test_eigengene_explained_variance: Test the significance of a target eigengene

In fruciano/resampleWGCNA: Resampling-based analyses on WGCNA results

Description

Performs a permutation test of significance for the eigengene

Usage

test_eigengene_explained_variance(OriginalData, Original_blockwiseModules,
  target_module, permutations = 999, usebicor = FALSE, ...)

Arguments

OriginalData	Matrix or data frame containing the original data on which the blockwiseModules function has been run (observations in rows, variables in columns).
Original_blockwiseModules	output of the blockwiseModules function
target_module	module whose largest eigenvalue/eigengene will be tested
permutations	number of permutations to use
usebicor	whether one should use the standard eigengene function in WGCNA (default), or bicor
...	further parameters to be passed to either moduleEigengenes or bicor

Details

Given a dataset, a set of results from blockwiseModules, and a target module, this function performs a permutation test of the variance explained by the eigengene (first eigenvalue of the correlation matrix of the genes belonging to the module). The target module should be expressed in the same way as when using blockwiseModules (i.e., color or number) Notice that option bicor=TRUE can be extremely computationally demanding

Value

The function outputs a list with the following elements:

moduleEigengenes_ALL

The eigengenes for all the modules (will be empty if bicor=TRUE)

explained_variance_target_module

The observed explained variance for the target module

p_value

The p value obtained through permutation

Citation

If you use this function please cite Fruciano et al. 2019

References

Fruciano, C., Meyer, A., Franchini, P. 2019. Divergent allometric trajectories in gene expression and coexpression produce species differences in sympatrically speciating Midas cichlid fish. Genome Biology and Evolution 11, 1644-1657.

fruciano/resampleWGCNA documentation built on Feb. 11, 2022, 5:38 a.m.