Type:	Package
Title:	Deconvolution of Spatial Transcriptomics Data Based on Neural Networks
Version:	1.0.3
Maintainer:	Diego Mañanes <dmananesc@cnic.es>
Description:	Deconvolution of spatial transcriptomics data based on neural networks and single-cell RNA-seq data. SpatialDDLS implements a workflow to create neural network models able to make accurate estimates of cell composition of spots from spatial transcriptomics data using deep learning and the meaningful information provided by single-cell RNA-seq data. See Torroja and Sanchez-Cabo (2019) <doi:10.3389/fgene.2019.00978> and Mañanes et al. (2024) <doi:10.1093/bioinformatics/btae072> to get an overview of the method and see some examples of its performance.
License:	GPL-3
URL:	https://diegommcc.github.io/SpatialDDLS/, https://github.com/diegommcc/SpatialDDLS
BugReports:	https://github.com/diegommcc/SpatialDDLS/issues
Encoding:	UTF-8
Depends:	R (≥ 4.0.0)
Imports:	rlang, grr, Matrix, methods, SpatialExperiment, SingleCellExperiment, SummarizedExperiment, zinbwave, stats, pbapply, S4Vectors, dplyr, reshape2, gtools, reticulate, keras, tensorflow, FNN, ggplot2, ggpubr, scran, scuttle
Suggests:	knitr, rmarkdown, BiocParallel, rhdf5, DelayedArray, DelayedMatrixStats, HDF5Array, testthat, ComplexHeatmap, grid, bluster, lsa, irlba
SystemRequirements:	Python (>= 2.7.0), TensorFlow (https://www.tensorflow.org/)
RoxygenNote:	7.3.2
Collate:	'AllClasses.R' 'AllGenerics.R' 'SpatialDDLS.R' 'dnnModel.R' 'evalMetrics.R' 'interGradientsDL.R' 'loadData.R' 'plotSpatialCoor.R' 'simMixedSpots.R' 'simSingleCell.R' 'spatialClustering.R' 'utils.R'
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2024-10-31 08:55:20 UTC; dmananes
Author:	Diego Mañanes [aut, cre], Carlos Torroja [aut], Fatima Sanchez-Cabo [aut]
Repository:	CRAN
Date/Publication:	2024-10-31 09:40:06 UTC

SpatialDDLS: Deconvolution of Spatial Transcriptomics Data Based on Neural Networks

Description

Deconvolution of spatial transcriptomics data based on neural networks and single-cell RNA-seq data. SpatialDDLS implements a workflow to create neural network models able to make accurate estimates of cell composition of spots from spatial transcriptomics data using deep learning and the meaningful information provided by single-cell RNA-seq data. See Torroja and Sanchez-Cabo (2019) doi:10.3389/fgene.2019.00978 and Mañanes et al. (2024) doi:10.1093/bioinformatics/btae072 to get an overview of the method and see some examples of its performance.

Author(s)

Maintainer: Diego Mañanes dmananesc@cnic.es (ORCID)

Authors:

• Carlos Torroja ctorroja@cnic.es (ORCID)

• Fatima Sanchez-Cabo fscabo@cnic.es (ORCID)

See Also

Useful links:

• https://diegommcc.github.io/SpatialDDLS/

• https://github.com/diegommcc/SpatialDDLS

• Report bugs at https://github.com/diegommcc/SpatialDDLS/issues

The DeconvDLModel Class

Description

The DeconvDLModel object stores all the information related to deep neural network models. It consists of the trained model, the training history, and the predictions on test data. After running calculateEvalMetrics, it is possible to find the performance evaluation of the model on test data (see ?calculateEvalMetrics for details).

Details

The steps related to Deep Learning are carried out using the keras and tensorflow packages, which use the R6 classes system. If you want to save the DeconvDLModel object as an RDS file, SpatialDDLS provides a saveRDS generic function that transforms the R6 object containing the trained model into a native valid R object. Specifically, the model is converted into a list with the architecture of the network and the weights learned during training, which is the minimum information needed to use the model as a predictor. If you want to keep the optimizer state, see ?saveTrainedModelAsH5. If you want to store either the DeconvDLModel or the SpatialDDLS objects on disk as RDA files, see ?preparingToSave.

Slots

model

Trained deep neural network. This slot can contain an R6 keras.engine.sequential.Sequential object or a list with two elements: the architecture of the model and the resulting weights after training.

training.history

List with the evolution of the selected metrics during training.

test.metrics

Performance of the model on test data.

test.pred

Predicted cell type proportions on test data.

cell.types

Vector with cell types considered by the model.

features

Vector with features (genes) considered by the model. These features will be used for subsequent predictions.

test.deconv.metrics

Performance of the model on test data by cell type. This slot is generated by the calculateEvalMetrics function (see ?calculateEvalMetrics for more details).

interpret.gradients

Gradients for interpretation. SpatialDDLS provides some functions to better understand prediction made by the model (see ?interGradientsDL for more details). This slot is a list of either one or two elements: gradients of either the loss function or the predicted class with respect to the input variables using pure (only one cell type) mixed transcriptional profiles. These gradients can be interpreted as to what extent the model is using these variables to predict each cell type proportions.

The PropCellTypes Class

Description

The PropCellTypes class is a data storage class which contains information related to cell type composition matrices used to simulate mixed transcriptional profiles. This matrix is stored in the prob.matrix slot while the other slots contain additional information generated during the process and required for subsequent steps.

Details

See ?genMixedCellProp function for information about how cell type composition matrices are generated. Plots of cell type proportion distributions can be accessed using the showProbPlot function (see ?showProbPlot for more details).

Slots

prob.matrix

Matrix of cell type proportions to simulate mixed transcriptional profiles.

cell.names

Matrix containing cells used to generate the simulated mixed transcriptional profiles.

set.list

List of cells sorted by cell type.

set

Vector containing cell names present in the object.

method

Vector indicating the method by which cell type proportions were generated.

plots

Plots showing cell type proportion distributions. See ?showProbPlot for more details.

type.data

Character indicating the type of data contained: 'train' or 'test'.

SpatialDDLS: an R package to deconvolute spatial transcriptomics data using deep neural networks

Description

SpatialDDLS is an R package that provides a neural network-based solution for cell type deconvolution of spatial transcriptomics data. The package takes advantage of single-cell RNA sequencing (scRNA-seq) data to simulate mixed transcriptional profiles with known cell composition and train fully-connected neural networks to predict the cell type composition of spatial transcriptomics spots. The resulting trained models can be applied to new spatial transcriptomics data to predict cell type proportions, allowing for more accurate cell type identification and characterization of spatially-resolved transcriptomic data. Finally, predictions are forced to keep spatial consistency through a process we refer to as spatial regularization. Overall, SpatialDDLS is a powerful tool for cell type deconvolution in spatial transcriptomics data, providing a reliable, fast and flexible solution for researchers in the field. See Mañanes et al. (2024) (doi:10.1093/bioinformatics/btae072) and some examples (https://diegommcc.github.io/SpatialDDLS/) for more details.

The SpatialDDLS Class

Description

The SpatialDDLS object is the core of the SpatialDDLS package. This object stores different intermediate data needed for the construction of new deconvolution models, the spatial transcriptomics profiles to be deconvoluted, and the predicted cell type proportions.

