hierarchicalConsensusKME  R Documentation 
This function calculates several measures of fuzzy module membership in hiearchical consensus modules.
hierarchicalConsensusKME( multiExpr, moduleLabels, multiWeights = NULL, multiEigengenes = NULL, consensusTree, signed = TRUE, useModules = NULL, metaAnalysisWeights = NULL, corAndPvalueFnc = corAndPvalue, corOptions = list(), corComponent = "cor", getFDR = FALSE, useRankPvalue = TRUE, rankPvalueOptions = list(calculateQvalue = getFDR, pValueMethod = "scale"), setNames = names(multiExpr), excludeGrey = TRUE, greyLabel = if (is.numeric(moduleLabels)) 0 else "grey", reportWeightType = NULL, getOwnModuleZ = TRUE, getBestModuleZ = TRUE, getOwnConsensusKME = TRUE, getBestConsensusKME = TRUE, getAverageKME = FALSE, getConsensusKME = TRUE, getMetaColsFor1Set = FALSE, getMetaP = FALSE, getMetaFDR = getMetaP && getFDR, getSetKME = TRUE, getSetZ = FALSE, getSetP = FALSE, getSetFDR = getSetP && getFDR, includeID = TRUE, additionalGeneInfo = NULL, includeWeightTypeInColnames = TRUE)
multiExpr 
Expression data in the multiset format (see 
moduleLabels 
A vector with one entry per column (gene or probe) in 
multiWeights 
optional observation weights for data in 
multiEigengenes 
Optional specification of module eigengenes of the modules ( 
consensusTree 
A list specifying the consensus calculation. See details. 
signed 
Logical: should module membership be considered singed? Signed membership should be used for signed (including signed hybrid) networks and means that negative module membership means the gene is not a member of the module. In other words, in signed networks negative kME values are not considered significant and the corresponding pvalues will be onesided. In unsigned networks, negative kME values are considered significant and the corresponding pvalues will be twosided. 
useModules 
Optional vector specifying which modules should be used. Defaults to all modules except the unassigned module. 
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 
getFDR 
Logical: should 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. 
reportWeightType 
One of 
getOwnModuleZ 
Logical: should metaanalysis Z statistic in own module be returned as a column of the output? 
getBestModuleZ 
Logical: should highest metaanalysis Z statistic across all modules and the corresponding module be returned as columns of the output? 
getOwnConsensusKME 
Logical: should consensus KME (eigengenebased connectivity) statistic in own module be returned as a column of the output? 
getBestConsensusKME 
Logical: should highest consensus KME across all modules and the corresponding module be returned as columns of the output? 
getAverageKME 
Logical: Should average KME be calculated? 
getConsensusKME 
Logical: should consensus KME be calculated? 
getMetaColsFor1Set 
Logical: should the metastatistics be returned if the input data only have 1 set? For 1 set, meta and individual kME values are the same, so metacolumns essentially duplicate individual columns. 
getMetaP 
Logical: should metaanalysis pvalues corresponding to the KME metaanalysis Z statistics be calculated? 
getMetaFDR 
Logical: should FDR estimates for the metaanalysis pvalues corresponding to the KME metaanalysis Z statistics be calculated? 
getSetKME 
Logical: should KME values for individual sets be returned? 
getSetZ 
Logical: should Z statistics corresponding to KME for individual sets be returned? 
getSetP 
Logical: should p values corresponding to KME for individual sets be returned? 
getSetFDR 
Logical: should FDR estimates corresponding to KME for individual sets be returned? 
includeID 
Logical: should gene ID (taken from column names of 
additionalGeneInfo 
Optional data frame with rows corresponding to genes in 
includeWeightTypeInColnames 
Logical: should weight type ( 
This function calculates several measures of (hierarchical) consensus KME (eigengenebased intramodular connectivity or fuzzy module membership) for all genes in all modules.
First, it calculates the metaanalysis Z statistics for correlations between genes and module eigengenes; this is known as the consensus module membership Z statistic. The metaanalysis weights can be specified by the user either explicitly or implicitly ("equal", "RootDoF" or "DoF").
Second, it can calculate the consensus KME, i.e., the hierarchical consensus of the KMEs (correlations with
eigengenes) across the individual sets. The consensus calculation is specified in the argument
consensusTree
;
typically, the consensusTree
used here will be the same as the one used for the actual consensus
network construction and module identification.
See newConsensusTree
for details on how to specify consensus trees.
Third, the function can also calculate the (weighted) average KME using the metaanalysis weights; the average KME can be interpreted as the metaanalysis of the KMEs in the individual sets. This is related to but somewhat distinct from the metaanalysis Z statistics.
In addition to these, optional output also includes, for each gene, KME values in the module to which the gene is assigned as well as the maximum KME values and modules for which the maxima are attained. For most genes, the assigned module will be the one with highest KME values, but for some genes the assigned module and module of maximum KME may be different.
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"
. If weights are
given, these are passed to corAndPvalueFnc
as argument weights.x
.
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, some of which may be missing depending on input options (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 
If given, a copy of additionalGeneInfo
.
Z.kME.inOwnModule 
Metaanalysis Z statistic for membership in assigned module. 
maxZ.kME 
Maximum metaanalysis Z statistic for membership across all modules. 
moduleOfMaxZ.kME 
Module in which the maximum metaanalysis Z statistic is attained. 
consKME.inOwnModule 
Consensus KME in assigned module. 
maxConsKME 
Maximum consensus KME across all modules. 
moduleOfMaxConsKME 
Module in which the maximum consensus KME is attained. 
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 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
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.
newConsensusTree
for more details on hierarchical consensus trees and calculations.
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