prepSim {muscat} | R Documentation

## SCE preparation for `simData`

### Description

`prepSim` prepares an input SCE for simulation with `muscat`'s `simData` function by

1. basic filtering of genes and cells

2. (optional) filtering of subpopulation-sample instances

3. estimation of cell (library sizes) and gene parameters (dispersions and sample-specific means), respectively.

### Usage

    prepSim(
      x,
      min_count = 1,
      min_cells = 10,
      min_genes = 100,
      min_size = 100,
      group_keep = NULL,
      verbose = TRUE
    )

### Arguments

`x` | a `SingleCellExperiment`.
`min_count, min_cells` | used for filtering of genes; only genes with a count > `min_count` in >= `min_cells` will be retained.
`min_genes` | used for filtering cells; only cells with a count > 0 in >= `min_genes` will be retained.
`min_size` | used for filtering subpopulation-sample combinations; only instances with >= `min_size` cells will be retained. Specifying `min_size = NULL` skips this step.
`group_keep` | character string; if `nlevels(x$group_id) > 1`, specifies which group of samples to keep (see details). The default NULL retains samples from `levels(x$group_id)[1]`; otherwise, if 'colData(x)$group_id' is not specified, all samples will be kept.
`verbose` | logical; should information on progress be reported?

### Details

For each gene g, `prepSim` fits a model to estimate sample-specific means β_g^s, for each sample s, and dispersion parameters φ_g using `edgeR`'s `estimateDisp` function with default parameters. Thus, the reference count data is modeled as NB distributed:

Y_{gc} \sim NB(μ_{gc}, φ_g)

for gene g and cell c, where the mean μ_{gc} = \exp(β_{g}^{s(c)}) \cdot λ_c. Here, β_{g}^{s(c)} is the relative abundance of gene g in sample s(c), λ_c is the library size (total number of counts), and φ_g is the dispersion.

### Value

a `SingleCellExperiment` containing, for each cell, library size (`colData(x)$offset`) and, for each gene, dispersion and sample-specific mean estimates (`rowData(x)$dispersion` and `$beta.sample_id`, respectively).

### Author(s)

Helena L Crowell

### References

Crowell, HL, Soneson, C, Germain, P-L, Calini, D, Collin, L, Raposo, C, Malhotra, D & Robinson, MD: On the discovery of population-specific state transitions from multi-sample multi-condition single-cell RNA sequencing data. bioRxiv 713412 (2018). doi: https://doi.org/10.1101/713412

### Examples

    data(sce)
    library(SingleCellExperiment)

    ref <- prepSim(sce)

    # nb. of genes/cells before vs. after
    ns <- cbind(before = dim(sce), after = dim(ref))
    rownames(ns) <- c("#genes", "#cells"); ns

    head(rowData(ref)) # gene parameters
    head(colData(ref)) # cell parameters

[Package muscat version 1.1.6 Index]