comparecor {diffcoexp} | R Documentation |
This function calculates correlation coefficients of all gene pairs under two conditions and compare them using Fisher's Z-transformation.
comparecor(exprs.1, exprs.2, r.method = c("pearson", "spearman")[1], q.method = c("BH", "holm", "hochberg", "hommel", "bonferroni", "BY", "fdr", "none")[1])
exprs.1 |
a SummarizedExperiment, data frame or matrix for condition 1, with gene IDs as rownames and sample IDs as column names. |
exprs.2 |
a SummarizedExperiment, data frame or matrix for condition 2, with gene IDs as rownames and sample IDs as column names. |
r.method |
a character string specifying the method to be used to calculate correlation coefficients. It is passed to the cor function of the WGCNA package. |
q.method |
a character string specifying the method for adjusting p values. It is passed to the p.adjust function of the stats package. |
a data frame containing the differences between the correlation coefficients under two consitions and their p values. It has the following columns:
|
Gene ID |
|
Gene ID |
|
correlation coefficients under condition 1 |
|
correlation coefficients under condition 2 |
|
difference between correlation coefficients under condition 2 and condition 1 |
|
p value under null hypothesis that correlation coefficient under condition 1 equals to zero |
|
p value under null hypothesis that correlation coefficient under condition 2 equals to zero |
|
p value under null hypothesis that difference between two correlation coefficients under two conditions equals to zero using Fisher's r-to-Z transformation |
|
adjusted p value under null hypothesis that correlation coefficient under condition 1 equals to zero |
|
adjusted p value under null hypothesis that correlation coefficient under condition 2 equals to zero |
|
adjusted p value under null hypothesis that the difference between two correlation coefficients under two conditions equals to zero using Fisher's r-to-Z transformation |
data(gse4158part) allowWGCNAThreads() res=comparecor(exprs.1 = exprs.1, exprs.2 = exprs.2, r.method = "spearman") #The result is a data frames. str(res)