Package 'CARDspa' reference manual

Title:	Spatially Informed Cell Type Deconvolution for Spatial Transcriptomics
Description:	CARD is a reference-based deconvolution method that estimates cell type composition in spatial transcriptomics based on cell type specific expression information obtained from a reference scRNA-seq data. A key feature of CARD is its ability to accommodate spatial correlation in the cell type composition across tissue locations, enabling accurate and spatially informed cell type deconvolution as well as refined spatial map construction. CARD relies on an efficient optimization algorithm for constrained maximum likelihood estimation and is scalable to spatial transcriptomics with tens of thousands of spatial locations and tens of thousands of genes.
Authors:	Ying Ma [aut], Jing Fu [cre]
Maintainer:	Jing Fu <[email protected]>
License:	GPL-3 + file LICENSE
Version:	0.99.5
Built:	2025-02-22 03:30:01 UTC
Source:	https://github.com/bioc/CARDspa

The function to assign the spatial location information for each single cell

Description

The function to assign the spatial location information for each single cell

Usage

assign_sc_cords(mappint_spot_cell_cor, cords_new, numcell, sc_eset, ct_varname)
assign_sc_cords(mappint_spot_cell_cor, cords_new, numcell, sc_eset, ct_varname)

Arguments

`mappint_spot_cell_cor`	a mapped correlation matrix indicating the relashionship between each measured spatial location and the single cell in the scRNAseq reference
`cords_new`	output from the function get_high_res_cords
`numcell`	a numeric value indicating the number of single cells in each measured location, we suggest 20 for ST technology, 7 for 10x Viisum and 2 for Slide-seq
`sc_eset`	a single cell experiment object stored in CARD object
`ct_varname`	character, the name of the column in metaData that specifies the cell type annotation information, stroed in CARD object

Value

Return the assigned spatial location information for the mapped single cell

Spatially Informed Cell Type Deconvolution for Spatial Transcriptomics by CARD

Description

Spatially Informed Cell Type Deconvolution for Spatial Transcriptomics by CARD

Usage

CARD_deconvolution(
  sc_count,
  sc_meta,
  spatial_count,
  spatial_location,
  ct_varname,
  ct_select,
  sample_varname,
  mincountgene = 100,
  mincountspot = 5,
  sce = NULL,
  spe = NULL
)
CARD_deconvolution(
  sc_count,
  sc_meta,
  spatial_count,
  spatial_location,
  ct_varname,
  ct_select,
  sample_varname,
  mincountgene = 100,
  mincountspot = 5,
  sce = NULL,
  spe = NULL
)

Arguments

`sc_count`	Raw scRNA-seq count data, each column is a cell and each row is a gene.
`sc_meta`	data frame, with each row representing the cell type and/or sample information of a specific cell. The row names of this data frame should match exactly with the column names of the sc_count data
`spatial_count`	Raw spatial resolved transcriptomics data, each column is a spatial location, and each row is a gene.
`spatial_location`	data frame, with two columns representing the x and y coordinates of the spatial location. The rownames of this data frame should match eaxctly with the columns of the spatial_count.
`ct_varname`	character, the name of the column in metaData that specifies the cell type annotation information
`ct_select`	vector of cell type names that you are interested in to deconvolute, default as NULL. If NULL, then use all cell types provided by single cell dataset;
`sample_varname`	character,the name of the column in metaData that specifies the sample information. If NULL, we just use the whole as one sample.
`mincountgene`	Minimum counts for each gene
`mincountspot`	Minimum counts for each spatial location
`sce`	a `SingleCellExperiment` object containing scRNA-seq count data in the `counts` assay, and cell types and sample information in the colData.
`spe`	a `SpatialExperiment` object containing spatial data in the `counts` assay, and spatial coordinates in the spatialCoords.

Value

Returns a SpatialExperiment object with estimated cell type proportion stored in object$Proportion_CARD.

Examples

library(RcppML)
library(NMF)
library(RcppArmadillo)
data(spatial_count)
data(spatial_location)
data(sc_count)
data(sc_meta)
CARD_obj <- CARD_deconvolution(
    sc_count = sc_count,
    sc_meta = sc_meta,
    spatial_count = spatial_count,
    spatial_location = spatial_location,
    ct_varname = "cellType",
    ct_select = unique(sc_meta$cellType),
    sample_varname = "sampleInfo",
    mincountgene = 100,
    mincountspot = 5
)
library(RcppML)
library(NMF)
library(RcppArmadillo)
data(spatial_count)
data(spatial_location)
data(sc_count)
data(sc_meta)
CARD_obj <- CARD_deconvolution(
    sc_count = sc_count,
    sc_meta = sc_meta,
    spatial_count = spatial_count,
    spatial_location = spatial_location,
    ct_varname = "cellType",
    ct_select = unique(sc_meta$cellType),
    sample_varname = "sampleInfo",
    mincountgene = 100,
    mincountspot = 5
)

