eset_cor: Helper function to calculate the correlation matrix.
In coreheat: Correlation Heatmaps

Description Usage Arguments Details Value References

View source: R/cormap2.R

Helper function to calculate the correlation matrix.

1	eset_cor(x, with.pvalues = TRUE, order.list = TRUE, verbose = FALSE)

`x`	(`ExpressionSet`, `data.frame` or `numeric`). A numeric data frame, matrix or an ExpressionSet object.
`with.pvalues`	(`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`.
`order.list`	(`logical`). Is the input matrix column order reversed? Only applicable if input is correlation matrix. Defaults to `TRUE`.
`verbose`	(`logical`). Should verbose output be written to the console? Defaults to `FALSE`.

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".

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.

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.

coreheat documentation built on Sept. 19, 2021, 9:07 a.m.