BASiCS_DivideAndConquer {BASiCS} | R Documentation |
Performs MCMC inference on batches of data. Data
is divided
into NSubsets
batches, and BASiCS_MCMC
is run on each
batch separately.
BASiCS_DivideAndConquer( Data, NSubsets = 5, SubsetBy = c("cell", "gene"), Alpha = 0.05, WithSpikes, Regression, BPPARAM = BiocParallel::bpparam(), PriorParam = BASiCS_PriorParam(Data, PriorMu = "EmpiricalBayes"), ... )
Data |
SingleCellExperiemnt object |
NSubsets |
The number of batches to create and perform MCMC inference with. |
SubsetBy |
A character value specifying whether batches should consist
of a subset of the cells in |
Alpha |
A numeric value specifying the statistical significance level used to determine whether the average library size or average count are significantly different between batches. |
WithSpikes, Regression, PriorParam |
See |
BPPARAM |
A |
... |
Passed to |
Subsets are chosen such that the average library size (when partitioning
by cells) or average count (when partitioning by genes) is not significantly
different between batches, at a significance level Alpha
.
A list of BASiCS_Chain objects.
Simple, Scalable and Accurate Posterior Interval Estimation Cheng Li and Sanvesh Srivastava and David B. Dunson arXiv (2016)
bp <- BiocParallel::SnowParam() Data <- BASiCS_MockSCE() BASiCS_DivideAndConquer( Data, NSubsets = 2, SubsetBy = "gene", N = 8, Thin = 2, Burn = 4, WithSpikes = TRUE, Regression = TRUE, BPPARAM = bp )