Construct an enhanced spatial expression map on the unmeasured tissue locations

Description

Construct an enhanced spatial expression map on the unmeasured tissue locations

Usage

CARD_imputation(CARD_object, num_grids, ineibor = 10, exclude = NULL)
CARD_imputation(CARD_object, num_grids, ineibor = 10, exclude = NULL)

Arguments

`CARD_object`	SpatialExperiment Object created by CARD_deconvolution with estimated cell type compositions on the original spatial resolved transcriptomics data.
`num_grids`	Initial number of newly grided spatial locations. The final number of newly grided spatial locations will be lower than this value since the newly grided locations outside the shape of the tissue will be filtered
`ineibor`	Numeric, number of neighbors used in the imputation on newly grided spatial locations, default is 10.
`exclude`	Vector, the rownames of spatial location data on the original resolution that you want to exclude. This is to avoid the weird detection of the shape.

Value

Return a SpatialExperiment object with the refined cell type compositions estimated for newly grided spots and the refined predicted gene expression (normalized).

Examples

data(spatial_count)
data(spatial_location)
data(sc_count)
data(sc_meta)
CARD_obj <- CARD_deconvolution(
    sc_count = sc_count,
    sc_meta = sc_meta,
    spatial_count = spatial_count,
    spatial_location = spatial_location,
    ct_varname = "cellType",
    ct_select = unique(sc_meta$cellType),
    sample_varname = "sampleInfo",
    mincountgene = 100,
    mincountspot = 5
)
CARD_obj <- CARD_imputation(
    CARD_obj,
    num_grids = 200,
    ineibor = 10,
    exclude = NULL
)

data(spatial_count)
data(spatial_location)
data(sc_count)
data(sc_meta)
CARD_obj <- CARD_deconvolution(
    sc_count = sc_count,
    sc_meta = sc_meta,
    spatial_count = spatial_count,
    spatial_location = spatial_location,
    ct_varname = "cellType",
    ct_select = unique(sc_meta$cellType),
    sample_varname = "sampleInfo",
    mincountgene = 100,
    mincountspot = 5
)
CARD_obj <- CARD_imputation(
    CARD_obj,
    num_grids = 200,
    ineibor = 10,
    exclude = NULL
)

Extension of CARD into a reference-free version of deconvolution: CARDfree.

Description

Extension of CARD into a reference-free version of deconvolution: CARDfree.

Usage

CARD_refFree(
  markerlist,
  spatial_count,
  spatial_location,
  mincountgene = 100,
  mincountspot = 5,
  spe = NULL
)
CARD_refFree(
  markerlist,
  spatial_count,
  spatial_location,
  mincountgene = 100,
  mincountspot = 5,
  spe = NULL
)

Arguments

`markerlist`	a list of marker genes, with each element of the list being the vector of cell type specific marker genes
`spatial_count`	Raw spatial resolved transcriptomics data, each column is a spatial location, and each row is a gene.
`spatial_location`	data frame, with two columns representing the x and y coordinates of the spatial location. The rownames of this data frame should match eaxctly with the columns of the spatial_count.
`mincountgene`	Minimum counts for each gene
`mincountspot`	Minimum counts for each spatial location
`spe`	a `SpatialExperiment` object containing spatial data in the `counts` assay, and spatial coordinates in the spatialCoords.

Value

Returns a SpatialExperiment object with estimated cell type proportion stored in object$Proportion_CARD. Because this is a reference-free version, the columns of estimated proportion is not cell type but cell type cluster

Examples

library(RcppML)
library(NMF)
library(RcppArmadillo)
data(markerList)
data(spatial_count)
data(spatial_location)
CARDfree_obj <- CARD_refFree(
markerlist = markerList[8:16],
spatial_count = spatial_count[1:2500, ],
spatial_location = spatial_location,
mincountgene = 100,
mincountspot = 5
)

library(RcppML)
library(NMF)
library(RcppArmadillo)
data(markerList)
data(spatial_count)
data(spatial_location)
CARDfree_obj <- CARD_refFree(
markerlist = markerList[8:16],
spatial_count = spatial_count[1:2500, ],
spatial_location = spatial_location,
mincountgene = 100,
mincountspot = 5
)

Extension of CARD into performing single cell Mapping from non-single cell spatial transcriptomics dataset.

Description

Extension of CARD into performing single cell Mapping from non-single cell spatial transcriptomics dataset.

