runBayesSpaceClustering.RdA wrapper around the BayesSpace clustering pipeline introduced by Zhao et al. 2021.
runBayesSpaceClustering(
object,
name = "bayes_space",
n.Pcs = 15,
n.HVGs = 2000,
skip.PCA = FALSE,
log.normalize = TRUE,
assay.type = "logcounts",
BSPARAM = BiocSingular::ExactParam(),
qs = 3:10,
burn.in = c(100, 1000),
nrep = c(1000, 50000),
use.dimred = "PCA",
d = 15,
init.method = "mclust",
model = "t",
gamma = 3,
mu0 = NULL,
lambda0 = NULL,
alpha = 1,
beta = 0.01,
save.chain = FALSE,
chain.fname = NULL,
prefix = "",
return_model = TRUE,
empty_remove = FALSE,
overwrite = FALSE,
assign_sce = NULL,
assign_envir = .GlobalEnv,
seed = NULL,
verbose = NULL,
...
)An object of class spata2.
Character value. The name the cluster variable has in
the feature data of the SPATA2 object. Defaults to bayes_space.
Number of highly variable genes to run PCA upon.
Skip PCA (if dimensionality reduction was previously computed.)
Whether to log-normalize the input data with scater. May be omitted if log-normalization previously computed.
Name of assay in sce containing normalized counts.
Leave as "logcounts" unless you explicitly pre-computed a different
normalization and added it to sce under another assay. Note that we
do not recommend running BayesSpace on PCs computed from raw counts.
A BiocSingularParam object specifying which
algorithm should be used to perform the PCA. By default, an exact PCA is
performed, as current spatial datasets are generally small (<10,000 spots).
To perform a faster approximate PCA, please specify
FastAutoParam() and set a random seed to ensure
reproducibility.
The values of q to evaluate. If qs is only one value exactly
that is given to q of BayesSpace::spatialCluster(). Else the optimal
q from all provided values is identified using BayesSpace::qTune().
Integers specifying the range of repetitions to compute.
Name of a reduced dimensionality result in
reducedDims(sce). If provided, cluster on these features directly.
Number of top principal components to use when clustering.
If init is not provided, cluster the top d
PCs with this method to obtain initial cluster assignments.
Error model. ('normal' or 't')
Smoothing parameter. Defaults to 2 for platform="ST" and
3 for platform="Visium". (Values in range of 1-3 seem to work well.)
Prior mean hyperparameter for mu. If not provided, mu0 is set to the mean of PCs over all spots.
Prior precision hyperparam for mu. If not provided, lambda0 is set to a diagonal matrix \(0.01 I\).
Hyperparameter for Wishart distributed precision lambda.
Hyperparameter for Wishart distributed precision lambda.
If true, save the MCMC chain to an HDF5 file.
File path for saved chain. Tempfile used if not provided.
Character value. Prefix of the cluster groups.
Logical value. If TRUE, name overwrites features
in feature data of the SPATA2 object.
Character value or NULL. If character, specifies the
name under which the bayes space output (object of class SingleCellExperiment)
is assigned to the global environment. This makes the whole output
of the bayes space pipeline available instead of only adding the clustering
output as a grouping variable to the SPATA2 object.
Logical. If set to TRUE informative messages regarding the computational progress will be printed.
(Warning messages will always be printed.)
Additional arguments given to BayesSpace::spatialCluster(). Exception:
sce, q are specified within the function.
Numeric value or FALSE. If numeric, it forces the number
of output clusters with input value. If FALSE, the optimal number
of clusters is chosen for q determined by the elbow point of BayesSpace::qTune().
An updated SPATA2 object.
This function is a wrapper around readVisium(),
spatialPreprocess(), qTune() and spatialCluster()
of the BayesSpace package. The results are stored in form of a grouping
variable in the feature data.frame of the returned SPATA2 object.