0 prior to performing PCA.
#> NA values are included in data that were set to 0 prior to performing PCA.