copa on a particular dataset, based
on permuting the class assignments.
copaPerm(object, copa, outlier.num, gene.pairs, B = 100, pval = FALSE, verbose = TRUE)ExpressionSet, or a matrix or data.frame.copa on a set of microarray data.FALSE. This result will only be reasonable
for large numbers of permutations (500 - 1000). See details.TRUEcopa on a set of microarray data will result in the
output of an object of class 'copa', which is a list containing (among
other things) an ordered vector that lists the number of mutually
exclusive outlier samples for various gene pairs. This vector is
ordered from smallest to largest following the assumption that the
gene pairs with the most mutually exclusive outliers are probably more
likely to be involved in some sort of recurrent fusion. One can see how many pairs of genes resulted in a given number of
outliers by calling tableCopa. One may then
want to determine how significant a certain number of pairs is (e.g.,
how likely is it to get that many pairs if there is no recurrent
fusion occuring). The most straightforward way to estimate the
significance of a given result is to repeatedly permute the
classlabels and see how many times one gets a result as large or
larger than what was observed.
Technically speaking, to get a reasonable estimate of significance and a false discovery rate, one would need to permute 500 - 1000 times. However, this can take an inordinate amount of time (best left for an overnight run). To get a quick idea of significance, one could simply permute maybe 10 times (with pval = FALSE) to see how likely it is to get a certain number of outliers.