WGCNA: multiData.eigengeneSignificance – R documentation

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multiData.eigengeneSignificance

Eigengene significance across multiple sets

Description

This function calculates eigengene significance and the associated significance statistics (p-values, q-values etc) across several data sets.

Usage

multiData.eigengeneSignificance(
  multiData, multiTrait, 
  moduleLabels, multiEigengenes = NULL, 
  useModules = NULL, 
  corAndPvalueFnc = corAndPvalue, corOptions = list(), 
  corComponent = "cor", 
  getQvalues = FALSE, setNames = NULL, 
  excludeGrey = TRUE, greyLabel = ifelse(is.numeric(moduleLabels), 0, "grey"))

Arguments

`multiData`	Expression data (or other data) in multi-set format (see `checkSets`). A vector of lists; in each list there must be a component named `data` whose content is a matrix or dataframe or array of dimension 2.
`multiTrait`	Trait or ourcome data in multi-set format. Only one trait is allowed; consequesntly, the `data` component of each component list can be either a vector or a data frame (matrix, array of dimension 2).
`moduleLabels`	Module labels: one label for each gene in `multiExpr`.
`multiEigengenes`	Optional eigengenes of modules specified in `moduleLabels`. If not given, will be calculated from `multiExpr`.
`useModules`	Optional specification of module labels to which the analysis should be restricted. This could be useful if there are many modules, most of which are not interesting. Note that the "grey" module cannot be used with `useModules`.
`corAndPvalueFnc`	Function that calculates associations between expression profiles and eigengenes. See details.
`corOptions`	List giving additional arguments to function `corAndPvalueFnc`. See details.
`corComponent`	Name of the component of output of `corAndPvalueFnc` that contains the actual correlation.
`getQvalues`	logical: should q-values (estimates of FDR) be calculated?
`setNames`	names for the input sets. If not given, will be taken from `names(multiExpr)`. If those are `NULL` as well, the names will be `"Set_1", "Set_2", ...`.
`excludeGrey`	logical: should the grey module be excluded from the kME tables? Since the grey module is typically not a real module, it makes little sense to report kME values for it.
`greyLabel`	label that labels the grey module.

Details

This is a convenience function that calculates module eigengene significances (i.e., correlations of module eigengenes with a given trait) across all sets in a multi-set analysis. Also returned are p-values, Z scores, numbers of present (i.e., non-missing) observations for each significance, and optionally the q-values (false discovery rates) corresponding to the p-values.

The function corAndPvalueFnc is currently is expected to accept arguments x (gene expression profiles) and y (eigengene expression profiles). Any additional arguments can be passed via corOptions.

The function corAndPvalueFnc should return a list which at the least contains (1) a matrix of associations of genes and eigengenes (this component should have the name given by corComponent), and (2) a matrix of the corresponding p-values, named "p" or "p.value". Other components are optional but for full functionality should include (3) nObs giving the number of observations for each association (which is the number of samples less number of missing data - this can in principle vary from association to association), and (4) Z giving a Z static for each observation. If these are missing, nObs is calculated in the main function, and calculations using the Z statistic are skipped.

Value

A list containing the following components. Each component is a matrix in which the rows correspond to module eigengenes and columns to data sets. Row and column names are set appropriately.

`eigengeneSignificance`	Module eigengene significance.
`p.value`	p-values (returned by `corAndPvalueFnc`).
`q.value`	q-values corresponding to the p-values above. Only returned in input `getWvalues` is `TRUE`.
`Z`	Z statistics (if returned by `corAndPvalueFnc`).
`nObservations`	Number of non-missing observations in each correlation/p-value.

Author(s)

Peter Langfelder

WGCNA

Weighted Correlation Network Analysis

v1.70-3

GPL (>= 2)

Authors

Peter Langfelder <Peter.Langfelder@gmail.com> and Steve Horvath <SHorvath@mednet.ucla.edu> with contributions by Chaochao Cai, Jun Dong, Jeremy Miller, Lin Song, Andy Yip, and Bin Zhang

Initial release

2021-02-17