Helper function to calculate the correlation matrix.
eset_cor(x, with.pvalues = TRUE, order.list = TRUE, verbose = FALSE)(ExpressionSet, data.frame or numeric). A numeric data frame, matrix or an ExpressionSet object.
(logical). Should P-Values be calculated for the correlations? If TRUE P-Values will be
depicted in the heatmap by significance asterisks. See 'Details'. Defaults to FALSE.
(logical). Is the input matrix column order reversed? Only applicable if input is correlation matrix.
Defaults to TRUE.
(logical). Should verbose output be written to the console? Defaults to FALSE.
A correlation matrix or a list with three slots: the correlation matrix, the number of samples with no
missing value for each gene and the P-Values matrix.
P-Values are calculated from the t-test value of the correlation coefficient: \(t = r x sqrt(n-2) / sqrt(1-r^2)\),
where r is the correlation coefficient, n is the number of samples with no missing values for each gene (row-wise
ncol(eset) minus the number of columns that have an NA). P-Values are then calculated using pt and
corrected account for the two-tailed nature of the test, i.e., the possibility of positive as well as negative correlation.
The approach to calculate correlation significance was adopted from Miles, J., & Banyard, P. (2007) on
"Calculating the exact significance of a Pearson correlation in MS Excel".
Miles, J., & Banyard, P. (2007). Understanding and using statistics in psychology: A practical introduction. Sage Publications Ltd. https://psycnet.apa.org/record/2007-06525-000.