sim.rnaseq.ts: sim.rnaseq.ts

Description Usage Arguments Value Author(s) See Also Examples

Description

Simulating time series single cell RNA-seq data

Usage

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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, ...)

Arguments

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)

Value

A SummarizedExperiment object of simulated temporal scRNA-seq data

Author(s)

Wuming Gong, gongx030@umn.edu

See Also

landscape

Examples

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# 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)

gongx030/tcm documentation built on June 4, 2019, 7:26 p.m.