Outlier detection via POMA R Package

Usage

detect_outliers_POMA(
  se,
  ain = "log2",
  condition = NULL,
  method = "euclidean",
  type = "median",
  group = TRUE,
  coeff = 1.5
)

Arguments

se: SummarizedExperiment containing all necessary information of the proteomics data set
ain: String which data type should be used (default raw)
condition: Column name of condition (if NULL, condition saved in SummarizedExperiment will be taken)
method: String specifying the method that should be used to calculate the distance matrix
type: String specifying the type of distance calculation to centroid or spatial median
group: String specifying if the outlier detection should be performed multi-variate (with conditions) or on the complete data set
coeff: This value corresponds to the classical 1.5 in Q3 + 1.5 * IQR formula to detect outliers. By changing this value, the permissiveness in outlier detection will change.

Value

list of two ggplot objects and a data.table with outlier samples

Examples

data(tuberculosis_TMT_se)
poma_res <- detect_outliers_POMA(tuberculosis_TMT_se, ain="raw",
                                 condition = NULL, method="euclidean",
                                 type="median", group=TRUE, coeff = 1.5)
#> Condition of SummarizedExperiment used!
#> Warning: some squared distances are negative and changed to zero
#> Scale for fill is already present.
#> Adding another scale for fill, which will replace the existing scale.
#> Scale for colour is already present.
#> Adding another scale for colour, which will replace the existing scale.
#> Scale for fill is already present.
#> Adding another scale for fill, which will replace the existing scale.