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

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

# \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
)
#> === Spatial transcriptomics data not provided
#> === Processing single-cell data
#>       - Filtering features:
#>          - Selected features: 15
#>          - Discarded features: 0
#> 
#> === No mitochondrial genes were found by using ^mt- as regrex
#> 
#> === Final number of dimensions for further analyses: 15
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
)
#> 
#> === The number of mixed profiles that will be generated is equal to 50
#> 
#> === Training set cells by type:
#>     - CellType1: 4
#>     - CellType2: 3
#> === Test set cells by type:
#>     - CellType1: 2
#>     - CellType2: 1
#> === Probability matrix for training data:
#>     - Mixed spots: 34
#>     - Cell types: 2
#> === Probability matrix for test data:
#>     - Mixed spots: 16
#>     - Cell types: 2
#> DONE
SDDLS <- simMixedProfiles(SDDLS)
#> === Setting parallel environment to 1 thread(s)
#> 
#> === Generating train mixed profiles:
#> 
#> === Generating test mixed profiles:
#> 
#> DONE
SDDLS <- trainDeconvModel(
  object = SDDLS,
  batch.size = 12,
  num.epochs = 5
)
#> === Training and test from stored data
#>     Using only simulated mixed samples
#>     Using only simulated mixed samples
#> Model: "SpatialDDLS"
#> _____________________________________________________________________
#> Layer (type)                   Output Shape               Param #    
#> =====================================================================
#> Dense1 (Dense)                 (None, 200)                3200       
#> _____________________________________________________________________
#> BatchNormalization1 (BatchNorm (None, 200)                800        
#> _____________________________________________________________________
#> Activation1 (Activation)       (None, 200)                0          
#> _____________________________________________________________________
#> Dropout1 (Dropout)             (None, 200)                0          
#> _____________________________________________________________________
#> Dense2 (Dense)                 (None, 200)                40200      
#> _____________________________________________________________________
#> BatchNormalization2 (BatchNorm (None, 200)                800        
#> _____________________________________________________________________
#> Activation2 (Activation)       (None, 200)                0          
#> _____________________________________________________________________
#> Dropout2 (Dropout)             (None, 200)                0          
#> _____________________________________________________________________
#> Dense3 (Dense)                 (None, 2)                  402        
#> _____________________________________________________________________
#> BatchNormalization3 (BatchNorm (None, 2)                  8          
#> _____________________________________________________________________
#> ActivationSoftmax (Activation) (None, 2)                  0          
#> =====================================================================
#> Total params: 45,410
#> Trainable params: 44,606
#> Non-trainable params: 804
#> _____________________________________________________________________
#> 
#> === Training DNN with 34 samples:
#> 
#> === Evaluating DNN in test data (16 samples)
#>    - loss: NaN
#>    - accuracy: 0.5
#>    - mean_absolute_error: NaN
#>    - categorical_accuracy: 0.5
#> 
#> === Generating prediction results using test data
#> DONE
## calculating gradients
SDDLS <- interGradientsDL(SDDLS)
plotHeatmapGradsAgg(SDDLS, top.n.genes = 2)
#> $Absolute

#> 
#> $Positive

#> 
#> $Negative

#> 
# }