spatialPropClustering: Cluster spatial data based on predicted cell proportions

In SpatialDDLS: Deconvolution of Spatial Transcriptomics Data Based on Neural Networks

Cluster spatial data based on predicted cell proportions

Description

Cluster spatial transcriptomics data according to the cell proportions predicted in each spot. It allows to segregate ST data into niches with similar cell composition.

Usage

spatialPropClustering(
  object,
  index.st,
  method = "graph",
  k.nn = 10,
  k.centers = 5,
  verbose = TRUE
)

Arguments

object	SpatialDDLS object with deconvoluted ST datasets.
index.st	Name or index of the dataset/slide already deconvoluted to be clustered. If missing, all datasets already deconvoluted will be clustered.
method	Clustering method. It can be graph (a nearest neighbor graph is created and Louvain algorithm is used to detect communities) or k.means (k-means algorithm is run with the specified number of centers (k.centers parameter)).
k.nn	An integer specifying the number of nearest neighbors to be used during graph construction (10 by default). Only if method == "graph".
k.centers	An integer specifying the number of centers for k-means algorithm (5 by default). Only if method == "k.means".
verbose	Show informative messages during the execution (TRUE by default).

Value

A SpatialDDLS object containing computed clusters as a column in the slot colData of the SpatialExperiment objects.

See Also

plotTrainingHistory deconvSpatialDDLS

Examples

set.seed(123)
sce <- SingleCellExperiment::SingleCellExperiment(
  assays = list(
    counts = matrix(
      rpois(30, lambda = 5), nrow = 15, ncol = 10,
      dimnames = list(paste0("Gene", seq(15)), paste0("RHC", seq(10)))
    )
  ),
  colData = data.frame(
    Cell_ID = paste0("RHC", seq(10)),
    Cell_Type = sample(x = paste0("CellType", seq(2)), size = 10,
                       replace = TRUE)
  ),
  rowData = data.frame(
    Gene_ID = paste0("Gene", seq(15))
  )
)
SDDLS <- createSpatialDDLSobject(
  sc.data = sce,
  sc.cell.ID.column = "Cell_ID",
  sc.gene.ID.column = "Gene_ID",
  sc.filt.genes.cluster = FALSE
) 
SDDLS <- genMixedCellProp(
  SDDLS,
  cell.ID.column = "Cell_ID",
  cell.type.column = "Cell_Type",
  num.sim.spots = 50,
  train.freq.cells = 2/3,
  train.freq.spots = 2/3,
  verbose = TRUE
) 
SDDLS <- simMixedProfiles(SDDLS) 
SDDLS <- trainDeconvModel(
  SDDLS,
  batch.size = 12,
  num.epochs = 5
) 
# simulating spatial data
ngenes <- sample(3:40, size = 1)
ncells <- sample(10:40, size = 1)
counts <- matrix(
  rpois(ngenes * ncells, lambda = 5), ncol = ncells,
  dimnames = list(paste0("Gene", seq(ngenes)), paste0("Spot", seq(ncells)))
)
coordinates <- matrix(
  rep(c(1, 2), ncells), ncol = 2
)
st <- SpatialExperiment::SpatialExperiment(
  assays = list(counts = as.matrix(counts)),
  rowData = data.frame(Gene_ID = paste0("Gene", seq(ngenes))),
  colData = data.frame(Cell_ID = paste0("Spot", seq(ncells))),
  spatialCoords = coordinates
)
SDDLS <- loadSTProfiles(
  object = SDDLS,
  st.data = st,
  st.spot.ID.column = "Cell_ID",
  st.gene.ID.column = "Gene_ID"
)
SDDLS <- deconvSpatialDDLS(
  SDDLS,
  index.st = 1
) 
SDDLS <- spatialPropClustering(SDDLS, index.st = 1, k.nn = 5)

  

SpatialDDLS documentation built on Oct. 31, 2024, 5:07 p.m.