README.md

In XiDsLab/DtracePLN: PLNet: Sparse precision matrix estimation via lasso penalized D-trace loss in the multivariate Poisson log-normal model

PLNet: Network inference for poisson log-normal model based on Dtrace method

PLNet is a package for calculating the precision matrix of poission log-normal model using moment method and dtrace loss. We first calculate the covariance matrix by moment method and then calculate the precision matrix by dtrace loss using R package EQUAL.

Installation

PLNet is available on Github.

R Package installation

• Before installing PLNet package, please install cescwang85-EQUAL-da9cbbb.tar.gz on Github.
• For the development version, use the github install ```{r package github, eval = FALSE} remotes::install_github("https://github.com/XiDsLab/PLNet")

## Usage and main functions
The package comes with a simulation data and a single-cell RNA sequence data about Interferon β-Stimulated PBMCs to present the functionality of main function.

### PLNet
```{r load PLNet, eval = FALSE}
library(PLNet)

## simulation data for testing
data("obs_mat")
PLNet_res_sim<-PLNet::PLNet(obs_mat = PLNet::obs_mat,
Sd_est = "GMPR",lambda_vec = NULL , n_lambda = 50 , lambda_valuemax0 = 10,penalize.diagonal = TRUE)

## real data analysis
data("realdatamatrix")
PLNet_res_real<-PLNet::PLNet(obs_mat = PLNet::realdatamatrix,
Sd_est = "GMPR",lambda_vec = NULL , n_lambda = 50 , lambda_valuemax0 = 10,penalize.diagonal = TRUE)

data_generator

{r, warning = FALSE} library(MASS) data_generator_res<-PLNet::data_generator(n = 100, p = 50, densy_degree = 0.05, sd_ls = 0.1, mean_ls = log(10),value_nondiagonal = 0.3)

Reference

Please cite our work using the following references:

XiDsLab/DtracePLN documentation built on Dec. 18, 2021, 7:21 p.m.