Omics Quality Control — quality

This function is a wrapper function that performs the key quality controls steps on an 'omics data set. Key principles: 1. keep the source underlying data as it is 2. copy the source data to a new data layer called qcing for processing 3. build an exclusion list, accumulating codes for exclusion reasons 4. make any adjustments needed in the destination copy of the data, flag these in the exclusion list 5. copy the final result to a data layer called post_qc 6. return the Omiprep object with the newly populated data layers

Usage

quality_control(
  omiprep,
  source_layer = "input",
  sample_missingness = 0.2,
  feature_missingness = 0.2,
  feature_skewness_threshold = NULL,
  feature_skewness_direction = "left",
  total_peak_area_sd = 5,
  outlier_udist = 5,
  outlier_treatment = "leave_be",
  winsorize_quantile = 1,
  tree_cut_height = 0.5,
  feature_selection = "max_var_exp",
  pc_outlier_sd = 5,
  max_num_pcs = 10,
  sample_ids = NULL,
  feature_ids = NULL,
  features_exclude_but_keep = NULL,
  cores = NULL,
  fast = FALSE
)

Arguments

omiprep: an object of class Omiprep
source_layer: character, the data layer to summarise
sample_missingness: numeric 0-1, percentage of data missingness which should prompt exclusion of a sample
feature_missingness: numeric 0-1, percentage of data missingness which should prompt exclusion of a feature
feature_skewness_threshold: numeric, optional skewness threshold to exclude features with skewed distributions. Set to `NULL` to disable.
feature_skewness_direction: character, direction of skewness to apply when `feature_skewness_threshold` is set. One of `"left"`, `"right"`, or `"both"`.
total_peak_area_sd: numeric, number of TPA SD after which a sample would be excluded
outlier_udist: the unit distance in SD or IQR from the mean or median estimate, respectively outliers are identified at. Default value is 5.
outlier_treatment: character, how to handle outlier data values - options 'leave_be', 'turn_NA', or 'winsorize'
winsorize_quantile: numeric, quantile to winsorize to, only relevant if 'outlier_treatment'='winsorize'
tree_cut_height: numeric, the threshold for feature independence in hierarchical clustering. Default is 0.5.
feature_selection: character, either 'max_var_exp' or 'least_missingness', how to select the independent feature within clusters
pc_outlier_sd: numeric, number of PCA SD after which a sample would be excluded
max_num_pcs: numeric, the maximum number of PCs to use (look in) when filtering samples on PC outlier SD, default=10, set to NULL to use all informative PCs from the Scree analysis
sample_ids: character, vector of sample ids to retain and work with, all others samples will be excluded
feature_ids: character, vector of feature ids to retain and work with, all other features will be excluded
features_exclude_but_keep: character, vector of feature ids indicating features to exclude from the sample and PCA quality control analysis but keep in the data, OR a name of a logical column in the features data indicating the same
cores: number of cores available for parallelism; the default null will try find the maximum available cores - 1; set to 1 for linear, but potentially slow, computation of the correlation matrix.
fast: If TRUE, accelerates correlation computation by imputing missing values to the column minimum, pre-ranking all columns, and computing Pearson correlation on ranked data (approximating Spearman). Substantially faster than exact Spearman at large feature dimensions (\(p > 5000\)) but assumes missing data are missing at random. Features with high missingness will have inflated rank ties at the median (ensure these are filtered out appropriately with the missingness option). Default FALSE.