Usage

CARD_scmapping(CARD_object, shapeSpot = "Square", numcell, ncore = 10)
CARD_scmapping(CARD_object, shapeSpot = "Square", numcell, ncore = 10)

Arguments

`CARD_object`	CARD object create by the CARD_deconvolution function.
`shapeSpot`	a character indicating whether the sampled spatial coordinates for single cells locating in a Square-like region or a Circle-like region. The center of this region is the measured spatial location in the non-single cell resolution spatial transcriptomics data. The default is 'Square', the other shape is 'Circle'
`numcell`	a numeric value indicating the number of single cells in each measured location, we suggest 20 for ST technology, 7 for 10x Viisum and 2 for Slide-seq
`ncore`	a numeric value indicating the number of cores used to accelerating the procedure

Value

Returns a SingleCellExperiment SCE object with the mapped expression at single cell resolution and the spatial location information of each single cell

Examples

library(SingleCellExperiment)
data(spatial_count)
data(spatial_location)
data(sc_count)
data(sc_meta)
CARD_obj <- CARD_deconvolution(
    sc_count = sc_count,
    sc_meta = sc_meta,
    spatial_count = spatial_count,
    spatial_location = spatial_location,
    ct_varname = "cellType",
    ct_select = unique(sc_meta$cellType),
    sample_varname = "sampleInfo",
    mincountgene = 100,
    mincountspot = 5
)
scMapping <- CARD_scmapping(
CARD_obj, 
shapeSpot = "Square", 
numcell = 20, 
ncore = 2)
print(scMapping)

library(SingleCellExperiment)
data(spatial_count)
data(spatial_location)
data(sc_count)
data(sc_meta)
CARD_obj <- CARD_deconvolution(
    sc_count = sc_count,
    sc_meta = sc_meta,
    spatial_count = spatial_count,
    spatial_location = spatial_location,
    ct_varname = "cellType",
    ct_select = unique(sc_meta$cellType),
    sample_varname = "sampleInfo",
    mincountgene = 100,
    mincountspot = 5
)
scMapping <- CARD_scmapping(
CARD_obj, 
shapeSpot = "Square", 
numcell = 20, 
ncore = 2)
print(scMapping)

Visualize the cell type proportion correlation

Description

Visualize the cell type proportion correlation

Usage

CARD_visualize_Cor(proportion, colors = colors)
CARD_visualize_Cor(proportion, colors = colors)

Arguments

`proportion`	Data frame, cell type proportion estimated by CARD in either original resolution or enhanced resolution.
`colors`	Vector of color names that you want to use, if NULL, we will use the default color scale c("#91a28c","white","#8f2c37")

Value

Returns a ggcorrplot figure.

Examples

library(ggplot2)
data(spatial_count)
data(spatial_location)
data(sc_count)
data(sc_meta)
CARD_obj <- CARD_deconvolution(
    sc_count = sc_count,
    sc_meta = sc_meta,
    spatial_count = spatial_count,
    spatial_location = spatial_location,
    ct_varname = "cellType",
    ct_select = unique(sc_meta$cellType),
    sample_varname = "sampleInfo",
    mincountgene = 100,
    mincountspot = 5
)
CARD_visualize_Cor(CARD_obj$Proportion_CARD, colors = NULL)
library(ggplot2)
data(spatial_count)
data(spatial_location)
data(sc_count)
data(sc_meta)
CARD_obj <- CARD_deconvolution(
    sc_count = sc_count,
    sc_meta = sc_meta,
    spatial_count = spatial_count,
    spatial_location = spatial_location,
    ct_varname = "cellType",
    ct_select = unique(sc_meta$cellType),
    sample_varname = "sampleInfo",
    mincountgene = 100,
    mincountspot = 5
)
CARD_visualize_Cor(CARD_obj$Proportion_CARD, colors = NULL)

Visualize the spatial distribution of cell type proportion

Description

Visualize the spatial distribution of cell type proportion

Usage

CARD_visualize_gene(
  spatial_expression,
  spatial_location,
  gene_visualize,
  colors = colors,
  NumCols
)
CARD_visualize_gene(
  spatial_expression,
  spatial_location,
  gene_visualize,
  colors = colors,
  NumCols
)

Arguments

`spatial_expression`	Data frame, spatial gene expression in either original resolution or enhanced resolution.
`spatial_location`	Data frame, spatial location information.
`gene_visualize`	Vector of selected gene names that are interested to visualize
`colors`	Vector of color names that you want to use, if NULL, we will use the default color scale in virdis palette
`NumCols`	Numeric, number of columns in the figure panel, it depends on the number of cell types you want to visualize.

Value

Returns a ggplot2 figure.

