BASiCS_Sim {BASiCS} | R Documentation |
BASiCS_Sim
creates a simulated dataset from the model
implemented in BASiCS.
BASiCS_Sim(Mu, Mu_spikes = NULL, Delta, Phi = NULL, S, Theta, BatchInfo = NULL)
Mu |
Gene-specific mean expression parameters μ_i for all
biological genes (vector of length |
Mu_spikes |
μ_i for all technical genes defined as true
input molecules (vector of length |
Delta |
Gene-specific biological over-dispersion parameters
δ_i, biological genes only
(vector of length |
Phi |
Cell-specific mRNA content normalising parameters φ_j
(vector of length |
S |
Cell-specific technical normalising parameters s_j
(vector of length |
Theta |
Technical variability parameter θ (must be positive).
|
BatchInfo |
Vector detailing which batch each cell should be simulated
from. If spike-ins are not in use, the number of unique values contained in
|
An object of class SingleCellExperiment
,
including synthetic data generated by the model implemented in BASiCS.
Catalina A. Vallejos cnvallej@uc.cl, Nils Eling
Vallejos, Marioni and Richardson (2015). PLoS Computational Biology.
# Simulated parameter values for 10 genes # (7 biogical and 3 spike-in) measured in 5 cells Mu <- c(8.36, 10.65, 4.88, 6.29, 21.72, 12.93, 30.19) Mu_spikes <- c(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 with spike-ins, single batch Data <- BASiCS_Sim(Mu, Mu_spikes, Delta, Phi, S, Theta) head(SingleCellExperiment::counts(Data)) dim(SingleCellExperiment::counts(Data)) altExp(Data) rowData(altExp(Data)) # Data with spike-ins, multiple batches BatchInfo <- c(1,1,1,2,2) Theta2 <- rep(Theta, times = 2) Data <- BASiCS_Sim(Mu, Mu_spikes, Delta, Phi, S, Theta2, BatchInfo) # Data without spike-ins, multiple batches Data <- BASiCS_Sim(Mu, Mu_spikes = NULL, Delta, Phi = NULL, S, Theta2, BatchInfo)