Cont2Gaus: A transfomation from count data into Gaussian data

In equSA: Learning High-Dimensional Graphical Models

Description

To transform count data into Gaussian distributed and also keep the consistency for contructing networks.

Usage

Cont2Gaus(iData,total_iteration=5000,stepsize=0.05)

Arguments

iData	a nxp count data matrix.
total_iteration	Total iteration number for Baysian random effect model-based transformation, default of 5000.
stepsize	The stepsize of updating parameters in transformation, default of 0.05.

Details

This is the function that transform the count data into Gaussian data which include two steps. First, we do data continuized transformation ContTran(data,...) and then we apply the semiparametric transformation (Liu, H et al, 2009) provided in huge packages to tranform continuized data into Gaussian distributed.

Value

Gaus	A nxp matrix of normalized data with Gaussian distribution.

Author(s)

Bochao [e-mail] and Faming Liang

References

Jia, B., Xu, S., Xiao, G., Lamba, V., Liang, F. (2017) Inference of Genetic Networks from Next Generation Sequencing Data. Biometrics.

Liu, H., Lafferty, J. and Wasserman, L. (2009). The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs. Journal of Machine Learning Research , 10, 2295-2328.

Examples

      
      
           library(equSA)
           data(count)
           Cont2Gaus(count,total_iteration=1000)
      

equSA documentation built on May 6, 2019, 1:06 a.m.