Examples

library(ggplot2)
library(SummarizedExperiment)
library(SpatialExperiment)
data(spatial_count)
data(spatial_location)
data(sc_count)
data(sc_meta)
CARD_obj <- CARD_deconvolution(
    sc_count = sc_count,
    sc_meta = sc_meta,
    spatial_count = spatial_count,
    spatial_location = spatial_location,
    ct_varname = "cellType",
    ct_select = unique(sc_meta$cellType),
    sample_varname = "sampleInfo",
    mincountgene = 100,
    mincountspot = 5
)
CARD_visualize_gene(
    spatial_expression = assays(CARD_obj)$spatial_countMat,
    spatial_location = spatialCoords(CARD_obj),
    gene_visualize = c("A4GNT", "AAMDC", "CD248"),
    colors = NULL,
    NumCols = 3
)

library(ggplot2)
library(SummarizedExperiment)
library(SpatialExperiment)
data(spatial_count)
data(spatial_location)
data(sc_count)
data(sc_meta)
CARD_obj <- CARD_deconvolution(
    sc_count = sc_count,
    sc_meta = sc_meta,
    spatial_count = spatial_count,
    spatial_location = spatial_location,
    ct_varname = "cellType",
    ct_select = unique(sc_meta$cellType),
    sample_varname = "sampleInfo",
    mincountgene = 100,
    mincountspot = 5
)
CARD_visualize_gene(
    spatial_expression = assays(CARD_obj)$spatial_countMat,
    spatial_location = spatialCoords(CARD_obj),
    gene_visualize = c("A4GNT", "AAMDC", "CD248"),
    colors = NULL,
    NumCols = 3
)

Visualize the spatial distribution of cell type proportion in a geom scatterpie plot

Description

Visualize the spatial distribution of cell type proportion in a geom scatterpie plot

Usage

CARD_visualize_pie(proportion, spatial_location, colors = NULL, radius = NULL)
CARD_visualize_pie(proportion, spatial_location, colors = NULL, radius = NULL)

Arguments

`proportion`	Data frame, cell type proportion estimated by CARD in either original resolution or enhanced resolution.
`spatial_location`	Data frame, spatial location information.
`colors`	Vector of color names that you want to use, if NULL, we will use the color palette "Spectral" from RColorBrewer package.
`radius`	Numeric value about the radius of each pie chart, if NULL, we will calculate it inside the function.

Value

Returns a ggplot2 figure.

Examples

library(ggplot2)
library(SpatialExperiment)
data(spatial_count)
data(spatial_location)
data(sc_count)
data(sc_meta)
CARD_obj <- CARD_deconvolution(
    sc_count = sc_count,
    sc_meta = sc_meta,
    spatial_count = spatial_count,
    spatial_location = spatial_location,
    ct_varname = "cellType",
    ct_select = unique(sc_meta$cellType),
    sample_varname = "sampleInfo",
    mincountgene = 100,
    mincountspot = 5
)
colors <- c(
    "#FFD92F", "#4DAF4A", "#FCCDE5", "#D9D9D9", "#377EB8", "#7FC97F",
    "#BEAED4", "#FDC086", "#FFFF99", "#386CB0", "#F0027F", "#BF5B17", 
    "#666666", "#1B9E77", "#D95F02", "#7570B3", "#E7298A", "#66A61E", 
    "#E6AB02", "#A6761D"
)
CARD_visualize_pie(
    proportion = CARD_obj$Proportion_CARD,
    spatial_location = spatialCoords(CARD_obj),
    colors = colors,
    radius = 0.52
)
library(ggplot2)
library(SpatialExperiment)
data(spatial_count)
data(spatial_location)
data(sc_count)
data(sc_meta)
CARD_obj <- CARD_deconvolution(
    sc_count = sc_count,
    sc_meta = sc_meta,
    spatial_count = spatial_count,
    spatial_location = spatial_location,
    ct_varname = "cellType",
    ct_select = unique(sc_meta$cellType),
    sample_varname = "sampleInfo",
    mincountgene = 100,
    mincountspot = 5
)
colors <- c(
    "#FFD92F", "#4DAF4A", "#FCCDE5", "#D9D9D9", "#377EB8", "#7FC97F",
    "#BEAED4", "#FDC086", "#FFFF99", "#386CB0", "#F0027F", "#BF5B17", 
    "#666666", "#1B9E77", "#D95F02", "#7570B3", "#E7298A", "#66A61E", 
    "#E6AB02", "#A6761D"
)
CARD_visualize_pie(
    proportion = CARD_obj$Proportion_CARD,
    spatial_location = spatialCoords(CARD_obj),
    colors = colors,
    radius = 0.52
)

Visualize the spatial distribution of cell type proportion

Description

Visualize the spatial distribution of cell type proportion

Usage

CARD_visualize_prop(
  proportion,
  spatial_location,
  ct_visualize = ct_visualize,
  colors = c("lightblue", "lightyellow", "red"),
  NumCols,
  pointSize = 3
)
CARD_visualize_prop(
  proportion,
  spatial_location,
  ct_visualize = ct_visualize,
  colors = c("lightblue", "lightyellow", "red"),
  NumCols,
  pointSize = 3
)

Arguments

`proportion`	Data frame, cell type proportion estimated by CARD in either original resolution or enhanced resolution.
`spatial_location`	Data frame, spatial location information.
`ct_visualize`	Vector of selected cell type names that are interested to visualize
`colors`	Vector of color names that you want to use, if NULL, we will use the default color scale c("lightblue","lightyellow","red")
`NumCols`	Numeric, number of columns in the figure panel, it depends on the number of cell types you want to visualize.
`pointSize`	Size of each point used for plotting

Value

Returns a ggplot2 figure.

