Selects top variable (up- and down-regulated) features based on adjusted p-value and log fold-change thresholds.
Usage
high_var_fea(
result,
target,
name_padj = "padj",
padj_cutoff = 1,
name_logfc,
logfc_cutoff = 0,
n = 10,
data_type = NULL
)Arguments
- result
Data frame or tibble. Statistical results containing feature, adjusted p-value, and logFC columns.
- target
Character. Column name of feature identifiers.
- name_padj
Character. Adjusted p-value column name. Default is `"padj"`.
- padj_cutoff
Numeric. Adjusted p-value threshold. Default is 1.
- name_logfc
Character. log2 fold-change column name.
- logfc_cutoff
Numeric. Absolute log2 fold-change threshold. Default is 0.
- n
Integer. Number of top up and top down features to select. Default is 10.
- data_type
Character or `NULL`. If `"survival"`, adjusts logFC interpretation. Default is `NULL`.
Examples
result_data <- data.frame(
gene = c("Gene1", "Gene2", "Gene3", "Gene4", "Gene5"),
padj = c(0.01, 0.02, 0.05, 0.001, 0.03),
logfc = c(-2, 1.5, -3, 2.5, 0.5)
)
high_var_fea(
result = result_data,
target = "gene",
name_padj = "padj",
name_logfc = "logfc",
n = 2,
padj_cutoff = 0.05,
logfc_cutoff = 1.5
)
#> ! Cutoff too strict for down-regulated features, only 1 found
#> ! Cutoff too strict for up-regulated features, only 1 found
#> [1] "Gene1" "Gene4"