Generate Bland-Altman agreement plots between predicted and expected cell type proportions of test data

Generate Bland-Altman agreement plots between predicted and expected cell type proportions from test data. The Bland-Altman agreement plots can be shown all mixed or split by either cell type (CellType) or the number of cell types present in spots (nCellTypes). See the facet.by argument and examples for more information.

Usage

blandAltmanLehPlot(
  object,
  colors,
  color.by = "CellType",
  facet.by = NULL,
  log.2 = FALSE,
  filter.sc = TRUE,
  density = TRUE,
  color.density = "darkblue",
  size.point = 0.05,
  alpha.point = 1,
  ncol = NULL,
  nrow = NULL,
  title = NULL,
  theme = NULL,
  ...
)

Arguments

object: SpatialDDLS object with trained.model slot containing metrics in the test.deconv.metrics slot of a DeconvDLModel object.
colors: Vector of colors to be used.
color.by: Variable used to color data. Options are nCellTypes and CellType.
facet.by: Variable used to show the data in different panels. If NULL, the plot is not split into different panels. Options are nCellTypes (by number of different cell types) and CellType (by cell type).
log.2: Whether to show the Bland-Altman agreement plot in log2 space (FALSE by default).
filter.sc: Boolean indicating whether single-cell profiles are filtered out and only correlations of results associated with mixed spot profiles are shown (TRUE by default).
density: Boolean indicating whether density lines should be shown (TRUE by default).
color.density: Color of density lines if the density argument is TRUE.
size.point: Size of the points (0.1 by default).
alpha.point: Alpha of the points (0.1 by default).
ncol: Number of columns if facet.by is used.
nrow: Number of rows if facet.by is used.
title: Title of the plot.
theme: ggplot2 theme.
...: Additional argument for the facet_wrap function of ggplot2 if facet.by is not NULL.

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
#>       - 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: 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: 34
#>     - Cell types: 6
#> === Probability matrix for test data:
#>     - Mixed spots: 16
#>     - 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 = 15,
  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 34 samples:
#> 
#> === Evaluating DNN in test data (16 samples)
#>    - loss: 1.807
#>    - accuracy: 0.0625
#>    - mean_absolute_error: 0.2502
#>    - categorical_accuracy: 0.0625
#> 
#> === Generating prediction results using test data
#> DONE
# evaluation using test data
SDDLS <- calculateEvalMetrics(object = SDDLS)
# Bland-Altman plot by cell type
blandAltmanLehPlot(
  object = SDDLS,
  facet.by = "CellType",
  color.by = "CellType"
)
#> Warning: `stat_contour()`: Zero contours were generated
#> Warning: no non-missing arguments to min; returning Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: `stat_contour()`: Zero contours were generated
#> Warning: no non-missing arguments to min; returning Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: `stat_contour()`: Zero contours were generated
#> Warning: no non-missing arguments to min; returning Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: `stat_contour()`: Zero contours were generated
#> Warning: no non-missing arguments to min; returning Inf
#> Warning: no non-missing arguments to max; returning -Inf

# Bland-Altman plot of all samples mixed
blandAltmanLehPlot(
  object = SDDLS,
  facet.by = NULL,
  color.by = "CellType",
  alpha.point = 0.3,
  log2 = TRUE
)

# }

Generate Bland-Altman agreement plots between predicted and expected cell type proportions of test data

Usage

Arguments

Value

See also

Examples