Examples

library(ggplot2)
library(SpatialExperiment)
data(spatial_count)
data(spatial_location)
data(sc_count)
data(sc_meta)
CARD_obj <- CARD_deconvolution(
    sc_count = sc_count,
    sc_meta = sc_meta,
    spatial_count = spatial_count,
    spatial_location = spatial_location,
    ct_varname = "cellType",
    ct_select = unique(sc_meta$cellType),
    sample_varname = "sampleInfo",
    mincountgene = 100,
    mincountspot = 5
)
ct_visualize <- c(
    "Acinar_cells", "Cancer_clone_A", "Cancer_clone_B",
    "Ductal_terminal_ductal_like", "Ductal_CRISP3_high-centroacinar_like",
    "Ductal_MHC_Class_II", "Ductal_APOL1_high-hypoxic", "Fibroblasts"
)
CARD_visualize_prop(
    proportion = CARD_obj$Proportion_CARD,
    spatial_location = spatialCoords(CARD_obj),
    ct_visualize = ct_visualize,
    colors = c("lightblue", "lightyellow", "red"),
    NumCols = 4,
    pointSize = 3.0
)

library(ggplot2)
library(SpatialExperiment)
data(spatial_count)
data(spatial_location)
data(sc_count)
data(sc_meta)
CARD_obj <- CARD_deconvolution(
    sc_count = sc_count,
    sc_meta = sc_meta,
    spatial_count = spatial_count,
    spatial_location = spatial_location,
    ct_varname = "cellType",
    ct_select = unique(sc_meta$cellType),
    sample_varname = "sampleInfo",
    mincountgene = 100,
    mincountspot = 5
)
ct_visualize <- c(
    "Acinar_cells", "Cancer_clone_A", "Cancer_clone_B",
    "Ductal_terminal_ductal_like", "Ductal_CRISP3_high-centroacinar_like",
    "Ductal_MHC_Class_II", "Ductal_APOL1_high-hypoxic", "Fibroblasts"
)
CARD_visualize_prop(
    proportion = CARD_obj$Proportion_CARD,
    spatial_location = spatialCoords(CARD_obj),
    ct_visualize = ct_visualize,
    colors = c("lightblue", "lightyellow", "red"),
    NumCols = 4,
    pointSize = 3.0
)

Visualize the spatial distribution of two cell type proportions on the same plot

Description

Visualize the spatial distribution of two cell type proportions on the same plot

Usage

CARD_visualize_prop_2CT(
  proportion,
  spatial_location,
  ct2_visualize = ct2_visualize,
  colors = NULL
)
CARD_visualize_prop_2CT(
  proportion,
  spatial_location,
  ct2_visualize = ct2_visualize,
  colors = NULL
)

Arguments

`proportion`	Data frame, cell type proportion estimated by CARD in either original resolution or enhanced resolution.
`spatial_location`	Data frame, spatial location information.
`ct2_visualize`	Vector of selected two cell type names that are interested to visualize, here we only focus on two cell types
`colors`	list of color names that you want to use for each cell type, if NULL, we will use the default color scale list list(c("lightblue","lightyellow","red"),c("lightblue","lightyellow","black")

Value

Returns a ggplot2 figure.

Examples

library(ggplot2)
library(SpatialExperiment)
data(spatial_count)
data(spatial_location)
data(sc_count)
data(sc_meta)
CARD_obj <- CARD_deconvolution(
    sc_count = sc_count,
    sc_meta = sc_meta,
    spatial_count = spatial_count,
    spatial_location = spatial_location,
    ct_varname = "cellType",
    ct_select = unique(sc_meta$cellType),
    sample_varname = "sampleInfo",
    mincountgene = 100,
    mincountspot = 5
)
CARD_visualize_prop_2CT(
    proportion = CARD_obj$Proportion_CARD,
    spatial_location = spatialCoords(CARD_obj),
    ct2_visualize = c("Cancer_clone_A", "Cancer_clone_B"),
    colors = list(c("lightblue", "lightyellow", "red"), c(
        "lightblue", "lightyellow",
        "black"
    ))
)

