spatialEnhance: Enhance spot resolution

In edward130603/BayesSpace: Clustering and Resolution Enhancement of Spatial Transcriptomes

Enhance spot resolution

Description

Enhanced clustering of a spatial expression dataset to subspot resolution.

Usage

spatialEnhance(
  sce,
  q,
  platform = c("Visium", "VisiumHD", "ST"),
  use.dimred = "PCA",
  d = 15,
  nsubspots.per.edge = 3,
  init = NULL,
  init.method = c("spatialCluster", "mclust", "kmeans"),
  model = c("t", "normal"),
  nrep = 1e+05,
  gamma = NULL,
  mu0 = NULL,
  lambda0 = NULL,
  alpha = 1,
  beta = 0.01,
  save.chain = FALSE,
  chain.fname = NULL,
  burn.in = 10000,
  thin = 100,
  jitter.scale = 5,
  jitter.prior = 0.3,
  adapt.before = burn.in,
  cores = 1,
  verbose = FALSE
)

coreTune(sce, test.cores = detectCores(), test.times = 1, ...)

adjustClusterLabels(sce, burn.in)

Arguments

sce	A SingleCellExperiment object containing the spatial data.
q	The number of clusters.
platform	Spatial transcriptomic platform. Specify 'Visium' for hex lattice geometry or 'ST' and 'VisiumHD' for square lattice geometry. Specifying this parameter is optional when analyzing SingleCellExperiments processed using readVisium, spatialPreprocess, or spatialCluster, as this information is included in their metadata.
use.dimred	Name of a reduced dimensionality result in reducedDims(sce). If provided, cluster on these features directly.
d	Number of top principal components to use when clustering.
nsubspots.per.edge	Number of subspots per edge of the square. Only valid when platform is 'ST' or 'VisiumHD'.
init	Initial cluster assignments for spots.
init.method	If init is not provided, cluster the top d PCs with this method to obtain initial cluster assignments.
model	Error model. ('normal' or 't')
nrep	The number of MCMC iterations.
gamma	Smoothing parameter. (Values in range of 1-3 seem to work well.)
mu0	Prior mean hyperparameter for mu. If not provided, mu0 is set to the mean of PCs over all spots.
lambda0	Prior precision hyperparam for mu. If not provided, lambda0 is set to a diagonal matrix 0.01 I.
alpha	Hyperparameter for Wishart distributed precision lambda.
beta	Hyperparameter for Wishart distributed precision lambda.
save.chain	If true, save the MCMC chain to an HDF5 file.
chain.fname	File path for saved chain. Tempfile used if not provided.
burn.in	Number of iterations to exclude as burn-in period. The MCMC iterations are currently thinned to every 100; accordingly burn.in is rounded down to the nearest multiple of 100. If a value no larger than 1 is set, it is considered as a percentage. It is always considered as percentage for adjustClusterLabels.
thin	Thinning rate.
jitter.scale	Controls the amount of jittering. Small amounts of jittering are more likely to be accepted but result in exploring the space more slowly. We suggest tuning jitter.scale so that Ychange is on average around 25%-40%. Ychange can be accessed via mcmcChain(). Alternatively, set it to 0 to activate adaptive MCMC.
jitter.prior	Scale factor for the prior variance, parameterized as the proportion (default = 0.3) of the mean variance of the PCs. We suggest making jitter.prior smaller if the jittered values are not expected to vary much from the overall mean of the spot.
adapt.before	Adapting the MCMC chain before the specified number or proportion of iterations (by default equal to burn.in; set to 0 to always adapt). Only valid when jitter.scale is 0.
cores	The number of threads to use. The results are invariate to the value of cores.
verbose	Log progress to stderr.
test.cores	Either a list of, or a maximum number of cores to test. In the latter case, a list of values (power of 2) will be created
test.times	Times to repeat the benchmarking with microbenchmark.
...	Arguments for spatialEnhance (except for cores).

Details

The enhanced SingleCellExperiment has most of the properties of the input SCE - rowData, colData, reducedDims - but does not include expression data in counts or logcounts. To impute enhanced expression vectors, please use [enhanceFeatures()] after running spatialEnhance.

The colData of the enhanced SingleCellExperiment includes the following columns to permit referencing the subspots in spatial context and linking back to the original spots:

• spot.idx: Index of the spot this subspot belongs to (with respect to the input SCE).

• subspot.idx: Index of the subspot within its parent spot.

• spot.row: Array row of the subspot's parent spot.

• spot.col: Array col of the subspot's parent spot.

• array_row: Array row of the subspot. This is the parent spot's row plus an offset based on the subspot's position within the spot.

• array_col: Array col of the subspot. This is the parent spot's col plus an offset based on the subspot's position within the spot.

• pxl_row_in_fullres: Pixel row of the subspot. This is the parent spot's row plus an offset based on the subspot's position within the spot.

• pxl_col_in_fullres: Pixel col of the subspot. This is the parent spot's col plus an offset based on the subspot's position within the spot.

Value

spatialEnhance returns a new SingleCellExperiment object. By default, the assays of this object are empty, and the enhanced resolution PCs are stored as a reduced dimensionality result accessible with reducedDim(sce, 'PCA').

coresTune returns the output of microbenchmark.

adjustClusterLabels adjusts the cluster labels from the MCMC samples via burn.in, the percentage of samples to drop. The MCMC chain must be retained.

See Also

spatialCluster for clustering at the spot level before enhancing, clusterPlot for visualizing the cluster assignments, enhanceFeatures for imputing enhanced expression, and mcmcChain for examining the full MCMC chain associated with the enhanced clustering. .

Examples

set.seed(149)
sce <- exampleSCE()
sce <- spatialCluster(sce, 7, nrep = 100, burn.in = 10)
enhanced <- spatialEnhance(sce, 7, nrep = 100, burn.in = 10)

edward130603/BayesSpace documentation built on Oct. 23, 2024, 7:04 a.m.