View source: R/BASiCS_DivideAndConquer.R
BASiCS_DivideAndConquer | 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"),
RunName,
StoreChains,
StoreDir,
Start,
...
)
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 |
RunName , StoreChains , StoreDir , Start |
Unused. If used when calling this function, they are likely to result in undefined behaviour. |
... |
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
)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.