ssGrid

require(GCModeller); #' The S-system based gene regulatory network inference toolkit (基于 S-system 动力学系统的基因表达调控网络拟合工具) imports "ssGrid" from "biosystem";

The S-system based gene regulatory network inference toolkit (基于 S-system 动力学系统的基因表达调控网络拟合工具)

This R# package module exposes the SGrid pipeline as a set of R functions:

sfit_options: create the fitting configuration object(FittingOptions);

sfit: infer the S-system gene regulatory network from a gene expression matrix(rows are genes and the columns are the time points), the optional prior knowledge network(PriorNetwork) is used as a soft constraint of the network structure;

network_edges, sgrid_parameters, sgrid_coefficients and sgrid_r2: query the fitted model, these functions return data.frame objects;

sgrid_simulate: integrate the fitted S-system model and return the trajectory as a data.frame;

sgrid_context: map a pseudobulk context(the raw counts) onto the model state space, so that it can be used as the ODE initial state;

perturb: run the in silico knockdown/knockout/overexpression experiment, optionally under a given sample context;

perturb_export: save the perturbation screening result as csv files;

sgrid_prior: read the prior knowledge regulatory network from a csv table(or a data.frame);

sgrid_save: export all of the model tables and the .plas model script;

and the binary serialization support of the fitted model: writeBin(model, con = file("network.zip")) and readBin(file("network.zip"), what = "ssgrid_network").

.NET clr type export
ssgrid_network: SSystemNetwork	The fitted S-system gene regulatory network model. (拟合得到的基因表达调控网络模型)
sfit_options: FittingOptions	The configuration object of the S-system gene regulatory network inference. (基因表达调控网络的S-system拟合参数配置)
sgrid_prior: PriorNetwork	先验调控网络
sgrid_screening: ScreeningResult	批量虚拟扰动筛选的结果

.NET clr function exports
sfit_options	create the fitting configuration object of the S-system network inference
sfit	infer the S-system gene regulatory network from the gene expression matrix
sgrid_prior	read the prior knowledge regulatory network from a csv table or a data.frame
network_edges	get the network topology of the fitted S-system model as a data.frame
sgrid_parameters	get the fitted kinetic parameters of each gene as a data.frame
sgrid_coefficients	get the coefficient matrix of the fitted S-system model
sgrid_r2	get the R square of the slope fitting of each gene
sgrid_report	get the fitting report of the S-system network model
sgrid_save	export the model tables and the generated `.plas` model script into a directory
sgrid_simulate	integrate the fitted S-system model and return the trajectory
sgrid_context	map a pseudobulk context(the raw expression counts) onto the model state space
perturb	run the in silico perturbation experiment on the fitted S-system network model
perturb_export	save the virtual perturbation screening result as csv files

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