inferSSNetwork: Infers a Gene Regulatory Network from Steady-State Data

In tianyu-lu/dynUGENE: Dynamical Uncertainty-aware GEne NEtwork inference with random forests

Description

Given a dataframe genes as columns and different measurements as rows, returns the adjacency matrix of the inferred network, the estimated decay rates of each species, and the dynamics of the network learned by p random forests.

Usage

inferSSNetwork(
  data,
  mask = NULL,
  ntree = 10L,
  mtry = NULL,
  alpha = NULL,
  seed = 777,
  showPlot = FALSE
)

Arguments

data	A data.frame of gene expression values, should be numerics. Each row is a different measurement at steady state. Each column corresponds to one gene.
mask	A matrix which only includes the values 1 or NA. Must be of size numgenes*numgenes. If entry (i.j) = 1, then i can be used in predicting the value of j. Otherwise, the connection is snipped and such a dependency is not allowed when training the random forests.
ntree	A positive integer indicating the number of trees in each random forest. Equivalent to the ntree argument in the randomForest package. Defaults to 10L.
mtry	A positive integer indicating the number of randomly sampled candidates to use at each split of each random forest. Equivalent to the mtry argument in the randomForest package. Defaults to p/3, where p is the number of genes. This option is disabled when a mask is provided and the default value is used.
alpha	If not provided, assumed to be 1 for all genes. If provided, can be a vector of the degradation rates of each gene, or a single number (same rate for all genes).
seed	Random seed for reproducibility. Defaults to 777.
showPlot	Plots the weights matrix as a heatmap. Defaults to FALSE. #' @return Returns an object of class "ugene" with the following items: network - A matrix storing the importance weights w_ij of each pair of genes. alpha - A vector of the gene product degradation rates, possibly inferred from data. model - A list of "randomForest" objects where model[i] is the trained randomForest able to predict changes in concentrations of gene i given the current concentrations of all genes.

References

Geurts, P. (2018). dynGENIE3: dynamical GENIE3 for the inference of gene networks from time series expression data. Scientific reports, 8(1), 1-12.

A. Liaw and M. Wiener (2002). Classification and Regression by randomForest. R News 2(3), 18–22.

Examples

## Not run: 
   data <- grndata::syntren300.data
   ugene <- inferSSNetwork(data, showPlot = TRUE)

## End(Not run)

tianyu-lu/dynUGENE documentation built on Jan. 7, 2021, 6:27 p.m.