plotHeatmapGradsAgg {SpatialDDLS}

Plot a heatmap of gradients of classes / loss function wtih respect to the input

Description

Plot a heatmap showing the top positive and negative gene average gradients per cell type.

Usage

plotHeatmapGradsAgg(
  object,
  method = "class",
  top.n.genes = 15,
  scale.gradients = TRUE
)

Arguments

object

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

method

Method to calculate gradients with respect to input features. It can be 'class' (gradients of predicted classes w.r.t. input features) or 'loss' (gradients of loss w.r.t. input features) ('class' by default).

top.n.genes

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

scale.gradients

Whether to calculate feature-wise z-scores of gradients (TRUE by default).

Value

A list of Heatmap-class objects, one for top positive and another one for top negative gradients.

See Also

interGradientsDL trainDeconvModel

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(
  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)
plotHeatmapGradsAgg(SDDLS, top.n.genes = 2)