Details

This object uses other classes to store different types of data generated during the workflow:

• SingleCellExperiment class for single-cell RNA-Seq data storage, using sparse matrix from the Matrix package (dgCMatrix class) or HDF5Array class in case of using HDF5 files as back-end (see below for more information).

• SpatialExperiment class for spatial transcriptomics data storage.

• zinbModel class with estimated parameters for the simulation of new single-cell profiles.

• SummarizedExperiment class for simulated mixed transcriptional profiles storage.

• PropCellTypes class for composition cell type matrices. See ?PropCellTypes for details.

• DeconvDLModel class to store information related to deep neural network models. See ?DeconvDLModel for details.

In order to provide a way to work with large amounts of data in RAM-constrained machines, we provide the possibility of using HDF5 files as back-end to store count matrices of both real and simulated single-cell profiles by using the HDF5Array and DelayedArray classes from the homonymous packages.

Slots

single.cell.real

Real single-cell data stored in a SingleCellExperiment object. The count matrix is stored either as dgCMatrix or HDF5Array objects.

spatial.experiments

List of SpatialExperiment objects to be deconvoluted.

zinb.params

zinbModel object with estimated parameters for the simulation of new single-cell expression profiles.

single.cell.simul

Simulated single-cell expression profiles using the ZINB-WaVE model.

prob.cell.types

PropCellTypes class with cell composition matrices built for the simulation of mixed transcriptional profiles with known cell composition.

mixed.profiles

List of simulated train and test mixed transcriptional profiles. Each entry is a SummarizedExperiment object. Count matrices can be stored as HDF5Array objects using HDF5 files as back-end in case of RAM limitations.

trained.model

DeconvDLModel object with information related to the deconvolution model. See ?DeconvDLModel for more details.

deconv.spots

Deconvolution results. It consists of a list where each element corresponds to the results for each SpatialExperiment object contained in the spatial.experiments slot.

project

Name of the project.

version

Version of SpatialDDLS this object was built under.

The Class ZinbParametersModel

Description

The ZinbParametersModel class is a wrapper class for the zinbModel class from the zinbwave package.

Details

This wrapper class contains the zinbwave.model slot, which holds a valid zinbModel object.

Slots

zinbwave.model

A valid zinbModel object.

References

Risso, D., Perraudeau, F., Gribkova, S. et al. (2018). A general and flexible method for signal extraction from single-cell RNA-seq data. Nat Commun 9, 284. doi: doi:10.1038/s41467-017-02554-5.

Generate bar error plots

Description

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

Value

A ggplot object.

See Also

calculateEvalMetrics corrExpPredPlot distErrorPlot blandAltmanLehPlot

Examples

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
)
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
)
SDDLS <- simMixedProfiles(SDDLS)
# training of DDLS model
SDDLS <- trainDeconvModel(
  object = SDDLS,
  batch.size = 10,
  num.epochs = 5
)
# 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"
)

Bar plot of deconvoluted cell type proportions

Description

Bar plot of deconvoluted cell type proportions.

Usage

barPlotCellTypes(
  data,
  colors = NULL,
  set = NULL,
  prediction = "Regularized",
  color.line = NA,
  x.label = "Spots",
  rm.x.text = FALSE,
  title = "Results of deconvolution",
  legend.title = "Cell types",
  angle = 90,
  theme = NULL,
  index.st = NULL
)

Arguments

Value

A ggplot object with the provided cell proportions represented as a bar plot.

See Also

deconvSpatialDDLS

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

Description

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

Value

A ggplot object.

See Also

calculateEvalMetrics corrExpPredPlot distErrorPlot barErrorPlot

Examples

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
)
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)
# training of DDLS model
SDDLS <- trainDeconvModel(
  object = SDDLS,
  batch.size = 15,
  num.epochs = 5
)
# evaluation using test data
SDDLS <- calculateEvalMetrics(object = SDDLS)
# Bland-Altman plot by cell type
blandAltmanLehPlot(
  object = SDDLS,
  facet.by = "CellType",
  color.by = "CellType"
)
# Bland-Altman plot of all samples mixed
blandAltmanLehPlot(
  object = SDDLS,
  facet.by = NULL,
  color.by = "CellType",
  alpha.point = 0.3,
  log2 = TRUE
)

Calculate evaluation metrics on test mixed transcriptional profiles

Description

Calculate evaluation metrics on test mixed transcriptional profiles. By default, absolute error (AbsErr), proportional absolute error (ppAbsErr), squared error (SqrErr), and proportional squared error (ppSqrErr) are calculated for each test mixed profile. In addition, each of these metrics is aggregated according to three criteria: cell type (CellType), probability bins in ranges of 0.1 (pBin), and number of different cell types present in the spot (nCellTypes).

Usage

calculateEvalMetrics(object)

Arguments

Value

A SpatialDDLS object with is a DeconvDLModel object. The calculated metrics are stored in the test.deconv.metrics slot of the DeconvDLModel object.

See Also

distErrorPlot corrExpPredPlot blandAltmanLehPlot barErrorPlot

Get and set cell.names slot in a PropCellTypes object

Description

Get and set cell.names slot in a PropCellTypes object

Usage

cell.names(object)

cell.names(object) <- value

Arguments

Get and set cell.types slot in a DeconvDLModel object

Description

Get and set cell.types slot in a DeconvDLModel object

Usage

cell.types(object)

cell.types(object) <- value

Arguments

Generate correlation plots between predicted and expected cell type proportions of test data

Description

Generate correlation plots between predicted and expected cell type proportions of test data. Correlation plots can be shown all mixed or either split by cell type (CellType) or the number of different cell types present in the spots (nCellTypes).

Usage

corrExpPredPlot(
  object,
  colors,
  facet.by = NULL,
  color.by = "CellType",
  corr = "both",
  filter.sc = TRUE,
  pos.x.label = 0.01,
  pos.y.label = 0.95,
  sep.labels = 0.15,
  size.point = 0.1,
  alpha.point = 1,
  ncol = NULL,
  nrow = NULL,
  title = NULL,
  theme = NULL,
  ...
)

Arguments

Value

A ggplot object.

See Also

calculateEvalMetrics distErrorPlot blandAltmanLehPlot barErrorPlot

Examples

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
)
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)
# training of DDLS model
SDDLS <- trainDeconvModel(
  object = SDDLS,
  batch.size = 15,
  num.epochs = 5
)
# evaluation using test data
SDDLS <- calculateEvalMetrics(object = SDDLS)
# correlations by cell type
corrExpPredPlot(
  object = SDDLS,
  facet.by = "CellType",
  color.by = "CellType",
  corr = "both"
)
# correlations of all samples mixed
corrExpPredPlot(
  object = SDDLS,
  facet.by = NULL,
  color.by = "CellType",
  corr = "ccc",
  pos.x.label = 0.2,
  alpha.point = 0.3
)

Create a SpatialDDLS object

Description

Create a SpatialDDLS object by providing single-cell RNA-seq data. Additionally, spatial transcriptomics data contained in SpatialDDLS objects can also be provided. It is recommended to provide both types of data to only use genes shared between both experiments.

