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Filter features by missing values

Source: R/mv_feature_filter_class.R
mv_feature_filter.Rd

Removes features where the percentage of non-missing values falls below a threshold.

Usage

mv_feature_filter(
  threshold = 20,
  qc_label = "QC",
  method = "QC",
  factor_name,
  ...
)

Arguments

threshold

(numeric) The minimum percentage of non-missing values. The default is 20.

qc_label

(character) The label used to identify QC/group samples when using the "QC" (within a named group) filtering method. The default is "QC".

method

(character) Filtering method. Allowed values are limited to the following:

  • "within_all": Features are removed if the threshold for non-missing values is not met for all groups.

  • "within_one": Features are removed if the threshold for non-missing values is not met for any group.

  • "QC": Features are removed if the threshold for non-missing values is not met for the named group.

  • "across": The filter is applied ignoring sample group.

The default is "QC".

factor_name

(character) The name of a sample-meta column to use.

...

Additional slots and values passed to struct_class.

Value

A mv_feature_filter object with the following output slots:

filtered(DatasetExperiment) A DatasetExperiment object containing the filtered data.
flags(data.frame) % missing values and a flag indicating whether the sample was rejected. 0 = rejected.

Details

This object makes use of functionality from the following packages:

  • pmp

Inheritance

A mv_feature_filter object inherits the following struct classes:

[mv_feature_filter] >> [model] >> [struct_class]

References

Jankevics A, Lloyd GR, Weber RJM (????). pmp: Peak Matrix Processing and signal batch correction for metabolomics datasets. R package version 1.15.1.

Examples

M = mv_feature_filter(
      threshold = 20,
      qc_label = "QC",
      method = "QC",
      factor_name = "V1")

D = iris_DatasetExperiment()
M = mv_feature_filter(factor_name='Species',qc_label='versicolor')
M = model_apply(M,D)