batch_pcc: Batch way to calculate the partial correlation coefficient
| batch_pcc | R Documentation |
Batch way to calculate the partial correlation coefficient
Description
batch_pcc() provide a batch way to calculate the partial correlation coefficient between feature and others when controlling a third variable
Usage
batch_pcc(input, interferenceid, target, features, method = "pearson")
Arguments
input |
A data frame containing both feature variables and the interference variable. |
interferenceid |
The name of the column in the feature_data data frame representing the interference variable. |
target |
The name of the column in the input data frame representing the target variable for correlation. |
features |
A character vector specifying the names of the feature variables. |
method |
The correlation method to be used. Default value is "pearson"; options are "pearson", "spearman", or "kendall". |
pdata_group |
matrix;data signature matrix with multiple features |
Value
A tibble containing the feature names, partial correlation coefficients, p-values, adjusted p-values, log10 p-values, and significance stars.
Author(s)
Rongfang Shen
Examples
# Loading TCGA-STAD microenvironment signature data
data("sig_stad", package = "IOBR")
# Finding Pan_F_TBRs associated signature score excluding the effects of tumour purity.
res <- batch_pcc(input = sig_stad, interferenceid = "TumorPurity_estimate", target = "Pan_F_TBRs", method = "pearson", features = colnames(sig_stad)[70:ncol(sig_stad)])