Usage

createSpatialDDLSobject(
  sc.data,
  sc.cell.ID.column,
  sc.cell.type.column,
  sc.gene.ID.column,
  st.data,
  st.spot.ID.column,
  st.gene.ID.column,
  filter.mt.genes = "^mt-",
  sc.filt.genes.cluster = TRUE,
  sc.min.mean.counts = 1,
  sc.n.genes.per.cluster = 300,
  top.n.genes = 2000,
  sc.log.FC = TRUE,
  sc.min.counts = 1,
  sc.min.cells = 1,
  st.min.counts = 1,
  st.min.spots = 1,
  st.n.slides = 3,
  shared.genes = TRUE,
  sc.name.dataset.h5 = NULL,
  sc.file.backend = NULL,
  sc.name.dataset.backend = NULL,
  sc.compression.level = NULL,
  sc.chunk.dims = NULL,
  sc.block.processing = FALSE,
  verbose = TRUE,
  project = "SpatialDDLS-Proj"
)

Arguments

Details

Filtering genes

In order to reduce the number of dimensions used for subsequent steps, createSpatialDDLSobject implements different strategies aimed at removing useless genes for deconvolution:

• Filtering at the cell level: genes less expressed than a determined cutoff in N cells are removed. See sc.min.cells/st.min.cells and sc.min.counts/st.min.cells parameters.

• Filtering at the cluster level (only for scRNA-seq data): if sc.filt.genes.cluster == TRUE, createSpatialDDLSobject sets a cutoff of non-zero average counts per cluster (sc.min.mean.counts parameter) and take only the sc.n.genes.per.cluster genes with the highest logFC per cluster. LogFCs are calculated using normalized logCPM of each cluster with respect to the average in the whole dataset). Finally, if the number of remaining genes is greater than top.n.genes, genes are ranked based on variance and the top.n.genes most variable genes are used for downstream analyses.

Single-cell RNA-seq data

Single-cell RNA-seq data can be provided from files (formats allowed: tsv, tsv.gz, mtx (sparse matrix) and hdf5) or a SingleCellExperiment object. Data will be stored in the single.cell.real slot, and must consist of three pieces of information:

• Single-cell counts: genes as rows and cells as columns.

• Cells metadata: annotations (columns) for each cell (rows).

• Genes metadata: annotations (columns) for each gene (rows).

If data are provided from files, single.cell.real argument must be a vector of three elements ordered so that the first file corresponds to the count matrix, the second to the cells metadata, and the last to the genes metadata. On the other hand, if data are provided as a SingleCellExperiment object, it must contain single-cell counts in assay, cells metadata in colData, and genes metadata in rowData. Data must be provided without any transformation (e.g. log-transformation), raw counts are preferred.

Spatial transcriptomics data

It must be a SpatialExperiment object (or a list of them if more than one slide is going to be deconvoluted) containing the same information as the single-cell RNA-seq data: the count matrix, spots metadata, and genes metadata. Please, make sure the gene identifiers used the spatial and single-cell transcriptomics data are consistent.

Value

A SpatialDDLS object with the single-cell RNA-seq data provided loaded into the single.cell.real slot as a SingleCellExperiment object. If spatial transcriptomics data are provided, they will be loaded into the spatial.experiments slot.

See Also

estimateZinbwaveParams genMixedCellProp

Examples

set.seed(123)
sce <- SingleCellExperiment::SingleCellExperiment(
  assays = list(
    counts = matrix(
      rpois(100, lambda = 5), nrow = 40, ncol = 30,
      dimnames = list(paste0("Gene", seq(40)), paste0("RHC", seq(30)))
    )
  ),
  colData = data.frame(
    Cell_ID = paste0("RHC", seq(30)),
    Cell_Type = sample(x = paste0("CellType", seq(4)), size = 30,
                       replace = TRUE)
  ),
  rowData = data.frame(
    Gene_ID = paste0("Gene", seq(40))
  )
)
counts <- matrix(
  rpois(30, lambda = 5), ncol = 6,
  dimnames = list(paste0("Gene", 1:5), paste0("Spot", 1:6))
)
coordinates <- matrix(
  c(1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3), ncol = 2
)
ste <- SpatialExperiment::SpatialExperiment(
  assays = list(counts = as.matrix(counts)),
  rowData = data.frame(Gene_ID = paste0("Gene", 1:5)),
  colData = data.frame(Cell_ID = paste0("Spot", 1:6)),
  spatialCoords = coordinates
)

SDDLS <- createSpatialDDLSobject(
  sc.data = sce,
  sc.cell.ID.column = "Cell_ID",
  sc.gene.ID.column = "Gene_ID",
  st.data = ste,
  st.spot.ID.column = "Cell_ID",
  st.gene.ID.column = "Gene_ID",
  project = "Simul_example",
  sc.filt.genes.cluster = FALSE
)
  

Get and set deconv.spots slot in a SpatialExperiment object

Description

Get and set deconv.spots slot in a SpatialExperiment object

Usage

deconv.spots(object, index.st = NULL)

deconv.spots(object, index.st = NULL) <- value

Arguments

Deconvolute spatial transcriptomics data using trained model

Description

Deconvolute spatial transcriptomics data using the trained model in the SpatialDDLS object. The trained model is used to predict cell proportions of two mirrored transcriptional profiles:

• 'Intrinsic' profiles: transcriptional profiles of each spot in the ST dataset.

• 'Extrinsic' profiles: profiles simulated from the surrounding spots of each spot.

After prediction, cell proportions from the intrinsic profiles (intrinsic cell proportions) are regularized based on the similarity between intrinsic and extrinsic profiles in order to maintain spatial consistency. This approach leverages both transcriptional and spatial information. For more details, see Mañanes et al., 2023 and the Details section.

Usage

deconvSpatialDDLS(
  object,
  index.st,
  normalize = TRUE,
  scaling = "standardize",
  k.spots = 4,
  pca.space = TRUE,
  fast.pca = TRUE,
  pcs.num = 50,
  pca.var = 0.8,
  metric = "euclidean",
  alpha.cutoff = "mean",
  alpha.quantile = 0.5,
  simplify.set = NULL,
  simplify.majority = NULL,
  use.generator = FALSE,
  batch.size = 64,
  verbose = TRUE
)

Arguments

Details

The deconvolution process involves two main steps: predicting cell proportions based on transcriptome using the trained neural network model, and regularization of cell proportions based on the spatial location of each spot. In the regularization step, a mirrored version of each spot is simulated based on its N-nearest spots. We refer to these profiles as 'extrinsic' profiles, whereas the transcriptional profiles of each spot are called 'intrinsic' profiles. Extrinsic profiles are used to regularize predictions based on intrinsic profiles. The rationale is that spots surrounded by transcriptionally similar spots should have similar cell compositions, and therefore predicted proportions can be smoothed to preserve their spatial consistency. On the other hand, spots surrounded by dissimilar spots cannot be predicted by their neighbors, and thus they can only be predicted by their own transcriptional profiles likely due to presenting very specific cell compositions.

Regarding the working os SpatialDDLS: first, extrinsic profiles are simulated based on the N-nearest spots for each spot by summing their transcriptomes. Distances between extrinsic and intrinsic profiles of each spot are calculated so that similar/dissimilar spots are identified. These two sets of transcriptional profiles are used as input for the trained neural network model, and according to the calculated distances, a weighted mean between the predicted proportions for each spot is calculated. Spots with distances between intrinsic and extrinsic profiles greater than alpha.cutoff are not regularized, whereas spots with distances less than alpha.cutoff contribute to the weighted mean. Weights are calculated by rescaling distances less than alpha.cutoff between 0 and 0.5, so that the maximum extent to which a extrinsic profile can modified the predictions based on intrinsic profiles is 0.5 (a regular mean). For more details, see Mañanes et al., 2023.

