Feature Quality Control and Filtering

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

set.seed(123)
test_data <- data.frame(
  A = c(1, 2, 3),
  B = c(1, 1, 1), # zero variance
  C = c(1, NA, 3), # missing value
  D = c("a", "b", "c") # non-numeric
)
feas <- feature_manipulation(data = test_data, feature = colnames(test_data), print_result = TRUE)
#> ℹ Removing 1 feature with NA values
#> ℹ Removing 1 non-numeric feature
#> ℹ Removing 1 feature with zero variance
#> ✔ Retained 1 of 4 features
print(feas)
#> [1] "A"