Filter features

Filter features

Usage

feature_filtering(
  data,
  metadata_sample,
  metadata_info,
  core = FALSE,
  featurefilt = "Modified",
  cutoff_featurefilt = 0.8,
  metadata_feature = 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 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"). 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 "BiologicalReplicates" including numerical values. For core = TRUE add core_media = "Columnname_Input_SettingsFile", which specifies the name of the media controls in the Conditions.

core

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

featurefilt

Optional: If NULL, no feature filtering is performed. If set to "Standard" then it applies the 80%-filtering rule (Bijlsma S. et al., 2006) on the metabolite features on the whole dataset. If is set to "Modified",filtering is done based on the different conditions, thus a column named "Conditions" must be provided in the Input_SettingsFile input file including the individual conditions you want to apply the filtering to (Yang, J et al., 2015). If set to "Manual", the features to remove are taken directly from metadata_feature instead of being computed from a cutoff. If the expected column is missing from metadata_feature, a warning is raised and "Modified" is used instead; if "Modified" itself cannot be used because Conditions has fewer than 2 levels, a further warning is raised and "Standard" is used. Default = Modified

cutoff_featurefilt

Optional: percentage of feature filtering. Ignored if featurefilt = "Manual". Default = 0.8

metadata_feature

Optional: Only used if featurefilt = "Manual". Data frame with feature identifiers matching the column names of data as row names, and a logical TRUE/FALSE column (TRUE = remove this feature) named "FilteredFeatures", or as configured via metadata_info. Default = NULL

Value

List with elements: DF (filtered matrix); FilteredFeatures (a data frame with one row per input feature, columns FeatureID and FilteredFeatures containing TRUE or FALSE; this column is always named "FilteredFeatures" regardless of what it was called in metadata_feature); featurefilt_used (the filtering method actually applied, which differs from featurefilt if a fallback was triggered); and, for SummarizedExperiment input, SE (the filtered data as a SummarizedExperiment).

This function can be used as a standalone preprocessing step on raw input data with matching sample metadata.

Examples

data(intracell_raw)
Intra <- intracell_raw %>% tibble::column_to_rownames("Code")
Res <- feature_filtering(
    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"
    )
)
#> feature_filtering: Here we apply the modified 80%-filtering rule that takes the class information (Column `Conditions`) into account, which additionally reduces the effect of missing values (REF: Yang et. al., (2015), doi: 10.3389/fmolb.2015.00004). Filtering value selected: 0.8
#> 3 metabolites where removed: AICAR, FAICAR, SAICAR