This function requires a SpatialDDLS object with a trained deep neural network model (trained.model slot, and the spatial transcriptomics datasets to be deconvoluted in the spatial.experiments slot. See ?createSpatialDDLSobject or ?loadSTProfiles for more details.

Value

SpatialDDLS object with a deconv.spots slot. The output is a list containing 'Regularized', 'Intrinsic' and 'Extrinsic' deconvoluted cell proportions, 'Distances' between intrinsic and extrinsic transcriptional profiles, and 'Weight.factors' with the final weights used to regularize intrinsic cell proportions. If simplify.set and/or simplify.majority are provided, the deconv.spots slot will contain a list with raw and simplified results.

References

Mañanes, D., Rivero-García, I., Jimenez-Carretero, D., Torres, M., Sancho, D., Torroja, C., Sánchez-Cabo, F. (2023). SpatialDDLS: An R package to deconvolute spatial transcriptomics data using neural networks. biorxiv. doi: doi:10.1101/2023.08.31.555677.

See Also

trainDeconvModel SpatialDDLS

Examples

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
)
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)
# training of SDDLS model
SDDLS <- trainDeconvModel(
  object = SDDLS,
  batch.size = 15,
  num.epochs = 5
)
# simulating spatial data
ngenes <- sample(3:40, size = 1)
ncells <- sample(10:40, size = 1)
counts <- matrix(
  rpois(ngenes * ncells, lambda = 5), ncol = ncells,
  dimnames = list(paste0("Gene", seq(ngenes)), paste0("Spot", seq(ncells)))
)
coordinates <- matrix(
  rep(c(1, 2), ncells), ncol = 2
)
st <- SpatialExperiment::SpatialExperiment(
  assays = list(counts = as.matrix(counts)),
  rowData = data.frame(Gene_ID = paste0("Gene", seq(ngenes))),
  colData = data.frame(Cell_ID = paste0("Spot", seq(ncells))),
  spatialCoords = coordinates
)
SDDLS <- loadSTProfiles(
  object = SDDLS,
  st.data = st,
  st.spot.ID.column = "Cell_ID",
  st.gene.ID.column = "Gene_ID"
)
# simplify arguments
simplify <- list(CellGroup1 = c("CellType1", "CellType2", "CellType4"),
                 CellGroup2 = c("CellType3", "CellType5"))
SDDLS <- deconvSpatialDDLS(
  object = SDDLS,
  index.st = 1,
  simplify.set = simplify, 
  simplify.majority = simplify
)

Generate box or violin plots showing error distribution

Description

Generate box or violin plots to show how errors are distributed. Errors can be shown all mixed or either split by cell type (CellType) or number of cell types present in the spots (nCellTypes). See the facet.by argument and examples for more details.

Usage

distErrorPlot(
  object,
  error,
  colors,
  x.by = "pBin",
  facet.by = NULL,
  color.by = "nCellTypes",
  filter.sc = TRUE,
  error.label = FALSE,
  pos.x.label = 4.6,
  pos.y.label = NULL,
  size.point = 0.1,
  alpha.point = 1,
  type = "violinplot",
  ylimit = NULL,
  nrow = NULL,
  ncol = NULL,
  title = NULL,
  theme = NULL,
  ...
)

Arguments

Value

A ggplot object.

See Also

calculateEvalMetrics corrExpPredPlot blandAltmanLehPlot barErrorPlot

Examples

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
)
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)
# training of DDLS model
SDDLS <- trainDeconvModel(
  object = SDDLS,
  batch.size = 15,
  num.epochs = 5
)
# evaluation using test data
SDDLS <- calculateEvalMetrics(object = SDDLS)
# representation, for more examples, see the vignettes
distErrorPlot(
  object = SDDLS,
  error = "AbsErr",
  facet.by = "CellType",
  color.by = "nCellTypes",
  error.label = TRUE
)
distErrorPlot(
  object = SDDLS,
  error = "AbsErr",
  x.by = "CellType",
  facet.by = NULL,
  color.by = "CellType",
  error.label = TRUE
)

Estimate parameters of the ZINB-WaVE model to simulate new single-cell RNA-Seq expression profiles

Description

Estimate the parameters of the ZINB-WaVE model using a real single-cell RNA-Seq data set as reference to simulate new single-cell profiles and increase the signal of underrepresented cell types. This step is only is needed if the size of the single-cell RNA-seq dataset is too small or there are underrepresented cell types. After this step, the simSCProfiles function will use the estimated parameters to simulate new single-cell profiles. See ?simSCProfiles for more information.

Usage

estimateZinbwaveParams(
  object,
  cell.type.column,
  cell.ID.column,
  gene.ID.column,
  cell.cov.columns,
  gene.cov.columns,
  subset.cells = NULL,
  proportional = TRUE,
  set.type = "All",
  threads = 1,
  verbose = TRUE
)

Arguments

Details

ZINB-WaVE is a flexible model for zero-inflated count data. This function carries out the model fit to real single-cell data modeling Y_{ij} (the count of feature j for sample i) as a random variable following a zero-inflated negative binomial (ZINB) distribution. The estimated parameters will be used for the simulation of new single-cell expression profiles by sampling a negative binomial distribution and inserting dropouts from a binomial distribution. To do so, SpatialDDLS uses the zinbFit function from the zinbwave package (Risso et al., 2018). For more details about the model, see Risso et al., 2018.

Value

A SpatialDDLS object with zinb.params slot containing a ZinbParametersModel object. This object contains a slot with the estimated ZINB-WaVE parameters from the real single-cell RNA-Seq data.

References

Risso, D., Perraudeau, F., Gribkova, S. et al. (2018). A general and flexible method for signal extraction from single-cell RNA-seq data. Nat Commun 9, 284. doi: doi:10.1038/s41467-017-02554-5.

Torroja, C. and Sánchez-Cabo, F. (2019). digitalDLSorter: A Deep Learning algorithm to quantify immune cell populations based on scRNA-Seq data. Frontiers in Genetics 10, 978. doi: doi:10.3389/fgene.2019.00978.

See Also

simSCProfiles

Examples

set.seed(123) # reproducibility
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",
  project = "Simul_example",
  sc.filt.genes.cluster = FALSE
)
SDDLS <- estimateZinbwaveParams(
  object = SDDLS,
  cell.type.column = "Cell_Type",
  cell.ID.column = "Cell_ID",
  gene.ID.column = "Gene_ID",
  subset.cells = 2,
  verbose = TRUE
)

Get and set features slot in a DeconvDLModel object

Description

Get and set features slot in a DeconvDLModel object

Usage

features(object)

features(object) <- value

Arguments

Generate training and test cell type composition matrices

Description

Generate training and test cell type composition matrices for the simulation of mixed transcriptional profiles with known cell composition using single-cell expression profiles. The resulting PropCellTypes object will contain all the information needed to simulate new mixed transcriptional profiles. Note this function does not simulate the mixed profiles, this task is performed by the simMixedProfiles or trainDeconvModel functions (see Documentation).

