Description Usage Arguments Value Author(s) See Also Examples
Simulating time series single cell RNA-seq data
1 2 3 | sim.rnaseq.ts(N = 2000, M = 500, ls, lambda.dropout = 0.25,
alpha0 = 0.1, library.size = c(1000, 1e+05), n.lineage = 5,
type = "sequential", n.time.points = 5, ...)
|
N |
number of genes |
M |
number of cells |
ls |
the prototype landscape (default: a "plate" with 100 circles and the number of prototypes per circle of 10) |
alpha0 |
the argument for a Dirichlet distribution for sampling metagene basis (default: 0.1) |
library.size |
total number of reads per cell (default: c(1e3, 1e5)) |
n.lineage |
number of simulated lineages (default: 5) |
type |
the type of differentiation models (default: 'sequential') |
n.time.points |
the number of simulated time points (default: 5) |
... |
additional arguments |
lambda |
the argument of a exponential decay model for introducing the dropout noise (default: 0.25) |
A SummarizedExperiment object of simulated temporal scRNA-seq data
Wuming Gong, gongx030@umn.edu
1 2 3 4 | # simulate a simple temporal scRNA-seq data with 2,000 genes, 500 cells and five different lineages.
# The single cell data are sampled across five time points following a sequentail differentiation model.
set.seed(122)
sim <- sim.rnaseq.ts(N = 2000, M = 500, n.lineage = 5, n.time.points = 5)
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