mv_feature_filter: Filter features by missing values

In computational-metabolomics/structtoolbox: Data processing & analysis tools for Metabolomics and other omics

Filter features by missing values

Description

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.

Details

This object makes use of functionality from the following packages:

• pmp

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.

Inheritance

A mv_feature_filter object inherits the following struct classes:

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

References

Jankevics A, Lloyd GR, Weber RJM (2023). pmp: Peak Matrix Processing and signal batch correction for metabolomics datasets. doi:10.18129/B9.bioc.pmp https://doi.org/10.18129/B9.bioc.pmp, R package version 1.14.0, https://bioconductor.org/packages/pmp.

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)

computational-metabolomics/structtoolbox documentation built on Feb. 9, 2024, 8:19 a.m.