Usage

genMixedCellProp(
  object,
  cell.ID.column,
  cell.type.column,
  num.sim.spots,
  n.cells = 50,
  train.freq.cells = 3/4,
  train.freq.spots = 3/4,
  proportion.method = c(0, 0, 1),
  prob.sparity = 1,
  min.zero.prop = NULL,
  balanced.type.cells = TRUE,
  verbose = TRUE
)

Arguments

Details

First, the single-cell profiles are randomly divided into two subsets, with 2/3 of the data for training and 1/3 for testing. The default setting for this ratio can be changed using the train.freq.cells parameter. Next, a total of num.sim.spots mixed proportions are simulated using a Dirichlet distribution. This simulation takes into account the probability of missing cell types in each spot, which can be adjusted using the prob.sparity parameter. For each mixed sample, n.cells single-cell profiles are randomly selected and combined to generate the simulated mixed sample. In addition to the Dirichlet-based proportions, pure spots (containing only one cell type) and spots containing a specified number of different cell types (determined by the min.zero.prop parameter) are also generated in order to cover situations with only a few cell types present. The proportion of simulated spots generated by each method can be controlled using the proportion.method parameter. To visualize the distribution of cell type proportions generated by each method, the showProbPlot function can be used.

Value

A SpatialDDLS object with prob.cell.types slot containing a list with two PropCellTypes objects (training and test). For more information about the structure of this class, see ?PropCellTypes.

See Also

simMixedProfiles PropCellTypes

Examples

set.seed(123)
sce <- SingleCellExperiment::SingleCellExperiment(
  assays = list(
    counts = matrix(
      rpois(100, lambda = 5), nrow = 40, ncol = 30,
      dimnames = list(paste0("Gene", seq(40)), paste0("RHC", seq(30)))
    )
  ),
  colData = data.frame(
    Cell_ID = paste0("RHC", seq(30)),
    Cell_Type = sample(x = paste0("CellType", seq(4)), size = 30,
                       replace = TRUE)
  ),
  rowData = data.frame(
    Gene_ID = paste0("Gene", seq(40))
  )
)

SDDLS <- createSpatialDDLSobject(
  sc.data = sce,
  sc.cell.ID.column = "Cell_ID",
  sc.gene.ID.column = "Gene_ID",
  sc.filt.genes.cluster = FALSE,
  project = "Simul_example"
)
SDDLS <- genMixedCellProp(
  object = SDDLS,
  cell.ID.column = "Cell_ID",
  cell.type.column = "Cell_Type",
  num.sim.spots = 10,
  train.freq.cells = 2/3,
  train.freq.spots = 2/3,
  verbose = TRUE
)
  

Getter function for the cell composition matrix

Description

Getter function for the cell composition matrix. This function allows to access to the cell composition matrix of simulated mixed transcriptional profiles.

Usage

getProbMatrix(object, type.data)

Arguments

Value

Cell type proportion matrix.

See Also

genMixedCellProp

Install Python dependencies for SpatialDDLS

Description

This function facilitates the installation of the required Python dependencies for the SpatialDDLS R package, as it requires a Python interpreter with the TensorFlow Python library and its dependencies.

Usage

installTFpython(
  conda = "auto",
  python.version = "3.8",
  tensorflow.version = "2.6",
  install.conda = FALSE,
  miniconda.path = NULL
)

Arguments

Details

This function is intended to simplify the installation process for SpatialDDLS by automatically installing Miniconda and creating a new environment named SpatialDDLS-env with all SpatialDDLS' dependencies covered. For users who wish to use a different Python or conda environment, see the tensorflow::use_condaenv function for more information.

Value

No return value, called for side effects: installation of conda environment with a Python interpreter and Tensorflow

Examples

## Not run: 
notesInstallation <- installTFpython(
  conda = "auto", install.conda = TRUE
)

## End(Not run)

Calculate gradients of predicted cell types/loss function with respect to input features for interpreting trained deconvolution models

Description

This function enables users to gain insights into the interpretability of the deconvolution model. It calculates the gradients of classes/loss function with respect to the input features used in training. These numeric values are calculated per gene and cell type in pure mixed transcriptional profiles, providing information on the extent to which each feature influences the model's prediction of cell proportions for each cell type.

Usage

interGradientsDL(
  object,
  method = "class",
  normalize = TRUE,
  scaling = "standardize",
  verbose = TRUE
)

Arguments

Details

Gradients of classes / loss function with respect to the input features are calculated exclusively using pure mixed transcriptional profiles composed of a single cell type. Consequently, these numbers can be interpreted as the extent to which each feature is being used to predict each cell type proportion. Gradients are calculated at the sample level for each gene, but only mean gradients by cell type are reported. For additional details, see Mañanes et al., 2023.

Value

Object containing gradients in the interpret.gradients slot of the DeconvDLModel object (trained.model slot).

See Also

deconvSpatialDDLS plotTrainingHistory

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)

  

Loads spatial transcriptomics data into a SpatialDDLS object

Description

This function loads a SpatialExperiment object (or a list with several SpatialExperiment objects) into a SpatialDDLS object.

Usage

loadSTProfiles(
  object,
  st.data,
  st.spot.ID.column,
  st.gene.ID.column,
  st.min.counts = 0,
  st.min.spots = 0,
  st.n.slides = 3,
  verbose = TRUE
)

Arguments

Details

It is recommended to perform this step when creating the SpatialDDLS object using the createSpatialDDLSobject function in order to only keep genes shared between the spatial transcriptomics and the single-cell transcriptomics data used as reference. In addition, please, make sure the gene identifiers used the spatial and single-cell transcriptomics data are consistent.

Value

A SpatialDDLS object with the provided spatial trainscriptomics data loaded into the spatial.experiments slot.

See Also

createSpatialDDLSobject trainDeconvModel

Examples

set.seed(123)
sce <- SingleCellExperiment::SingleCellExperiment(
  assays = list(
    counts = matrix(
      rpois(100, lambda = 5), nrow = 40, ncol = 30,
      dimnames = list(paste0("Gene", seq(40)), paste0("RHC", seq(30)))
    )
  ),
  colData = data.frame(
    Cell_ID = paste0("RHC", seq(30)),
    Cell_Type = sample(x = paste0("CellType", seq(4)), size = 30,
                       replace = TRUE)
  ),
  rowData = data.frame(
    Gene_ID = paste0("Gene", seq(40))
  )
)
SDDLS <- createSpatialDDLSobject(
  sc.data = sce,
  sc.cell.ID.column = "Cell_ID",
  sc.gene.ID.column = "Gene_ID",
  sc.filt.genes.cluster = FALSE
)

## simulating a SpatialExperiment object
counts <- matrix(rpois(30, lambda = 5), ncol = 6)
rownames(counts) <- paste0("Gene", 1:5)
colnames(counts) <- paste0("Spot", 1:6)
coordinates <- matrix(
  c(1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3), ncol = 2
)
ste <- SpatialExperiment::SpatialExperiment(
  assays = list(counts = as.matrix(counts)),
  rowData = data.frame(Gene_ID = paste0("Gene", 1:5)),
  colData = data.frame(Cell_ID = paste0("Spot", 1:6)),
  spatialCoords = coordinates
)

## previous SpatialDDLS object
SDDLS <- loadSTProfiles(
  object = SDDLS,
  st.data = ste,
  st.spot.ID.column = "Cell_ID",
  st.gene.ID.column = "Gene_ID"
)

  

Load from an HDF5 file a trained deep neural network model into a SpatialDDLS object

Description

Load from an HDF5 file a trained deep neural network model into a SpatialDDLS object. Note that HDF5 file must be a valid trained model (keras object).

