equSA (version 1.2.1)

JGGM: Joint estimation of Multiple Gaussian Graphical Models

Description

Infer networks from Multiple Gaussian data from differnt groups using our proposed fast Bayesian integrative method.

Usage

JGGM(data,ALPHA1=0.05,ALPHA2=0.01,structure = "temporal",parallel=FALSE,nCPUs)

Arguments

data

a list of \(n\)x\(p\) data matrices. \(n\) can be different for each dataset but \(p\) should be the same.

ALPHA1

The significance level of correlation screening. In general, a high significance level of correlation screening will lead to a slightly large separator set \(S_{ij}\), which reduces the risk of missing some important variables in the conditioning set. Including a few false variables in the conditioning set will not hurt much the accuracy of the \(\psi\)-partial correlation coefficient.

ALPHA2

The significance level of \(\psi\) screening.

structure

The depedent structure of multiple networks, either "temporal" or "spatial". The default is "temporal".

parallel

Should parallelization be used? (logical), default is FALSE.

nCPUs

Number of cores used for parallelization. Recommend to be equal to the number of datasets.

Value

A list of three elements:

A

An array of multiple adjacency matrices of networks which is a \(M\)x\(p\)x\(p\) array. \(M\) is the number of dataset groups, \(p\) is the dimension of variables in each group.

score.sep

Separately estimated \(\psi\) scores matrix for all pairs in multiple datasets. The first two columns denote the pair indices of variables \(i\) and \(j\) and the rest columns denote the estimated \(\psi\) scores for this pair in different groups.

score.joint

Estimated integrative \(\psi\) scores matrix for all pairs in multiple datasets. The first two columns denote the pair indices of variables \(i\) and \(j\) and the rest columns denote the estimated integrative \(\psi\) scores for this pair in different groups.

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Details

This is the function that can jointly estimate multiple GGMs which can integrate the information throughtout all datasets. The method mainly consists three steps: (i) separate estimation of \(\psi\)-scores for each dataset, (ii) identifies possible changes of each edge across different groups and integrate the \(\psi\) scores across different groups simultaneously and (iii) apply multiple hypothesis test to identify edges using integrated \(\psi\) scores. See Jia, B., et al (2018).

References

Jia, B., and Liang, F. (2018). A Fast Hybrid Bayesian Integrative Learning of Multiple Gene Regulatory Networks for Type 1 Diabetes. Submitted to Biostatistics.

Examples

# NOT RUN {
# }
# NOT RUN {
library(equSA)
data(SR0)
data(TR0)
data_all <- vector("list",2)
data_all[[1]] <- SR0
data_all[[2]] <- TR0
JGGM(data_all,ALPHA1=0.05,ALPHA2=0.05)

# }
# NOT RUN {

         
# }