Description Usage Arguments Details Value Author(s) References Examples
Infer networks from Multiple Gaussian data from differnt groups using our proposed fast Bayesian integrative method.
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data |
a list of nxp 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 ψ-partial correlation coefficient. |
ALPHA2 |
The significance level of ψ screening. |
structure |
The depedent structure of multiple networks, either "temporal" or "spatial". The default is "temporal". |
parallel |
Should parallelization be used? (logical), default is |
nCPUs |
Number of cores used for parallelization. Recommend to be equal to the number of datasets. |
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 ψ-scores for each dataset, (ii) identifies possible changes of each edge across different groups and integrate the ψ scores across different groups simultaneously and (iii) apply multiple hypothesis test to identify edges using integrated ψ scores. See Jia, B., et al (2018).
A list of three elements:
A |
An array of multiple adjacency matrices of networks which is a Mxpxp array. M is the number of dataset groups, p is the dimension of variables in each group. |
score.sep |
Separately estimated ψ 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 ψ scores for this pair in different groups. |
score.joint |
Estimated integrative ψ 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 ψ scores for this pair in different groups. |
Bochao Jiajbc409@gmail.com and Faming Liang
Jia, B., and Liang, F. (2018). A Fast Hybrid Bayesian Integrative Learning of Multiple Gene Regulatory Networks for Type 1 Diabetes. Submitted to Biostatistics.
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