Usage

loadTrainedModelFromH5(object, file.path, reset.slot = FALSE)

Arguments

Value

SpatialDDLS object with trained.model slot with the new keras DNN model incorporated.

See Also

trainDeconvModel saveTrainedModelAsH5

Get and set method slot in a PropCellTypes object

Description

Get and set method slot in a PropCellTypes object

Usage

method(object)

method(object) <- value

Arguments

Get and set mixed.profiles slot in a SpatialExperiment object

Description

Get and set mixed.profiles slot in a SpatialExperiment object

Usage

mixed.profiles(object, type.data = "both")

mixed.profiles(object, type.data = "both") <- value

Arguments

Get and set model slot in a DeconvDLModel object

Description

Get and set model slot in a DeconvDLModel object

Usage

model(object)

model(object) <- value

Arguments

Plot distances between intrinsic and extrinsic profiles

Description

Color spots on the spatial coordinates according to distances between intrinsic and extrinsic transcriptional profiles.

Usage

plotDistances(
  object,
  index.st,
  mid.scale = "mean",
  size.point = 1,
  title = NULL,
  theme = NULL
)

Arguments

Value

A ggplot object.

See Also

deconvSpatialDDLS trainDeconvModel

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

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)

  

Plot results of clustering based on predicted cell proportions

Description

Color spots on the spatial coordinates according to the results of clustering based on predicted proportions.

Usage

plotSpatialClustering(
  object,
  index.st,
  method,
  k.nn,
  k.centers,
  colors,
  size.point = 1,
  title = NULL,
  theme = NULL
)

Arguments

Value

A ggplot object.

See Also

spatialPropClustering deconvSpatialDDLS

Plot normalized gene expression data (logCPM) in spatial coordinates

Description

Color spots on the spatial coordinates according to the logCPM values of a particular gene.

Usage

plotSpatialGeneExpr(
  object,
  index.st,
  gene,
  colors = "spectral",
  size.point = 1,
  title = NULL,
  theme = NULL
)

Arguments

Value

A ggplot object.

See Also

interGradientsDL topGradientsCellType

Plot predicted proportions for a specific cell type using spatial coordinates of spots

Description

Color spots on the spatial coordinates according to the predicted proportions of a particular cell type. Color scale is adapted depending on the range of predicted proportions.

Usage

plotSpatialProp(
  object,
  index.st,
  cell.type,
  colors = "blues",
  set = "raw",
  prediction = "Regularized",
  limits = NULL,
  size.point = 1,
  title = NULL,
  theme = NULL
)

Arguments

Value

A ggplot object.

See Also

plotSpatialPropAll deconvSpatialDDLS trainDeconvModel

Plot predicted proportions for all cell types using spatial coordinates of spots

Description

Color spots on the spatial coordinates plot according to their predicted cell type proportions. All cell types are represented together using the same color scale from 0 to 1.

Usage

plotSpatialPropAll(
  object,
  index.st,
  colors = "blues",
  set = "raw",
  prediction = "Regularized",
  size.point = 0.1,
  title = NULL,
  nrow = NULL,
  ncol = NULL,
  theme = NULL
)

Arguments

Value

A ggplot object.

See Also

plotSpatialProp deconvSpatialDDLS trainDeconvModel

Plot training history of a trained SpatialDDLS deep neural network model

Description

Plot training history of a trained SpatialDDLS deep neural network model.

Usage

plotTrainingHistory(
  object,
  title = "History of metrics during training",
  metrics = NULL
)

Arguments

Value

A ggplot object with the progression of the selected metrics during training.

See Also

trainDeconvModel

Get and set plots slot in a PropCellTypes object

Description

Get and set plots slot in a PropCellTypes object

Usage

plots(object)

plots(object) <- value

Arguments

Prepare SpatialDDLS object to be saved as an RDA file

Description

This function prepares a SpatialDDLS object to be saved as an RDA file when contains a DeconvDLModel object with a trained DNN model.

Usage

preparingToSave(object)

Arguments

Details

Since keras models cannot be saved natively as R objects, this function saves the structure of the model as a JSON-like character object and its weights as a list. This allows for the retrieval of the model and making predictions. It is important to note that the state of the optimizer is not saved, only the model's architecture and weights. To save the entire model, please see the saveTrainedModelAsH5 and loadTrainedModelFromH5 functions.

It is also possible to save a SpatialDDLS object as an RDS file with the saveRDS function without any preparation.

Value

A SpatialDDLS or DeconvDLModel object with its trained keras model transformed from a keras.engine.sequential.Sequential class into a list with its architecture as a JSON-like character object, and its weights as a list.

See Also

saveRDS saveTrainedModelAsH5

Get and set prob.cell.types slot in a SpatialExperiment object

Description

Get and set prob.cell.types slot in a SpatialExperiment object

Usage

prob.cell.types(object, type.data = "both")

prob.cell.types(object, type.data = "both") <- value

Arguments

Get and set prob.matrix slot in a PropCellTypes object

Description

Get and set prob.matrix slot in a PropCellTypes object

Usage

prob.matrix(object)

prob.matrix(object) <- value

Arguments

Get and set project slot in a SpatialExperiment object

Description

Get and set project slot in a SpatialExperiment object

Usage

project(object)

project(object) <- value

Arguments

Save SpatialExperiment objects as RDS files

Description

Save SpatialExperiment and DeconvDLModel objects as RDS files. keras models cannot be stored natively as R objects (e.g. RData or RDS files). By saving the architecture as a JSON-like character object and the weights as a list, it is possible to retrieve a functional model and make new predictions. If the trained.model slot is empty, the function will behave as usual. Note: with this option, the state of optimizer is not saved, only model's architecture and weights. It is possible to save the entire model as an HDF5 file with the saveTrainedModelAsH5 function and load it into a SpatialExperiment object with the loadTrainedModelFromH5 function. See documentation for details.

Usage

saveRDS(
  object,
  file,
  ascii = FALSE,
  version = NULL,
  compress = TRUE,
  refhook = NULL
)

## S4 method for signature 'DeconvDLModel'
saveRDS(
  object,
  file,
  ascii = FALSE,
  version = NULL,
  compress = TRUE,
  refhook = NULL
)

## S4 method for signature 'SpatialDDLS'
saveRDS(
  object,
  file,
  ascii = FALSE,
  version = NULL,
  compress = TRUE,
  refhook = NULL
)

Arguments

Value

No return value, saves a SpatialExperiment object as an RDS file on disk.

See Also

SpatialExperiment saveTrainedModelAsH5

Save a trained SpatialDDLS deep neural network model to disk as an HDF5 file

Description

Save a trained SpatialDDLS deep neural network model to disk as an HDF5 file. Note that this function does not save the DeconvDLModel object, only the trained keras model. This is the alternative to the saveRDS and preparingToSave functions if you want to keep the state of the optimizer.

Usage

saveTrainedModelAsH5(object, file.path, overwrite = FALSE)

Arguments

Value

No return value, saves a keras DNN trained model as HDF5 file on disk.

See Also

trainDeconvModel loadTrainedModelFromH5

Get and set set slot in a PropCellTypes object

Description

Get and set set slot in a PropCellTypes object

Usage

set(object)

set(object) <- value

Arguments

Get and set set.list slot in a PropCellTypes object

Description

Get and set set.list slot in a PropCellTypes object

Usage

set.list(object)

set.list(object) <- value

Arguments

Show distribution plots of the cell proportions generated by genMixedCellProp

Description

Show distribution plots of the cell proportions generated by the genMixedCellProp function.

