prepSim: SCE preparation for 'simData'

In HelenaLC/ddSingleCell: Multi-sample multi-group scRNA-seq data analysis tools

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 \beta_g^s, for each sample s, and dispersion parameters \phi_g using edgeR's estimateDisp function with default parameters. Thus, the reference count data is modeled as NB distributed:

Y_{gc} \sim NB(\mu_{gc}, \phi_g)

for gene g and cell c, where the mean \mu_{gc} = \exp(\beta_{g}^{s(c)}) \cdot \lambda_c. Here, \beta_{g}^{s(c)} is the relative abundance of gene g in sample s(c), \lambda_c is the library size (total number of counts), and \phi_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

# estimate simulation parameters
data(example_sce)
ref <- prepSim(example_sce)

# tabulate number of genes/cells before vs. after
ns <- cbind(
  before = dim(example_sce), 
  after = dim(ref)) 
rownames(ns) <- c("#genes", "#cells")
ns

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

HelenaLC/ddSingleCell documentation built on Oct. 14, 2024, 2:08 p.m.