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Feature Quality Control and Filtering

Source: R/feature_manipulation.R
feature_manipulation.Rd

Filters features (variables) in a matrix or data frame by removing those with missing values, non-numeric types, infinite values, or zero variance. This is useful for preparing data for downstream statistical analyses.

Usage

feature_manipulation(
  data,
  feature = NULL,
  is_matrix = FALSE,
  print_result = FALSE
)

Arguments

data

A matrix or data frame containing features to filter.

feature

Character vector of feature names to check. If `is_matrix = TRUE`, features are extracted from row names of the matrix.

is_matrix

Logical indicating whether `data` is a gene expression matrix (features as rows, samples as columns). If `TRUE`, the matrix is transposed for processing. Default is `FALSE`.

print_result

Logical indicating whether to print filtering statistics. Default is `FALSE`.

Value

Character vector of feature names that pass all quality checks.

Author

Dongqiang Zeng

Examples

eset_stad <- load_data("eset_stad")
feas <- feature_manipulation(
  data = eset_stad,
  feature = rownames(eset_stad),
  is_matrix = TRUE,
  print_result = TRUE
)
#> ℹ Removing 9060 features with zero variance
#> ✔ Retained 51423 of 60483 features