Usage

showProbPlot(object, type.data, set, type.plot = "boxplot")

Arguments

Details

These frequencies will determine the proportion of different cell types used during the simulation of mixed transcriptional profiles. Proportions generated by each method (see ?genMixedCellProp) can be visualized in three ways: box plots, violin plots, and lines plots. You can also plot the probabilities based on the number of different cell types present in the samples by setting type.plot = 'nCellTypes'.

Value

A ggplot object.

See Also

genMixedCellProp

Examples

set.seed(123)
sce <- SingleCellExperiment::SingleCellExperiment(
  assays = list(
    counts = matrix(
      rpois(100, lambda = 5), nrow = 40, ncol = 30,
      dimnames = list(paste0("Gene", seq(40)), paste0("RHC", seq(30)))
    )
  ),
  colData = data.frame(
    Cell_ID = paste0("RHC", seq(30)),
    Cell_Type = sample(x = paste0("CellType", seq(4)), size = 30,
                       replace = TRUE)
  ),
  rowData = data.frame(
    Gene_ID = paste0("Gene", seq(40))
  )
)

SDDLS <- createSpatialDDLSobject(
  sc.data = sce,
  sc.cell.ID.column = "Cell_ID",
  sc.gene.ID.column = "Gene_ID",
  project = "Simul_example",
  sc.filt.genes.cluster = FALSE
)
SDDLS <- genMixedCellProp(
  object = SDDLS,
  cell.ID.column = "Cell_ID",
  cell.type.column = "Cell_Type",
  num.sim.spots = 10, 
  train.freq.cells = 2/3,
  train.freq.spots = 2/3,
  verbose = TRUE
)
showProbPlot(
   SDDLS,
   type.data = "train",
   set = 1,
   type.plot = "boxplot"
 )
 

Simulate training and test mixed spot profiles

Description

Simulate training and test mixed spot transcriptional profiles using cell composition matrices generated by the genMixedCellProp function.

Usage

simMixedProfiles(
  object,
  type.data = "both",
  mixing.function = "AddRawCount",
  file.backend = NULL,
  compression.level = NULL,
  block.processing = FALSE,
  block.size = 1000,
  chunk.dims = NULL,
  threads = 1,
  verbose = TRUE
)

Arguments

Details

Mixed profiles are generated under the assumption that the expression level of a particular gene in a given spot is the sum of the expression levels of the cell types that make it up weighted by their proportions. In practice, as described in Torroja and Sanchez-Cabo, 2019, these profiles are generated by summing gene expression levels of a determined number of cells specified by a known cell composition matrix. The number of simulated spots and cells used to simulate each spot are determined by the genMixedCellProp function. This step can be avoided by using the on.the.fly argument in the trainDeconvModel function.

SpatialDDLS allows to use HDF5 files as back-end to store simulated data using the DelayedArray and HDF5Array packages. This functionality allows to work without keeping the data loaded into RAM, which could be useful during some computationally heavy steps such as neural network training on RAM-limited machines. You must provide a valid file path in the file.backend argument to store the resulting file with the '.h5' extension. This option slows down execution times, as subsequent transformations of the data will be done in blocks. Note that if you use the file.backend argument with block.processing = FALSE, all mixed profiles will be simulated in one step and, thus, loaded into RAM. Then, the matrix will be written to an HDF5 file. To avoid the RAM collapse, these profiles can be simulated and written to HDF5 files in blocks of block.size size by setting block.processing = TRUE. We recommend this option accordingly to the computational resources available and the number of simulated spots to be generated, but, in most of the cases, it is not necessary.

Value

A SpatialDDLS object with mixed.profiles slot containing a list with one or two entries (depending on selected type.data argument): 'train' and 'test'. Each entry consists of a SummarizedExperiment object with the simulated mixed slot profiles.

References

Fischer B, Smith M and Pau, G (2020). rhdf5: R Interface to HDF5. R package version 2.34.0.

Pagès H, Hickey P and Lun A (2020). DelayedArray: A unified framework for working transparently with on-disk and in-memory array-like datasets. R package version 0.16.0.

Pagès H (2020). HDF5Array: HDF5 backend for DelayedArray objects. R package version 1.18.0.

See Also

genMixedCellProp PropCellTypes trainDeconvModel

Examples

set.seed(123)
sce <- SingleCellExperiment::SingleCellExperiment(
  assays = list(
    counts = matrix(
      rpois(100, lambda = 5), nrow = 40, ncol = 30,
      dimnames = list(paste0("Gene", seq(40)), paste0("RHC", seq(30)))
    )
  ),
  colData = data.frame(
    Cell_ID = paste0("RHC", seq(30)),
    Cell_Type = sample(x = paste0("CellType", seq(4)), size = 30,
                       replace = TRUE)
  ),
  rowData = data.frame(
    Gene_ID = paste0("Gene", seq(40))
  )
)

SDDLS <- createSpatialDDLSobject(
  sc.data = sce,
  sc.cell.ID.column = "Cell_ID",
  sc.gene.ID.column = "Gene_ID",
  sc.filt.genes.cluster = FALSE,
  project = "Simul_example"
)
SDDLS <- genMixedCellProp(
  object = SDDLS,
  cell.ID.column = "Cell_ID",
  cell.type.column = "Cell_Type",
  num.sim.spots = 10,
  train.freq.cells = 2/3,
  train.freq.spots = 2/3,
  verbose = TRUE
)
SDDLS <- simMixedProfiles(SDDLS, verbose = TRUE)

Simulate new single-cell RNA-Seq expression profiles using the ZINB-WaVE model parameters

Description

Simulate single-cell expression profiles by randomly sampling from a negative binomial distribution and inserting dropouts by sampling from a binomial distribution using the ZINB-WaVE parameters estimated by the estimateZinbwaveParams function.

Usage

simSCProfiles(
  object,
  cell.ID.column,
  cell.type.column,
  n.cells,
  suffix.names = "_Simul",
  cell.types = NULL,
  file.backend = NULL,
  name.dataset.backend = NULL,
  compression.level = NULL,
  block.processing = FALSE,
  block.size = 1000,
  chunk.dims = NULL,
  verbose = TRUE
)

Arguments

Details

Before this step, see ?estimateZinbwaveParams. As described in Torroja and Sanchez-Cabo, 2019, this function simulates a given number of transcriptional profiles for each cell type provided by randomly sampling from a negative binomial distribution with \mu and \theta estimated parameters and inserting dropouts by sampling from a binomial distribution with probability pi. All parameters are estimated from single-cell real data using the estimateZinbwaveParams function. It uses the ZINB-WaVE model (Risso et al., 2018). For more details about the model, see ?estimateZinbwaveParams and Risso et al., 2018.

The file.backend argument allows to create a HDF5 file with simulated single-cell profiles to be used as back-end to work with data stored on disk instead of loaded into RAM. If the file.backend argument is used with block.processing = FALSE, all the single-cell profiles will be simulated in one step and, therefore, loaded into in RAM memory. Then, data will be written in HDF5 file. To avoid to collapse RAM memory if too many single-cell profiles are goin to be simulated, single-cell profiles can be simulated and written to HDF5 files in blocks of block.size size by setting block.processing = TRUE.

