BASiCS_Sim: Simulates expression counts according to the model implemented in BASiCS

Description

BASiCS_Sim creates a simulated dataset from the model implemented in BASiCS. This function is used in order to illustrate the performance of the BASiCS library.

Usage

BASiCS_Sim(mu, delta, phi, s, theta)

Arguments

Gene-specific expression levels \(\mu[i]\), defined as true input molecules in case of technical genes (vector of length q, technical genes located at the end of the vector, all elements must be positive numbers)

delta

Gene-specific biological cell-to-cell heterogeneity hyper-parameters \(\delta[i]\), biological genes only (vector of length q.bio, all elements must be positive numbers)

phi

Cell-specific mRNA content normalising constants \(\phi[j]\) (vector of length n, all elements must be positive numbers and the sum of its elements must be equal to n)

Cell-specific capture efficiency (or amplification biases if not using UMI based counts) normalising constants \(s[j]\) (vector of length n, all elements must be positive numbers)

theta

Technical variability hyper-parameter \(\theta\) (must be positive)

Value

An object of class BASiCS_Data-class, simulated from the model implemented in BASiCS.

References

Vallejos, Marioni and Richardson (2015). Bayesian Analysis of Single-Cell Sequencing data.

Examples

Run this code

# NOT RUN {
# Simulated parameter values for 10 genes
# (7 biogical and 5 spike-in) measured in 5 cells
Mu =  c(8.36, 10.65, 4.88, 6.29, 21.72, 12.93, 30.19, 1010.72, 7.90, 31.59)
Delta = c(1.29, 0.88, 1.51, 1.49, 0.54, 0.40, 0.85)
Phi = c(1.00, 1.06, 1.09, 1.05, 0.80)
S = c(0.38, 0.40, 0.38, 0.39, 0.34)
Theta = 0.39

Data = BASiCS_Sim(Mu, Delta, Phi, S, Theta)
head(counts(Data))
dim(counts(Data, type="biological"))
dim(counts(Data, type="technical"))
displayTechIndicator(Data)
displaySpikeInput(Data)
# }