simulatedData: Random Generation Networks for RNA-Seq Data

Description

A function that use a stochastic BA-modelfor building a graph and the simulated RNA-Seq counts (from a Poisson (multivariate or over-dispersed) distribution) that encode the underlying graph structure.

Usage

simulatedData(p = 50, n = 100, mu = 100, sigma = 0.25, ppower = 1, noise = F, seed = NULL)

Arguments

the number of genes in the networks.

the number of simulated samples for each gene.

the average mean of the simulated Poisson distributions.

sigma

the over-dispersed sd value in the case of over-dispersed Poisson simulation.

ppower

the power of the preferential attachment for the BA-model.

noise

logical. Should same noise be added to the data or not?

seed

a single value, interpreted as an integer, in order to control the simulated data.

Value

graph: the graph generated with the BA-model.
adjMat: the related adjacency matrix that encodes the underlying graph structure.
counts: the simulated RNA-Seq counts matrix.

References

Barabasi A.L., Albert R. (1999). Emergence of scaling in random networks. Science, 286 509-512. Gallopin M., Rau A., Jaffrezic F. (2013). A Hierarchical Poisson Log-Normal Model for Network Inference from RNA Sequencing Data. PLOSone.

Examples

Run this code

simData <- simulatedData(p = 50, n = 100, mu = 100, sigma = 0.25,
                        ppower = 0.73, noise = FALSE)
plot(simData$graph)

counts <- simData$counts
adjMat <- simData$adjMat