simulate_data {ccfindR} | R Documentation

## Generate simulated data for factorization

### Description

Use one of two schemes to generate simulated data suitable for testing factorization.

### Usage

    simulate_data(nfeatures, nsamples, generate.factors = FALSE,
      nfactor = 10, alpha0 = 0.5, shuffle = TRUE)

### Arguments

`nfeatures` | Number of features `m` (e.g., genes).
`nsamples` | Vector of sample sizes in each cluster. Rank `r` is equal to the length of this vector. Sum of elements is the total sample size `n`.
`generate.factors` | Generate factor matrices `W` and `H`, each with dimension `n x r` and `r x n`. If `FALSE`, factor matrices are not used and count data are generated directly from `r` multinomials for `m` genes.
`nfactor` | Total RNA count of multinomials for each cluster with `generate.factors = FALSE`. Small `nfactor` will yield sparse count matrix.
`alpha0` | Variance parameter of Dirichlet distribution from which multinomial probabilities are sampled with `generate.factors = FALSE`.
`shuffle` | Randomly permute rows and columns of count matrix.

### Details

In one scheme (`generate.factors = TRUE`), simulated factor matrices `W` and `H` are used to build count data `X = WH`. In the second scheme, factor matrices are not used and `X` is sampled directly from `r` (rank requested) sets of multinomial distributions.

### Value

If `generate.factors = TRUE`, list of components `w` (basis matrix, `nfeatures x rank`), `h` (coefficient matrix, `rank x ncells`, where `ncells` is equal to `n`, the sum of `nsamples`), and `x`, a matrix of Poisson deviates with mean `W x H`. If `generate.factors = FALSE`, only the count matrix `x` is in the list.

### Examples

    set.seed(1)
    x <- simulate_data(nfeatures=10,nsamples=c(20,20,60,40,30))
    s <- scNMFSet(x)
    s

[Package ccfindR version 1.7.0 Index]