dcTest: Statistical test for differential association analysis
In dcanr: Differential co-expression/association network analysis

Description Usage Arguments Details Value See Also Examples

Perform statistical tests for scores generated using dcScore. Selects appropriate tests for the different methods used in computing scores. The exact test is selected based on the scoring method used and cannot be manually specified. Available tests include the z-test and permutation tests. Parallel computation supported for the permutation test.

1	dcTest(dcscores, emat, condition, ...)

`dcscores`	a matrix, the result of the `dcScore` function. The results should be passed as produced by the function and not modified in intermediate steps
`emat`	a matrix, data.frame, ExpressionSet, SummarizedExperiment or DGEList. This should be the one passed to `dcScore`
`condition`	a numeric, (with 1's and 2's representing a binary condition), a factor with 2 levels or a character representing 2 conditions. This should be the one passed to `dcScore`
`...`	see details

Ensure that the score matrix passed to this function is the one produced by dcScore. Any modification to the result matrix will cause this function to fail. This is intended as the test need to be performed on the entire score matrix, not subsets.

The appropriate test is chosen automatically based on the scoring method used. A z-test is performed for the z-score method while no tests are performed for DiffCoEx, EBcoexpress and FTGI. Permutation tests are performed for the remainder of methods by permutation sample labels. Statistics from a permutation are pooled such that statistics from all scores are used to evaluate a single observed score.

Additional method specific parameters can be supplied to the function when performing permutation tests. B specifies the number of permutations to be performed and defaults to 20.

If a cluster exists, computation in a permutation test will be performed in parallel (see examples).

a matrix, of p-values (or scores in the case of DiffCoEx and EBcoexpress) representing significance of differential associations. DiffCoEx will return scores as the publication specifies direct thresholding of scores and EBcoexpress returns posterior probabilities.

dcMethods, dcScore

x <- matrix(rnorm(60), 2, 30)
cond <- rep(1:2, 15)
scores <- dcScore(x, cond, dc.method = 'mindy')
dcTest(scores, emat = x, condition = cond)

## Not run: 
#running in parallel
num_cores = 2
cl <- parallel::makeCluster(num_cores)
doSNOW::registerDoSNOW(cl) #or doParallel
set.seed(36) #for reproducibility
dcTest(scores, emat = x, condition = cond, B = 100)
parallel::stopCluster(cl)

## End(Not run)