Remove Rare Variables with Too Many Missing Values

Remove Rare Variables with Too Many Missing Values

Usage

remove_rare(x, prop = NULL, n = NULL, by = NULL, strict = FALSE, min_n = NULL)

# S3 method for class 'glyexp_experiment'
remove_rare(x, prop = NULL, n = NULL, by = NULL, strict = FALSE, min_n = NULL)

# S3 method for class 'matrix'
remove_rare(x, prop = NULL, n = NULL, by = NULL, strict = FALSE, min_n = NULL)

# Default S3 method
remove_rare(x, prop = NULL, n = NULL, by = NULL, strict = FALSE, min_n = NULL)

Arguments

x

Either a glyexp_experiment object or a matrix. If a matrix, rows should be variables and columns should be samples.

prop

The proportion of missing values to use as a threshold. Variables with missing values above this threshold will be removed. Defaults to 0.5.

n

The number of missing values to use as a threshold. An alternative to prop.

by

Either a column name in sample_info (string) or a factor/vector specifying group assignments for each sample. Missing value counts or proportions will be calculated within each group.

strict

Works with by. If FALSE, remove a variable only if it passes the missing threshold in all groups. If TRUE, remove a variable if it passes the missing threshold in any group. See examples for more details.

min_n

The minimum number of non-missing values required for a variable to be kept. If NULL (default), it is calculated dynamically:

• For datasets with 1, 2, or 3 samples: min_n equals the sample count

• For datasets with >3 samples: min_n = 3

• When using by, the rule is applied within each group

Value

For glyexp_experiment input, returns a modified glyexp_experiment object. For matrix input, returns a filtered matrix.

Examples

# With glyexp_experiment
exp <- glyexp::toy_experiment
exp$expr_mat[1, 1] <- NA    # V1: 1/6 missing
exp$expr_mat[2, 1:3] <- NA  # V2: 3/6 missing
exp$expr_mat[3, 1:5] <- NA  # V3: 5/6 missing
exp$expr_mat[4, 1:6] <- NA  # V4: 6/6 missing
exp$expr_mat
#>    S1 S2 S3 S4 S5 S6
#> V1 NA  5  9 13 17 21
#> V2 NA NA NA 14 18 22
#> V3 NA NA NA NA NA 23
#> V4 NA NA NA NA NA NA

# Remove variables with more than 50% missing values.
remove_rare(exp, prop = 0.5)$expr_mat
#>    S1 S2 S3 S4 S5 S6
#> V1 NA  5  9 13 17 21
#> V2 NA NA NA 14 18 22

# Remove variables with more than 2 missing values.
remove_rare(exp, n = 2)$expr_mat
#>    S1 S2 S3 S4 S5 S6
#> V1 NA  5  9 13 17 21

# Remove variables if they have more than 1 missing value in all groups.
# In another word, keep variables as long as they have 1 or 0 missing value
# in any group.
remove_rare(exp, by = "group", strict = FALSE)$expr_mat
#>    S1 S2 S3 S4 S5 S6
#> V1 NA  5  9 13 17 21
#> V2 NA NA NA 14 18 22

# Keep only variables with no missing values.
remove_rare(exp, prop = 0)$expr_mat
#>      S1 S2 S3 S4 S5 S6

# Use custom min_n to require at least 4 non-missing values
remove_rare(exp, min_n = 4)$expr_mat
#>    S1 S2 S3 S4 S5 S6
#> V1 NA  5  9 13 17 21

# With matrix
mat <- matrix(c(1, 2, NA, 4, 5, NA, 7, 8, 9), nrow = 3)
mat_filtered <- remove_rare(mat, prop = 0.5)