library(ggplot2)
library(SpatialExperiment)
data(spatial_count)
data(spatial_location)
data(sc_count)
data(sc_meta)
CARD_obj <- CARD_deconvolution(
    sc_count = sc_count,
    sc_meta = sc_meta,
    spatial_count = spatial_count,
    spatial_location = spatial_location,
    ct_varname = "cellType",
    ct_select = unique(sc_meta$cellType),
    sample_varname = "sampleInfo",
    mincountgene = 100,
    mincountspot = 5
)
CARD_visualize_prop_2CT(
    proportion = CARD_obj$Proportion_CARD,
    spatial_location = spatialCoords(CARD_obj),
    ct2_visualize = c("Cancer_clone_A", "Cancer_clone_B"),
    colors = list(c("lightblue", "lightyellow", "red"), c(
        "lightblue", "lightyellow",
        "black"
    ))
)

Each CARD object has a number of slots which store information. Key slots to access are listed below.

Description

Each CARD object has a number of slots which store information. Key slots to access are listed below.

Value

Return an object of CARD class

Slots

sc_eset: The filtered scRNA-seq data along with meta data stored in the format of SingleCellExperiment.
spatial_countMat: The filtered spatial count data.
spatial_location: The weights for combining p-values from multiple kernels.
Proportion_CARD: The estimated cell type proportion by CARD with each row is a spatial location and each column is a cell type.
project: The name of the project, default is deconvolution.
info_parameters: The paramters that are used in model fitting.
algorithm_matrix: The intermediate matrices that are used in the model fitting step.
refined_prop: The refined cell type proportion matrix estimated by CARD for the newly grided spatial locations. The number of initial grids are defined by the user.
refined_expression: The refined predicted expression matrix (normalized) estimated by CARD for the newly grided spatial locations. The number of initial grids are defined by the user.

SpatialDeconv function based on Conditional Autoregressive model

Description

SpatialDeconv function based on Conditional Autoregressive model

Usage

CARDfree(
  XinputIn,
  UIn,
  WIn,
  phiIn,
  max_iterIn,
  epsilonIn,
  initV,
  initb,
  initSigma_e2,
  initLambda
)
CARDfree(
  XinputIn,
  UIn,
  WIn,
  phiIn,
  max_iterIn,
  epsilonIn,
  initV,
  initb,
  initSigma_e2,
  initLambda
)

Arguments

`XinputIn`	The input of normalized spatial data
`UIn`	The input of cell type specific basis matrix B
`WIn`	The constructed W weight matrix from Gaussian kernel
`phiIn`	The phi value
`max_iterIn`	Maximum iterations
`epsilonIn`	epsilon for convergence
`initV`	Initial matrix of cell type compositions V
`initb`	Initial vector of cell type specific intercept
`initSigma_e2`	Initial value of residual variance
`initLambda`	Initial vector of cell type sepcific scalar.

Value

A list

Each CARDfree object has a number of slots which store information. Key slots to access are listed below.

Description

Each CARDfree object has a number of slots which store information. Key slots to access are listed below.

Value

Return an object of CARDfree class

Slots

spatial_countMat: The filtered spatial count data.
spatial_location: The weights for combining p-values from multiple kernels.
Proportion_CARD: The estimated cell type proportion by CARD with each row is a spatial location and each column is a cell type.
estimated_refMatrix: The estimated reference matrix by CARDfree with each row represents a gene and each column represents a cell type cluster.
project: The name of the project, default is deconvolution.
markerList: The nlist of cell type specific markers, with each element represents the vector of cell type specific markers
info_parameters: The paramters that are used in model fitting.
algorithm_matrix: The intermediate matrices that are used in the model fitting step.
refined_prop: The refined cell type proportion matrix estimated by CARD for the newly grided spatial locations. The number of initial grids are defined by the user.
refined_expression: The refined predicted expression matrix (normalized) estimated by CARD for the newly grided spatial locations. The number of initial grids are defined by the user.

SpatialDeconv function based on Conditional Autoregressive model

Description

SpatialDeconv function based on Conditional Autoregressive model

Usage

CARDref(
  XinputIn,
  UIn,
  WIn,
  phiIn,
  max_iterIn,
  epsilonIn,
  initV,
  initb,
  initSigma_e2,
  initLambda
)
CARDref(
  XinputIn,
  UIn,
  WIn,
  phiIn,
  max_iterIn,
  epsilonIn,
  initV,
  initb,
  initSigma_e2,
  initLambda
)

Arguments

`XinputIn`	The input of normalized spatial data
`UIn`	The input of cell type specific basis matrix B
`WIn`	The constructed W weight matrix from Gaussian kernel
`phiIn`	The phi value
`max_iterIn`	Maximum iterations
`epsilonIn`	epsilon for convergence
`initV`	Initial matrix of cell type compositions V
`initb`	Initial vector of cell type specific intercept
`initSigma_e2`	Initial value of residual variance
`initLambda`	Initial vector of cell type sepcific scalar.

