Get ICP cell cluster probability table(s)
Usage
GetCellClusterProbability.SingleCellExperiment(
object,
icp.run,
icp.round,
concatenate
)
# S4 method for class 'SingleCellExperiment'
GetCellClusterProbability(
object,
icp.run = NULL,
icp.round = NULL,
concatenate = TRUE
)Arguments
- object
An object of
SingleCellExperimentclass with ICP cell cluster probability tables saved inmetadata(object)$coralysis$joint.probability. After runningRunParallelDivisiveICP.- icp.run
ICP run(s) to retrieve from
metadata(object)$coralysis$joint.probability. By defaultNULL, i.e., all are retrieved. Specify a numeric vector to retrieve a specific set of tables.- icp.round
ICP round(s) to retrieve from
metadata(object)$coralysis$joint.probability. By defaultNULL, i.e., all are retrieved.- concatenate
Concatenate list of ICP cell cluster probability tables retrieved. By default
TRUE, i.e., the list of ICP cell cluster probability tables is concatenated.
Examples
# Import package
suppressPackageStartupMessages(library("SingleCellExperiment"))
# Create toy SCE data
batches <- c("b1", "b2")
set.seed(239)
batch <- sample(x = batches, size = nrow(iris), replace = TRUE)
sce <- SingleCellExperiment(assays = list(logcounts = t(iris[,1:4])),
colData = DataFrame("Species" = iris$Species,
"Batch" = batch))
colnames(sce) <- paste0("samp", 1:ncol(sce))
# Prepare SCE object for analysis
sce <- PrepareData(sce)
#> Converting object of `matrix` class into `dgCMatrix`. Please note that Coralysis has been designed to work with sparse data, i.e. data with a high proportion of zero values! Dense data will likely increase run time and memory usage drastically!
#> 4/4 features remain after filtering features with only zero values.
# Multi-level integration (just for highlighting purposes; use default parameters)
set.seed(123)
sce <- RunParallelDivisiveICP(object = sce, batch.label = "Batch",
k = 2, L = 25, C = 1, train.k.nn = 10,
train.k.nn.prop = NULL, use.cluster.seed = FALSE,
build.train.set = FALSE, ari.cutoff = 0.1,
threads = 2)
#>
#> Initializing divisive ICP clustering...
#>
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#>
#> Divisive ICP clustering completed successfully.
#>
#> Predicting cell cluster probabilities using ICP models...
#> Prediction of cell cluster probabilities completed successfully.
#>
#> Multi-level integration completed successfully.
# Get cluster probability for all ICP runs
probs <- GetCellClusterProbability(object = sce, icp.round = 1, concatenate = TRUE)
probs[1:10, 1:5]
#> 1 2 1 2 1
#> samp1 0.9643473 0.03565274 0.9638023 0.03619770 0.03965366
#> samp2 0.9264623 0.07353774 0.9255917 0.07440832 0.08002244
#> samp3 0.9519228 0.04807721 0.9512637 0.04873629 0.05290241
#> samp4 0.9268923 0.07310768 0.9260116 0.07398839 0.07971891
#> samp5 0.9692441 0.03075587 0.9687533 0.03124672 0.03435248
#> samp6 0.9698052 0.03019485 0.9693037 0.03069632 0.03394382
#> samp7 0.9587039 0.04129606 0.9581008 0.04189923 0.04573415
#> samp8 0.9524988 0.04750117 0.9518274 0.04817258 0.05249006
#> samp9 0.9153487 0.08465131 0.9144061 0.08559393 0.09170199
#> samp10 0.9268923 0.07310768 0.9260116 0.07398839 0.07971891