GEARS

require(GCModeller);

#' GEARS: the graph neural network based in silico perturbation prediction toolkit
imports "GEARS" from "biosystem";

GEARS: the graph neural network based in silico perturbation prediction toolkit

This R# package module provides the toolkit for train a GEARS model(Gene Expression Additive Response Simulator) from the Perturb-seq experiment data, the trained model can be used for predict the gene expression response of the in silico gene perturbation(the knockout/overexpression/knockdown experiment):

• new: create a new GEARS model from the gene expression matrix, the prior regulatory network and the model configuration;

• training_set: set the Perturb-seq training sample set of the GEARS model;

• train: train the GEARS model with the given training sample set.

the trained @T:SMRUCC.genomics.Analysis.GEARS.GEARS model object implements the @T:SMRUCC.genomics.Analysis.BNLearn.InsilicoPerturbationExperiment interface, so that it can be used by the knockouts, overexpress and knockdown api of the bnlearn package module, and the perturbation result can be exported via the make_exports api.