A tool to perform correlation in a batch mode and return tidy result
Correlation analysis is very common, here we wrap several functions for correlation analysis to help us analyze datasets such as TCGA (The Cancer Genome Atlas) dataset in bioinformatics
if(!require("remotes")){install.packages("remotes")}
remotes::install_github("Byronxy/ezcor")ezcor() ##run basic correlation
ezcor_batch() ##run basic correlation in a batch mode
ezcor_bicor() ##run WCGAA Biweight Midcorrelation correlation
ezcor_bicor_batch() ##run WCGAA Biweight Midcorrelation correlation in a batch mode
ezcor_partial_cor() ##run partial correlation
ezcor_partial_cor_batch() ##run partial correlation in a batch modeWe select HALLMARK_HYPOXIA pathway genes from TCGA pan-cancer dataset to illustrate examples
data("exprSet", package = "ezcor", envir = environment())
exprSet[1:5,1:5]
#> patient tissue B3GALT6 ERRFI1 ENO1
#> TCGA-RZ-AB0B TCGA-RZ-AB0B UVM 3.4517 0.4865 9.6230
#> TCGA-V3-A9ZX TCGA-V3-A9ZX UVM 3.5971 0.6880 10.3269
#> TCGA-V3-A9ZY TCGA-V3-A9ZY UVM 3.3061 0.0014 9.0812
#> TCGA-V4-A9E5 TCGA-V4-A9E5 UVM 3.2841 -0.3940 10.5923
#> TCGA-V4-A9E8 TCGA-V4-A9E8 UVM 3.9929 -0.6873 10.3873
g1 <- colnames(exprSet)[3]
g2 <- colnames(exprSet)[4]
res <- ezcor::ezcor(data= exprSet,
split = TRUE,
split_var = "tissue",
var1 = g1,
var2 = g2,
cor_method = "spearman",
adjust_method = "none",
sig_label = TRUE,
verbose = TRUE)
head(res)
#> cor p.value method adjust v1 v2 group pstar
#> ACC 0.43894888 6.512276e-05 spearman none B3GALT6 ERRFI1 ACC ***
#> BLCA 0.10167474 4.034319e-02 spearman none B3GALT6 ERRFI1 BLCA *
#> BRCA 0.12752295 2.373650e-05 spearman none B3GALT6 ERRFI1 BRCA ***
#> CESC 0.03334268 5.625112e-01 spearman none B3GALT6 ERRFI1 CESC
#> CHOL -0.15958816 3.525194e-01 spearman none B3GALT6 ERRFI1 CHOL
#> COAD 0.04617798 4.349938e-01 spearman none B3GALT6 ERRFI1 COAD