NetRep (version 1.0.2)

permutation: Permutation test P-values for module preservation statistics

Description

Evaluates the statistical significance of each module preservation test statistic for one or more modules.

requiredPerms: how many permutations do I need to be able to detect significance at a given threshold alpha?

Usage

permutationTest(nulls, observed, nVarsPresent, totalSize, alternative = "greater")
requiredPerms(alpha, alternative = "greater")

Arguments

nulls

a 3-dimension matrix where the columns correspond to module preservation statistics, rows correspond to modules, and the third dimension to null distribution observations drawn from the permutation procedure in modulePreservation.

observed

a matrix of observed values for each module preservation statistc (columns) for each module (rows) returned from modulePreservation.

nVarsPresent

a vector containing the number of variables/nodes in each module that was present in the test dataset. Returned as a list element of the same name by modulePreservation.

totalSize

the size of the test network used to perform the test. Returned as a list element of the same name by modulePreservation.

alternative

a character string specifying the alternative hypothesis, must be one of "greater" (default), "less", or "two.sided". You can specify just the initial letter.

alpha

desired significance threshold.

Value

The minimum number of permutations required to detect any significant associations at the provided alpha. The minimum p-value will always be smaller than alpha.

Details

Calculates exact p-values for permutation tests when permutations are randomly drawn with replacement using the permp function in the statmod package. This function may be useful for re-calculating permutation test P-values, for example when there are missing values due to sparse data. In this case the user may decide that these missing values should be assigned 0 so that P-values aren't signficant purely due to many incalculable statistics leading to low power.

References

1. Phipson, B. & Smyth, G. K. Permutation P-values should never be zero: calculating exact P-values when permutations are randomly drawn. Stat. Appl. Genet. Mol. Biol. 9, Article39 (2010).

Examples

data("NetRep")

# Set up input lists for each input matrix type across datasets. The list
# elements can have any names, so long as they are consistent between the
# inputs.
network_list <- list(discovery=discovery_network, test=test_network)
data_list <- list(discovery=discovery_data, test=test_data)
correlation_list <- list(discovery=discovery_correlation, test=test_correlation)
labels_list <- list(discovery=module_labels)

# How many permutations are required to Bonferroni adjust for the 4 modules 
# in the example data? 
nPerm <- requiredPerms(0.05/4) 

# Note that we recommend running at least 10,000 permutations to make sure 
# that the null distributions are representative.

preservation <- modulePreservation(
 network=network_list, data=data_list, correlation=correlation_list, 
 moduleAssignments=labels_list, nPerm=nPerm, discovery="discovery", 
 test="test"
)

# Re-calculate the permutation test P-values
p.values <- permutationTest(
  preservation$nulls, preservation$observed, preservation$nVarsPresent,
  preservation$totalSize, preservation$alternative
)