Value

A list

Construct the mean gene expression basis matrix (B), this is the faster version

Description

Construct the mean gene expression basis matrix (B), this is the faster version

Usage

create_ref(sc_eset, ct_select = NULL, ct_varname, sample_varname = NULL)
create_ref(sc_eset, ct_select = NULL, ct_varname, sample_varname = NULL)

Arguments

`sc_eset`	S4 class for storing data from single-cell experiments. This format is usually created by the package SingleCellExperiment with stored counts, along with the usual metadata for genes and cells.
`ct_select`	vector of cell type names that you are interested in to deconvolute, default as NULL. If NULL, then use all cell types provided by single cell dataset;
`ct_varname`	character, the name of the column in metaData that specifies the cell type annotation information
`sample_varname`	character,the name of the column in metaData that specifies the sample information. If NULL, we just use the whole as one sample.

Value

Return a list of basis (B) matrix

Create the CARD object

Description

Create the CARD object

Usage

createCARDfreeObject(
  markerlist,
  spatial_count,
  spatial_location,
  mincountgene = 100,
  mincountspot = 5,
  spe = NULL
)
createCARDfreeObject(
  markerlist,
  spatial_count,
  spatial_location,
  mincountgene = 100,
  mincountspot = 5,
  spe = NULL
)

Arguments

`markerlist`	a list of marker genes, with each element of the list being the vector of cell type specific marker genes
`spatial_count`	Raw spatial resolved transcriptomics data, each column is a spatial location, and each row is a gene.
`spatial_location`	data frame, with two columns representing the x and y coordinates of the spatial location. The rownames of this data frame should match eaxctly with the columns of the spatial_count.
`mincountgene`	Minimum counts for each gene
`mincountspot`	Minimum counts for each spatial location
`spe`	a `SpatialExperiment` object containing spatial data in the `counts` assay, and spatial coordinates in the spatialCoords.

Value

Returns CARDfree object with filtered spatial count and marker gene list.

Create the CARD object

Description

Create the CARD object

Usage

createCARDObject(
  sc_count,
  sc_meta,
  spatial_count,
  spatial_location,
  ct_varname,
  ct_select,
  sample_varname,
  mincountgene = 100,
  mincountspot = 5,
  sce = NULL,
  spe = NULL
)
createCARDObject(
  sc_count,
  sc_meta,
  spatial_count,
  spatial_location,
  ct_varname,
  ct_select,
  sample_varname,
  mincountgene = 100,
  mincountspot = 5,
  sce = NULL,
  spe = NULL
)

Arguments

`sc_count`	Raw scRNA-seq count data, each column is a cell and each row is a gene.
`sc_meta`	data frame, with each row representing the cell type and/or sample information of a specific cell. The row names of this data frame should match exactly with the column names of the sc_count data
`spatial_count`	Raw spatial resolved transcriptomics data, each column is a spatial location, and each row is a gene.
`spatial_location`	data frame, with two columns representing the x and y coordinates of the spatial location. The rownames of this data frame should match eaxctly with the columns of the spatial_count.
`ct_varname`	character, the name of the column in metadata that specifies the cell type annotation information
`ct_select`	vector of cell type names that you are interested in to deconvolute, default as NULL. If NULL, then use all cell types provided by single cell dataset;
`sample_varname`	character,the name of the column in metadata that specifies the sample information. If NULL, we just use the whole as one sample.
`mincountgene`	Minimum counts for each gene
`mincountspot`	Minimum counts for each spatial location
`sce`	a `SingleCellExperiment` object containing scRNA-seq count data in the `counts` assay, and cell types and sample information in the colData.
`spe`	a `SpatialExperiment` object containing spatial data in the `counts` assay, and spatial coordinates in the spatialCoords.

Value

Returns CARD object with filtered spatial count and single cell RNA-seq dataset.