Value

A SpatialDDLS object with single.cell.simul slot containing a SingleCellExperiment object with the simulated single-cell expression profiles.

References

Risso, D., Perraudeau, F., Gribkova, S. et al. (2018). A general and flexible method for signal extraction from single-cell RNA-seq data. Nat Commun 9, 284. doi: doi:10.1038/s41467-017-02554-5.

Torroja, C. and Sánchez-Cabo, F. (2019). digitalDLSorter: A Deep Learning algorithm to quantify immune cell populations based on scRNA-Seq data. Frontiers in Genetics 10, 978. doi: doi:10.3389/fgene.2019.00978.

See Also

estimateZinbwaveParams

Examples

set.seed(123) # reproducibility
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,
  project = "Simul_example"
)
SDDLS <- estimateZinbwaveParams(
  object = SDDLS,
  cell.type.column = "Cell_Type",
  cell.ID.column = "Cell_ID",
  gene.ID.column = "Gene_ID",
  subset.cells = 2,
  verbose = TRUE
)
SDDLS <- simSCProfiles(
  object = SDDLS,
  cell.ID.column = "Cell_ID",
  cell.type.column = "Cell_Type",
  n.cells = 2,
  verbose = TRUE
)

Get and set single.cell.real slot in a SpatialExperiment object

Description

Get and set single.cell.real slot in a SpatialExperiment object

Usage

single.cell.real(object)

single.cell.real(object) <- value

Arguments

Get and set single.cell.simul slot in a SpatialExperiment object

Description

Get and set single.cell.simul slot in a SpatialExperiment object

Usage

single.cell.simul(object)

single.cell.simul(object) <- value

Arguments

Get and set spatial.experiments slot in a SpatialExperiment object

Description

Get and set spatial.experiments slot in a SpatialExperiment object

Usage

spatial.experiments(object, index.st = NULL)

spatial.experiments(object, index.st = NULL) <- value

Arguments

Cluster spatial data based on predicted cell proportions

Description

Cluster spatial transcriptomics data according to the cell proportions predicted in each spot. It allows to segregate ST data into niches with similar cell composition.

Usage

spatialPropClustering(
  object,
  index.st,
  method = "graph",
  k.nn = 10,
  k.centers = 5,
  verbose = TRUE
)

Arguments

Value

A SpatialDDLS object containing computed clusters as a column in the slot colData of the SpatialExperiment objects.

See Also

plotTrainingHistory deconvSpatialDDLS

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(
  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(
  SDDLS,
  batch.size = 12,
  num.epochs = 5
) 
# simulating spatial data
ngenes <- sample(3:40, size = 1)
ncells <- sample(10:40, size = 1)
counts <- matrix(
  rpois(ngenes * ncells, lambda = 5), ncol = ncells,
  dimnames = list(paste0("Gene", seq(ngenes)), paste0("Spot", seq(ncells)))
)
coordinates <- matrix(
  rep(c(1, 2), ncells), ncol = 2
)
st <- SpatialExperiment::SpatialExperiment(
  assays = list(counts = as.matrix(counts)),
  rowData = data.frame(Gene_ID = paste0("Gene", seq(ngenes))),
  colData = data.frame(Cell_ID = paste0("Spot", seq(ncells))),
  spatialCoords = coordinates
)
SDDLS <- loadSTProfiles(
  object = SDDLS,
  st.data = st,
  st.spot.ID.column = "Cell_ID",
  st.gene.ID.column = "Gene_ID"
)
SDDLS <- deconvSpatialDDLS(
  SDDLS,
  index.st = 1
) 
SDDLS <- spatialPropClustering(SDDLS, index.st = 1, k.nn = 5)

  

Get and set test.deconv.metrics slot in a DeconvDLModel object

Description

Get and set test.deconv.metrics slot in a DeconvDLModel object

Usage

test.deconv.metrics(object, metrics = "All")

test.deconv.metrics(object, metrics = "All") <- value

Arguments

Get and set test.metrics slot in a DeconvDLModel object

Description

Get and set test.metrics slot in a DeconvDLModel object

Usage

test.metrics(object)

test.metrics(object) <- value

Arguments

Get and set test.pred slot in a DeconvDLModel object

Description

Get and set test.pred slot in a DeconvDLModel object

Usage

test.pred(object)

test.pred(object) <- value

Arguments

Get top genes with largest/smallest gradients per cell type

Description

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

Value

List of gene names with the top positive and negative gradients per cell type.

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)
listGradients <- topGradientsCellType(SDDLS)
lapply(listGradients, head, n = 5)

  

Train deconvolution model for spatial transcriptomics data

Description

Train a deep neural network model using training data from the SpatialDDLS object. This model will be used to deconvolute spatial transcriptomics data from the same biological context as the single-cell RNA-seq data used to train it. In addition, the trained model is evaluated using test data, and prediction results are obtained to determine its performance (see ?calculateEvalMetrics).

Usage

trainDeconvModel(
  object,
  type.data.train = "mixed",
  type.data.test = "mixed",
  batch.size = 64,
  num.epochs = 60,
  num.hidden.layers = 2,
  num.units = c(200, 200),
  activation.fun = "relu",
  dropout.rate = 0.25,
  loss = "kullback_leibler_divergence",
  metrics = c("accuracy", "mean_absolute_error", "categorical_accuracy"),
  normalize = TRUE,
  scaling = "standardize",
  norm.batch.layers = TRUE,
  custom.model = NULL,
  shuffle = TRUE,
  sc.downsampling = NULL,
  use.generator = FALSE,
  on.the.fly = FALSE,
  agg.function = "AddRawCount",
  threads = 1,
  view.metrics.plot = TRUE,
  verbose = TRUE
)

Arguments

Details

Simulation of mixed transcriptional profiles 'on the fly'

trainDeconvModel can avoid storing simulated mixed spot profiles by using the on.the.fly argument. This functionality aims at reducing the the simMixedProfiles function's memory usage: simulated profiles are built in each batch during training/evaluation.

Neural network architecture

It is possible to change the model's architecture: number of hidden layers, number of neurons for each hidden layer, dropout rate, activation function, and loss function. For more customized models, it is possible to provide a pre-built model through the custom.model argument (a keras.engine.sequential.Sequential object) where it is necessary that the number of input neurons is equal to the number of considered features/genes, and the number of output neurons is equal to the number of considered cell types.

Value

A SpatialDDLS object with trained.model slot containing a DeconvDLModel object. For more information about the structure of this class, see ?DeconvDLModel.

See Also

plotTrainingHistory deconvSpatialDDLS

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
)

  

Get and set trained.model slot in a SpatialExperiment object

Description

Get and set trained.model slot in a SpatialExperiment object

Usage

trained.model(object)

trained.model(object) <- value

Arguments

Get and set training.history slot in a DeconvDLModel object

Description

Get and set training.history slot in a DeconvDLModel object

Usage

training.history(object)

training.history(object) <- value

Arguments

Get and set zinb.params slot in a SpatialExperiment object

Description

Get and set zinb.params slot in a SpatialExperiment object

Usage

zinb.params(object)

zinb.params(object) <- value

Arguments

Get and set zinbwave.model slot in a ZinbParametersModel object

Description

Get and set zinbwave.model slot in a ZinbParametersModel object

Usage

zinbwave.model(object)

zinbwave.model(object) <- value

Arguments