SpatialDDLS (version 1.0.1)

topGradientsCellType: Get top genes with largest/smallest gradients per cell type

Description

Retrieve feature names with the largest/smallest gradients per cell type. These genes can be used to visualize their spatial expression in the ST data (plotGeneSpatial function) or to plot the calculated gradients as a heatmap (plotGradHeatmap function).

Usage

topGradientsCellType(object, method = "class", top.n.genes = 15)

Value

List of gene names with the top positive and negative gradients per cell type.

Arguments

object

SpatialDDLS object with a DeconvDLModel object containing gradients in the interpret.gradients slot.

method

Method gradients were calculated by. It can be either 'class' (gradients of predicted classes w.r.t. inputs) or 'loss' (gradients of loss w.r.t. input features).

top.n.genes

Top n genes (positive and negative) taken per cell type.

See Also

interGradientsDL trainDeconvModel

Examples

# \donttest{
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(
  object = 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(
  object = SDDLS,
  batch.size = 12,
  num.epochs = 5
)
## calculating gradients
SDDLS <- interGradientsDL(SDDLS)
listGradients <- topGradientsCellType(SDDLS)
lapply(listGradients, head, n = 5)
# }