Performs correlation analysis between a target variable and multiple feature variables. Computes correlation coefficients, p-values, and adjusts for multiple testing using the Benjamini-Hochberg method.
Usage
batch_cor(data, target, feature, method = c("spearman", "pearson", "kendall"))Arguments
- data
Data frame containing the target and feature variables.
- target
Character string specifying the name of the target variable.
- feature
Character vector specifying the names of feature variables to correlate with the target.
- method
Character string specifying the correlation method. Options are `"spearman"`, `"pearson"`, or `"kendall"`. Default is `"spearman"`.
Value
Tibble containing the following columns for each feature:
- sig_names
Feature name
- p.value
Raw p-value
- statistic
Correlation coefficient
- p.adj
Adjusted p-value (Benjamini-Hochberg)
- log10pvalue
Negative log10-transformed p-value
- stars
Significance stars: **** p<0.0001, *** p<0.001, ** p<0.01, * p<0.05, + p<0.5
Examples
# Load TCGA-STAD signature data
sig_stad <- load_data("sig_stad")
# Perform batch correlation
results <- batch_cor(
data = sig_stad,
target = "CD_8_T_effector",
feature = colnames(sig_stad)[69:ncol(sig_stad)]
)
#> ℹ Computing spearman correlation for 254 features
#> ✔ Correlation analysis complete
head(results)
#> # A tibble: 6 × 6
#> sig_names p.value statistic p.adj log10pvalue stars
#> <chr> <dbl> <dbl> <dbl> <dbl> <fct>
#> 1 CD8_Rooney_et_al 4.10e-234 0.971 1.04e-231 233. ****
#> 2 TIP_Killing_of_cancer_cells_1 1.35e-232 0.971 1.72e-230 232. ****
#> 3 Cytotoxic_cells_Danaher_et_al 5.18e-212 0.962 4.38e-210 211. ****
#> 4 T_cell_inflamed_GEP_Ayers_et_… 5.95e-204 0.958 3.78e-202 203. ****
#> 5 IFNG_signature_Ayers_et_al 8.63e-173 0.938 4.38e-171 172. ****
#> 6 TMEscoreA_plus 7.35e-154 0.920 3.11e-152 153. ****