Get list of analyses classified as technical outliers

Retrieves analysis IDs of data outliers based the principal components PCA with SD or MAD fences

Usage

detect_outlier_pca(
  data = NULL,
  variable,
  filter_data,
  pca_component,
  qc_types = NA,
  fence_multiplicator,
  summarize_fun = c("pca", "rma"),
  outlier_detection = c("sd", "mad"),
  log_transform = TRUE
)

Arguments

data: MRMhubExperiment object
variable: Feature variable used for outlier detection
filter_data: Use all (default) or qc-filtered data
pca_component: PCA component to be used
qc_types: QC types included in the outlier detection
fence_multiplicator: Multiplicator for SD or MAD, respectively.
summarize_fun: Function used to summarize the features, either "pca" based on PCA, or "rma" based on mean relative abundance (RMA) of all features
outlier_detection: Outlier detection method, either based on "sd" or "mad"
log_transform: Log-transform data for outlier detection

Value

MRMhubExperiment object