Description Usage Arguments Details Value Author(s) References See Also
Calculate consensus kME (eigengenebased connectivities) across multiple data sets, typically following a consensus module analysis.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  consensusKME(
multiExpr,
moduleLabels,
multiEigengenes = NULL,
consensusQuantile = 0,
signed = TRUE,
useModules = NULL,
metaAnalysisWeights = NULL,
corAndPvalueFnc = corAndPvalue, corOptions = list(), corComponent = "cor",
getQvalues = FALSE,
useRankPvalue = TRUE,
rankPvalueOptions = list(calculateQvalue = getQvalues, pValueMethod = "scale"),
setNames = NULL,
excludeGrey = TRUE,
greyLabel = if (is.numeric(moduleLabels)) 0 else "grey")

multiExpr 
Expression (or other numeric) data in a multiset format. 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. 
moduleLabels 
Module labels: one label for each gene in 
multiEigengenes 
Optional eigengenes of modules specified in 
signed 
logical: should the network be considered signed? In signed networks ( 
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 
consensusQuantile 
Quantile for the consensus calculation. Should be a number between 0 (minimum) and 1. 
metaAnalysisWeights 
Optional specification of metaanalysis weights for each input set. If given, must be a numeric vector
of length equal the number of input data sets (i.e., 
corAndPvalueFnc 
Function that calculates associations between expression profiles and eigengenes. See details. 
corOptions 
List giving additional arguments to function 
corComponent 
Name of the component of output of 
getQvalues 
logical: should qvalues (estimates of FDR) be calculated? 
useRankPvalue 
Logical: should the 
rankPvalueOptions 
Additional options for function 
setNames 
names for the input sets. If not given, will be taken from 
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. 
The function corAndPvalueFnc
is currently
is expected to accept arguments x
(gene expression profiles), y
(eigengene expression
profiles), and alternative
with possibilities at least "greater", "two.sided"
.
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 pvalues, 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.
Data frame with the following components (for easier readability the order here is not the same as in the actual output):
ID 
Gene ID, taken from the column names of the first input data set 
consensus.kME.1, consensus.kME.2, ... 
Consensus kME (that is, the requested quantile of the kMEs in the
individual data sets)in each module for each gene across the input data
sets. The module labels (here 1, 2, etc.) correspond to those in 
weightedAverage.equalWeights.kME1, weightedAverage.equalWeights.kME2, ... 
Average kME in each module for each gene across the input data sets. 
weightedAverage.RootDoFWeights.kME1, weightedAverage.RootDoFWeights.kME2, ... 
Weighted average kME in each module for each gene across the input data sets. The weight of each data set is proportional to the square root of the number of samples in the set. 
weightedAverage.DoFWeights.kME1, weightedAverage.DoFWeights.kME2, ... 
Weighted average kME in each module for each gene across the input data sets. The weight of each data set is proportional to number of samples in the set. 
weightedAverage.userWeights.kME1, weightedAverage.userWeights.kME2, ... 
(Only present if input 
meta.Z.equalWeights.kME1, meta.Z.equalWeights.kME2, ... 
Metaanalysis Z statistic for kME in each module,
obtained by weighing the Z scores in each set equally. Only returned if the function 
meta.Z.RootDoFWeights.kME1, meta.Z.RootDoFWeights.kME2, ... 
Metaanalysis Z statistic for kME in each module,
obtained by weighing the Z scores in each set by the square root of the number of
samples. Only returned if the function 
meta.Z.DoFWeights.kME1, meta.Z.DoFWeights.kME2, ... 
Metaanalysis Z statistic for kME in each module,
obtained by weighing the Z scores in each set by the number of
samples. Only returned if the function 
meta.Z.userWeights.kME1, meta.Z.userWeights.kME2, ... 
Metaanalysis Z statistic for kME in each module,
obtained by weighing the Z scores in each set by 
meta.p.equalWeights.kME1, meta.p.equalWeights.kME2, ... 
pvalues obtained from the equalweight metaanalysis Z statistics. Only returned if the function

meta.p.RootDoFWeights.kME1, meta.p.RootDoFWeights.kME2, ... 
pvalues obtained from the metaanalysis Z statistics with weights proportional to the square root of the
number of samples. Only returned if the function

meta.p.DoFWeights.kME1, meta.p.DoFWeights.kME2, ... 
pvalues obtained from the degreeoffreedom weight metaanalysis Z statistics. Only returned if the function

meta.p.userWeights.kME1, meta.p.userWeights.kME2, ... 
pvalues obtained from the usersupplied weight metaanalysis Z statistics. Only returned if

