Generate bar error plots

Generate bar error plots by cell type (CellType) or by number of different cell types (nCellTypes) on test mixed transcriptional profiles.

Usage

barErrorPlot(
  object,
  error = "MSE",
  by = "CellType",
  dispersion = "se",
  filter.sc = TRUE,
  title = NULL,
  angle = NULL,
  theme = NULL
)

Arguments

object: SpatialDDLS object with trained.model slot containing metrics in the test.deconv.metrics slot of a DeconvDLModel object.
error: 'MAE' or 'MSE'.
by: Variable used to show errors. Available options are: 'nCellTypes', 'CellType'.
dispersion: Standard error ('se') or standard deviation ('sd'). The former by default.
filter.sc: Boolean indicating whether single-cell profiles are filtered out and only correlation of results associated with mixed transcriptional profiles are shown (TRUE by default).
title: Title of the plot.
angle: Angle of ticks.
theme: ggplot2 theme.

Value

A ggplot object.

Examples

# \donttest{
set.seed(123)
sce <- SingleCellExperiment::SingleCellExperiment(
  assays = list(
    counts = matrix(
      rpois(30, lambda = 5), nrow = 15, ncol = 20,
      dimnames = list(paste0("Gene", seq(15)), paste0("RHC", seq(20)))
    )
  ),
  colData = data.frame(
    Cell_ID = paste0("RHC", seq(20)),
    Cell_Type = sample(x = paste0("CellType", seq(6)), size = 20,
                       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
#> 'as(<dgCMatrix>, "dgTMatrix")' is deprecated.
#> Use 'as(., "TsparseMatrix")' instead.
#> See help("Deprecated") and help("Matrix-deprecated").
#>       - 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 = 100,
  train.freq.cells = 2/3,
  train.freq.spots = 2/3,
  verbose = TRUE
)
#> 
#> === The number of mixed profiles that will be generated is equal to 100
#> 
#> === Training set cells by type:
#>     - CellType1: 3
#>     - CellType2: 1
#>     - CellType3: 3
#>     - CellType4: 2
#>     - CellType5: 3
#>     - CellType6: 2
#> === Test set cells by type:
#>     - CellType1: 1
#>     - CellType2: 1
#>     - CellType3: 1
#>     - CellType4: 1
#>     - CellType5: 1
#>     - CellType6: 1
#> === Probability matrix for training data:
#>     - Mixed spots: 67
#>     - Cell types: 6
#> === Probability matrix for test data:
#>     - Mixed spots: 33
#>     - Cell types: 6
#> DONE
SDDLS <- simMixedProfiles(SDDLS)
#> === Setting parallel environment to 1 thread(s)
#> 
#> === Generating train mixed profiles:
#> 
#> === Generating test mixed profiles:
#> 
#> DONE
# training of DDLS model
SDDLS <- trainDeconvModel(
  object = SDDLS,
  batch.size = 10,
  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, 6)                  1206       
#> _____________________________________________________________________
#> BatchNormalization3 (BatchNorm (None, 6)                  24         
#> _____________________________________________________________________
#> ActivationSoftmax (Activation) (None, 6)                  0          
#> =====================================================================
#> Total params: 46,230
#> Trainable params: 45,418
#> Non-trainable params: 812
#> _____________________________________________________________________
#> 
#> === Training DNN with 67 samples:
#> 
#> === Evaluating DNN in test data (33 samples)
#>    - loss: 1.6731
#>    - accuracy: 0.1515
#>    - mean_absolute_error: 0.2434
#>    - categorical_accuracy: 0.1515
#> 
#> === Generating prediction results using test data
#> DONE
# evaluation using test data
SDDLS <- calculateEvalMetrics(object = SDDLS)
# bar error plots
barErrorPlot(
  object = SDDLS,
  error = "MSE",
  by = "CellType"
)

barErrorPlot(
  object = SDDLS,
  error = "MAE",
  by = "nCellTypes"
)

# }

Usage

Arguments

Value

See also

Examples