WGCNA: mutualInfoAdjacency – R documentation

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mutualInfoAdjacency

Calculate weighted adjacency matrices based on mutual information

Description

The function calculates different types of weighted adjacency matrices based on the mutual information between vectors (corresponding to the columns of the input data frame datE). The mutual information between pairs of vectors is divided by an upper bound so that the resulting normalized measure lies between 0 and 1.

Usage

mutualInfoAdjacency(
   datE, 
   discretizeColumns = TRUE, 
   entropyEstimationMethod = "MM", 
   numberBins = NULL)

Arguments

`datE`	`datE` is a data frame or matrix whose columns correspond to variables and whose rows correspond to measurements. For example, the columns may correspond to genes while the rows correspond to microarrays. The number of nodes in the mutual information network equals the number of columns of `datE`.
`discretizeColumns`	is a logical variable. If it is set to TRUE then the columns of `datE` will be discretized into a user-defined number of bins (see `numberBins`).
`entropyEstimationMethod`	takes a text string for specifying the entropy and mutual information estimation method. If `entropyEstimationMethod="MM"` then the Miller-Madow asymptotic bias corrected empirical estimator is used. If `entropyEstimationMethod="ML"` the maximum likelihood estimator (also known as plug-in or empirical estimator) is used. If `entropyEstimationMethod="shrink"`, the shrinkage estimator of a Dirichlet probability distribution is used. If `entropyEstimationMethod="SG"`, the Schurmann-Grassberger estimator of the entropy of a Dirichlet probability distribution is used.
`numberBins`	is an integer larger than 0 which specifies how many bins are used for the discretization step. This argument is only relevant if `discretizeColumns` has been set to TRUE. By default `numberBins` is set to sqrt(m) where m is the number of samples, i.e. the number of rows of `datE`. Thus the default is `numberBins`=sqrt(nrow(datE)).

Details

The function inputs a data frame datE and outputs a list whose components correspond to different weighted network adjacency measures defined beteween the columns of datE. Make sure to install the following R packages entropy, minet, infotheo since the function mutualInfoAdjacency makes use of the entropy function from the R package entropy (Hausser and Strimmer 2008) and functions from the minet and infotheo package (Meyer et al 2008). A weighted network adjacency matrix is a symmetric matrix whose entries take on values between 0 and 1. Each weighted adjacency matrix contains scaled versions of the mutual information between the columns of the input data frame datE. We assume that datE contains numeric values which will be discretized unless the user chooses the option discretizeColumns=FALSE. The raw (unscaled) mutual information and entropy measures have units "nat", i.e. natural logarithms are used in their definition (base e=2.71..). Several mutual information estimation methods have been proposed in the literature (reviewed in Hausser and Strimmer 2008, Meyer et al 2008). While mutual information networks allows one to detect non-linear relationships between the columns of datE, they may overfit the data if relatively few observations are available. Thus, if the number of rows of datE is smaller than say 200, it may be better to fit a correlation using the function adjacency.

Value

The function outputs a list with the following components:

`Entropy`	is a vector whose components report entropy estimates of each column of `datE`. The natural logarithm (base e) is used in the definition. Using the notation from the Wikipedia entry (http://en.wikipedia.org/wiki/Mutual_information), this vector contains the values Hx where x corresponds to a column in `datE`.
`MutualInformation`	is a symmetric matrix whose entries contain the pairwise mutual information measures between the columns of `datE`. The diagonal of the matrix `MutualInformation` equals `Entropy`. In general, the entries of this matrix can be larger than 1, i.e. this is not an adjacency matrix. Using the notation from the Wikipedia entry, this matrix contains the mutual information estimates I(X;Y)
`AdjacencySymmetricUncertainty`	is a weighted adjacency matrix whose entries are based on the mutual information. Using the notation from the Wikipedia entry, this matrix contains the mutual information estimates `AdjacencySymmetricUncertainty`=2*I(X;Y)/(H(X)+H(Y)). Since I(X;X)=H(X), the diagonal elements of `AdjacencySymmetricUncertainty` equal 1. In general the entries of this symmetric matrix `AdjacencySymmetricUncertainty` lie between 0 and 1.
`AdjacencyUniversalVersion1`	is a weighted adjacency matrix that is a simple function of the `AdjacencySymmetricUncertainty`. Specifically, `AdjacencyUniversalVersion1= AdjacencySymmetricUncertainty/(2- AdjacencySymmetricUncertainty)`. Note that f(x)= x/(2-x) is a monotonically increasing function on the unit interval [0,1] whose values lie between 0 and 1. The reason why we call it the universal adjacency is that dissUA=1-`AdjacencyUniversalVersion1` turns out to be a universal distance function, i.e. it satisfies the properties of a distance (including the triangle inequality) and it takes on a small value if any other distance measure takes on a small value (Kraskov et al 2003).
`AdjacencyUniversalVersion2`	is a weighted adjacency matrix for which dissUAversion2=1-`AdjacencyUniversalVersion2` is also a universal distance measure. Using the notation from Wikipedia, the entries of the symmetric matrix AdjacencyUniversalVersion2 are defined as follows `AdjacencyUniversalVersion2`=I(X;Y)/max(H(X),H(Y)).

Author(s)

Steve Horvath, Lin Song, Peter Langfelder

References

Hausser J, Strimmer K (2008) Entropy inference and the James-Stein estimator, with application to nonlinear gene association networks. See http://arxiv.org/abs/0811.3579

Patrick E. Meyer, Frederic Lafitte, and Gianluca Bontempi. minet: A R/Bioconductor Package for Inferring Large Transcriptional Networks Using Mutual Information. BMC Bioinformatics, Vol 9, 2008

Kraskov A, Stoegbauer H, Andrzejak RG, Grassberger P (2003) Hierarchical Clustering Based on Mutual Information. ArXiv q-bio/0311039

Examples

# Load requisite packages. These packages are considered "optional", 
# so WGCNA does not load them automatically.

if (require(infotheo, quietly = TRUE) && 
    require(minet, quietly = TRUE) && 
    require(entropy, quietly = TRUE))
{
  # Example can be executed.
  #Simulate a data frame datE which contains 5 columns and 50 observations
  m=50
  x1=rnorm(m)
  r=.5; x2=r*x1+sqrt(1-r^2)*rnorm(m)
  r=.3; x3=r*(x1-.5)^2+sqrt(1-r^2)*rnorm(m)
  x4=rnorm(m)
  r=.3; x5=r*x4+sqrt(1-r^2)*rnorm(m)
  datE=data.frame(x1,x2,x3,x4,x5)
  
  #calculate entropy, mutual information matrix and weighted adjacency 
  # matrices based on mutual information.
  MIadj=mutualInfoAdjacency(datE=datE)
} else 
  printFlush(paste("Please install packages infotheo, minet and entropy",
                   "before running this example."));

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