Get top genes with largest/smallest gradients per cell type

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)

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.

Value

List of gene names with the top positive and negative gradients 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
)
#> === 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)
listGradients <- topGradientsCellType(SDDLS)
lapply(listGradients, head, n = 5)
#> $CellType1
#> $CellType1$Absolute
#>  [1] "Gene12" "Gene4"  "Gene13" "Gene2"  "Gene11" "Gene14" "Gene8"  "Gene3" 
#>  [9] "Gene5"  "Gene6"  "Gene10" "Gene9"  "Gene7"  "Gene15" "Gene1" 
#> 
#> $CellType1$Positive
#>  [1] "Gene12" "Gene4"  "Gene13" "Gene2"  "Gene11" "Gene14" "Gene8"  "Gene3" 
#>  [9] "Gene5"  "Gene6"  "Gene10" "Gene9"  "Gene7"  "Gene15" "Gene1" 
#> 
#> $CellType1$Negative
#>  [1] "Gene12" "Gene4"  "Gene13" "Gene2"  "Gene11" "Gene14" "Gene8"  "Gene3" 
#>  [9] "Gene5"  "Gene6"  "Gene10" "Gene9"  "Gene7"  "Gene15" "Gene1" 
#> 
#> 
#> $CellType2
#> $CellType2$Absolute
#>  [1] "Gene15" "Gene10" "Gene9"  "Gene7"  "Gene5"  "Gene3"  "Gene8"  "Gene1" 
#>  [9] "Gene6"  "Gene13" "Gene4"  "Gene14" "Gene2"  "Gene12" "Gene11"
#> 
#> $CellType2$Positive
#>  [1] "Gene15" "Gene10" "Gene9"  "Gene7"  "Gene5"  "Gene3"  "Gene8"  "Gene1" 
#>  [9] "Gene6"  "Gene13" "Gene4"  "Gene14" "Gene2"  "Gene12" "Gene11"
#> 
#> $CellType2$Negative
#>  [1] "Gene15" "Gene10" "Gene9"  "Gene7"  "Gene5"  "Gene3"  "Gene8"  "Gene1" 
#>  [9] "Gene6"  "Gene13" "Gene4"  "Gene14" "Gene2"  "Gene12" "Gene11"
#> 
#> 
# }