meta.q.equalWeights.kME1, meta.q.equalWeights.kME2, ... 
qvalues obtained from the equalweight metaanalysis pvalues. Only present if

meta.q.RootDoFWeights.kME1, meta.q.RootDoFWeights.kME2, ... 
qvalues obtained from the metaanalysis pvalues with weights proportional to the square root of the
number of samples. Only present if

meta.q.DoFWeights.kME1, meta.q.DoFWeights.kME2, ... 
qvalues obtained from the degreeoffreedom weight metaanalysis pvalues. Only present if

meta.q.userWeights.kME1, meta.q.userWeights.kME2, ... 
qvalues obtained from the userspecified weight metaanalysis pvalues. Only present if

The next set of columns contain the results of function rankPvalue
and are only present if
input useRankPvalue
is TRUE
. Some columns may be missing depending on the options specified in
rankPvalueOptions
. We explicitly list columns that are based on weighing each set equally; names of
these columns carry the suffix .equalWeights
pValueExtremeRank.ME1.equalWeights, pValueExtremeRank.ME2.equalWeights, ... 
This is the minimum between pValueLowRank and pValueHighRank, i.e. min(pValueLow, pValueHigh) 
pValueLowRank.ME1.equalWeights, pValueLowRank.ME2.equalWeights, ... 
Asymptotic pvalue for observing a consistently low value across the columns of datS based on the rank method. 
pValueHighRank.ME1.equalWeights, pValueHighRank.ME2.equalWeights, ... 
Asymptotic pvalue for observing a consistently low value across the columns of datS based on the rank method. 
pValueExtremeScale.ME1.equalWeights, pValueExtremeScale.ME2.equalWeights, ... 
This is the minimum between pValueLowScale and pValueHighScale, i.e. min(pValueLow, pValueHigh) 
pValueLowScale.ME1.equalWeights, pValueLowScale.ME2.equalWeights, ... 
Asymptotic pvalue for observing a consistently low value across the columns of datS based on the Scale method. 
pValueHighScale.ME1.equalWeights, pValueHighScale.ME2.equalWeights, ... 
Asymptotic pvalue for observing a consistently low value across the columns of datS based on the Scale method. 
qValueExtremeRank.ME1.equalWeights, qValueExtremeRank.ME2.equalWeights, ... 
local false discovery rate (qvalue) corresponding to the pvalue pValueExtremeRank 
qValueLowRank.ME1.equalWeights, qValueLowRank.ME2.equalWeights, ... 
local false discovery rate (qvalue) corresponding to the pvalue pValueLowRank 
qValueHighRank.ME1.equalWeights, lueHighRank.ME2.equalWeights, ... 
local false discovery rate (qvalue) corresponding to the pvalue pValueHighRank 
qValueExtremeScale.ME1.equalWeights, qValueExtremeScale.ME2.equalWeights, ... 
local false discovery rate (qvalue) corresponding to the pvalue pValueExtremeScale 
qValueLowScale.ME1.equalWeights, qValueLowScale.ME2.equalWeights, ... 
local false discovery rate (qvalue) corresponding to the pvalue pValueLowScale 
qValueHighScale.ME1.equalWeights,qValueHighScale.ME2.equalWeights, ... 
local false discovery rate (qvalue) corresponding to the pvalue pValueHighScale 
... 
Analogous columns corresponding to weighing individual sets by the square root of the number of
samples, by number of samples, and by user weights (if given). The corresponding column name suffixes are

The following set of columns summarize kME in individual input data sets.
kME1.Set_1, kME1.Set_2, ..., kME2.Set_1, kME2.Set_2, ... 
kME values for each gene in each module in each given data set. 
p.kME1.Set_1, p.kME1.Set_2, ..., p.kME2.Set_1, p.kME2.Set_2, ... 
pvalues corresponding to kME values for each gene in each module in each given data set. 
q.kME1.Set_1, q.kME1.Set_2, ..., q.kME2.Set_1, q.kME2.Set_2, ... 
qvalues corresponding to
kME values for each gene in each module in each given data set. Only returned if 
Z.kME1.Set_1, Z.kME1.Set_2, ..., Z.kME2.Set_1, Z.kME2.Set_2, ... 
Z statistics corresponding to
kME values for each gene in each module in each given data set. Only present if the function

Peter Langfelder
Langfelder P, Horvath S., WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008 Dec 29; 9:559.
signedKME for eigengene based connectivity in a single data set. corAndPvalue, bicorAndPvalue for two alternatives for calculating correlations and the corresponding pvalues and Z scores. Both can be used with this function.
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