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idopNetwork

Most existing approaches for network reconstruction can only infer an overall network and, also, fail to capture a complete set of network properties. To address these issues, a new model has been developed, which converts static data into their ‘dynamic’ form. ‘idopNetwork’ can inferring informative, dynamic, omnidirectional and personalized networks.

Installation

You can install the development version of idopNetwork from GitHub with:

# install.packages("devtools")
devtools::install_github("cxzdsa2332/idopNetwork")

Vignette

We demonstrate how to use idopNetwork package to analysis gut microbiota and mustard microbiota data.(https://cxzdsa2332.github.io/idopNetwork/articles/idopNetwork_vignette.html)

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Version

Install

install.packages('idopNetwork')

Monthly Downloads

184

Version

0.1.2

License

GPL (>= 3)

Issues

0

Pull Requests

0

Stars

4

Forks

4

Maintainer

Ang Dong

Last Published

April 18th, 2023

Functions in idopNetwork (0.1.2)

gut_microbe

gut microbe OTU data (species level)
get_par_int

acquire initial parameters for functional clustering
data_match

match power_equation fit result for bi-variate model
normalization

min-max normalization
logsumexp

calculate log-sum-exp values
legendre_fit

generate curve based on legendre polynomials
fun_clu

main function for functional clustering
power_equation

use power equation parameters to generate y values
qdODEplot_convert

convert qdODE results to plot data
get_legendre_matrix

generate legendre matrix
mustard_microbe

mustard microbe OTU data
get_biSAD1

generate biSAD1 covariance matrix
get_interaction

Lasso-based variable selection
qdODEmod

quasi-dynamic lotka volterra model
darken

make color more dark
get_legendre_par

use legendre polynomials to fit a given data
power_equation_fit

use power equation to fit given dataset
data_cleaning

remove observation with too many 0 values
power_equation_plot

plot power equation fitting results
power_equation_all

use power equation to fit observed values
power_equation_base

use power equation to fit observed values
network_maxeffect

convert ODE results(ODE_solving2) to basic network plot table
network_plot

generate network plot
network_conversion

convert ODE results(ODE_solving2) to basic network plot table
get_mu

curve fit with modified logistic function
qdODE_plot_all

plot all decompose plot
qdODE_plot_base

plot single decompose plot
get_mu2

generate mean vectors with ck and stress condition
qdODE_ls

least-square fit for qdODE model
qdODE_parallel

wrapper for qdODE_all in parallel version
qdODE_fit

legendre polynomials fit to qdODE model
qdODE_all

wrapper for qdODE model
bifun_clu_plot

bifunctional clustering plot
biqdODE_plot_base

plot single decompose plot for two data
bipower_equation_plot

plot power equation fitting results for bi-variate model
biQ_function

Q-function to replace log-likelihood function
bifun_clu_parallel

parallel version for functional clustering
Q_function

Q-function to replace log-likelihood function
biqdODE_plot_all

plot all decompose plot for two data
bifun_clu_convert

convert result of bifunctional clustering result
bifun_clu

main function for bifunctional clustering
biget_par_int

acquire initial parameters for functional clustering
fun_clu_BIC

plot BIC results for functional clustering
fun_clu_convert

convert result of functional clustering result
fun_clu_select

select result of functional clustering result
fun_clu_plot

functional clustering plot
fun_clu_parallel

parallel version for functional clustering
get_SAD1_covmatrix

generate standard SAD1 covariance matrix