keep_non_missing_percentage: keep features with percentage of non-missing

In MoseleyBioinformaticsLab/visualizationQualityControl: Development of visualization methods for quality control

keep features with percentage of non-missing

Description

Given a value matrix (features are rows, samples are columns), and sample classes, find those things that are not missing in at least a certain number of samples in one of the classes, and keep those features for further processing.

Usage

keep_non_missing_percentage(
  data_matrix,
  sample_classes = NULL,
  keep_num = 0.75,
  missing_value = NA,
  all = FALSE
)

Arguments

data_matrix	the matrix of values to work with
sample_classes	the classes of each sample
keep_num	what number of samples in each class need a non-missing value (see Details)
missing_value	what number(s) represents missing values (default NA)
all	is this an either / or OR does it need to be present in all?

Details

The number of samples that must be non-missing can be expressed either as a whole number (that is greater than one), or as a fraction that will be be multiplied by the number of samples in each class to get the lower limits for each of the classes. If there are multiple values that represent missingness, use a vector. For example, to to use both 0 and NA, you can do missing_value = c(NA, 0).

Value

logical

MoseleyBioinformaticsLab/visualizationQualityControl documentation built on Feb. 21, 2024, 1:06 a.m.