RacipeSE {sRACIPE} | R Documentation

## RacipeSE constructor

### Description

Create an RacipeSE object. RacipeSE is an S4 class for Random Circuit Perturbation (RACIPE) simulations of networks in which a large number of models with randomized parameters are used for simulation of the circuit. Each model can be considered as a sample. It extends the SummarizedExperiment class to store and access the circuit, simulated gene expressions, parameters, intial conditions and other meta information. SummarizedExperiment slot assays is used for storing simulated gene expressions. The rows of these matrix-like elements correspond to various genes in the circuit and columns correspond to models. The first element is used for unperturbed deterministic simulations. The subsequent elements are used for stochastic simulations at different noise levels and/or knockout simulations. SummarizedExperiment slot rowData stores the circuit topology. It is a square matrix with dimension equal to the number of genes in the circuit. The values of the matrix represent the type of interaction in the gene pair given by row and column. 1 represents activation, 2 inhibition and 0 no interaction. This should not be set directly and `sracipeCircuit` accessor should be used instead. SummarizedExperiment slot colData contains the parameters and initial conditions for each model. Each gene in the circuit has two parameters, namely, its production rate and its degradation rate. Each interaction in the has three parameters, namely, threshold of activation, the hill coefficient, and the fold change. Each gene has one or more initial gene expression values as specified by nIC. This should not be modified directly and `sracipeParams` and `sracipeIC` accessors should be used instead. SummarizedExperiment slot metadata Contains metadata information especially the config list (containing the simulation settings), annotation, nInteraction (number of interactions in the circuit), normalized (whether the data is normalized or not), data analysis lists like pca, umap, cluster assignment of the models etc. The config list includes simulation parameters like integration method (stepper) and other lists or vectors like simParams, stochParams, hyperParams, options, thresholds etc. The list simParams contains values for parameters like the number of models (numModels), simulation time (simulationTime), step size for simulations (integrateStepSize), when to start recording the gene expressions (printStart), time interval between recordings (printInterval), number of initial conditions (nIC), output precision (outputPrecision), tolerance for adaptive runge kutta method (rkTolerance), parametric variation (paramRange). The list stochParams contains the parameters for stochastic simulations like the number of noise levels to be simulated (nNoise), the ratio of subsequent noise levels (noiseScalingFactor), maximum noise (initialNoise), whether to use same noise for all genes or to scale it as per the median expression of the genes (scaledNoise), ratio of shot noise to additive noise (shotNoise). The list hyperParams contains the parameters like the minimum and maximum production and degration of the genes, fold change, hill coefficient etc. The list options includes logical values like annealing (anneal), scaling of noise (scaledNoise), generation of new initial conditions (genIC), parameters (genParams) and whether to integrate or not (integrate). The user modifiable simulation options can be specified as arguments to `sracipeSimulate` function.

### Usage

    RacipeSE(
      .object = NULL,
      assays = SimpleList(),
      rowData = NULL,
      colData = DataFrame(),
      metadata = list(),
      ...
    )

### Arguments

`...` | Arguments passed to SummarizedExperiment

### Value

RacipeSE object

### Examples

    rSet <- RacipeSE()

[Package sRACIPE version 1.3.3 Index]