The function to sample the spatial location information for each single cell

Description

The function to sample the spatial location information for each single cell

Usage

get_high_res_cords(cords, numcell, shape = "Square")
get_high_res_cords(cords, numcell, shape = "Square")

Arguments

`cords`	The spatial location information in the measure spatial locations, with the first and second columns represent the 2-D x-y coordinate system
`numcell`	a numeric value indicating the number of single cells in each measured location, we suggest 20 for ST technology, 7 for 10x Viisum and 2 for Slide-seq
`shape`	a character indicating whether the sampled spatial coordinates for single cells locating in a Square-like region or a Circle-like region. The center of this region is the measured spatial location in the non-single cell resolution spatial transcriptomics data. The default is 'Square', the other shape is 'Circle'

Value

Returns a dataframe with the sampled spatial location information for each single cell

The function to estimate the cell type composition signature for each single cell in the scRNaseq reference data

Description

The function to estimate the cell type composition signature for each single cell in the scRNaseq reference data

Usage

get_weight_for_cell(sc_eset, ct_varname, ct_select, sample_varname, B)
get_weight_for_cell(sc_eset, ct_varname, ct_select, sample_varname, B)

Arguments

`sc_eset`	the sc_eset stored in the CARD object
`ct_varname`	character, the name of the column in metaData that specifies the cell type annotation information, stored in the CARD object
`ct_select`	vector of cell type names that you are interested in to deconvolute, default as NULL. stored in the CARD object
`sample_varname`	character,the name of the column in metaData that specifies the sample information. stored in the CARD object
`B`	reference basis matrix stored in the CARD object.

Value

Returns a matrix of the cell type composition signature for each single cell in the scRNaseq reference

marker gene list

Description

The marker gene list is a list format with each element of the list being the cell type specific gene markers.

Usage

data(markerList)
data(markerList)

Format

An object of class list of length 20.

Imputation and Construction of High-Resolution Spatial Maps for Cell Type Composition and Gene Expression by the spatial correlation structure between original spatial locations and new grided spatial locations

Description

Imputation and Construction of High-Resolution Spatial Maps for Cell Type Composition and Gene Expression by the spatial correlation structure between original spatial locations and new grided spatial locations

Usage

mvn_cv(
  vtrain,
  location_orig,
  train_ind,
  test_ind,
  B,
  xinput_norm,
  optimal_b,
  optimal_phi,
  lambda,
  ineibor
)
mvn_cv(
  vtrain,
  location_orig,
  train_ind,
  test_ind,
  B,
  xinput_norm,
  optimal_b,
  optimal_phi,
  lambda,
  ineibor
)

Arguments

`vtrain`	Matrix, estimated V matrix from CARD
`location_orig`	Data frame, spatial location data frame of the original spatial resolved transcriptomics dataset, stored in the spatialCoords(CARD_object)
`train_ind`	Vector, index of the original spatial locations
`test_ind`	Vector, index of the newly grided spatial locations
`B`	Matrix, used in the deconvolution as the reference basis matrix
`xinput_norm`	Matrix, used in the deconvolution as the normalized spatial count data
`optimal_b`	Vector, vector of the intercept for each cel type estimated based on the original spatial resolution
`optimal_phi`	Numeric, the optimal phi value stored in CARD_object
`lambda`	Vector, vector of cell type specific scalar in the CAR model
`ineibor`	Numeric, number of neighbors used in the imputation on newly grided spatial locations, default is 10.

Value

Return a list with the imputed Cell type composition Vtest matrix on the newly grided spatial locations and predicted normalized gene expression

Normalize the new spatial locations without changing the shape and relative positions

Description

Normalize the new spatial locations without changing the shape and relative positions

Usage

norm_coords_train_test(location_orig, train_ind, test_ind)
norm_coords_train_test(location_orig, train_ind, test_ind)

Arguments

`location_orig`	Data frame, spatial location data frame of the original spatial resolved transcriptomics dataset, stored in the spatialCoords(CARD_object)
`train_ind`	Vector, Index of the original spatial locations
`test_ind`	Vector, Index of the newly grided spatial locations

Value

Return the normalized spatial location data frame

Make new spatial locations on unmeasured tissue through grids.

Description

Make new spatial locations on unmeasured tissue through grids.

Usage

sample_grid_within(location, num_sample, concavity = 2)
sample_grid_within(location, num_sample, concavity = 2)

Arguments

`location`	Data frame, spatial location data frame of the original spatial resolved transcriptomics dataset, stored in the spatialCoords(CARD_object)
`num_sample`	Numeric, approximate number of cells in grid within the shape of the spatial location data frame
`concavity`	Numeric, a relative measure of concavity. The default is 2.0, which can prodecure detailed enough shapes. Infinity results in a convex hull while 1 results in a more detailed shape.

Value

Return a list of data frame with newly grided points

scRNA-seq count data

Description

The scRNA-seq count data must be in the format of matrix or sparseMatrix, while each row represents a gene and each column represents a cell.

Usage

data(sc_count)
data(sc_count)

Format

An object of class dgCMatrix with 7000 rows and 1926 columns.

scRNAseq meta data

Description

The scRNAseq meta data must be in the format of data frame while each row represents a cell. The rownames of the scRNAseq meta data should match exactly with the column names of the scRNAseq count data. The sc_meta data must contain the column indicating the cell type assignment for each cell (e.g., “cellType” column in the example sc_meta data). Sample/subject information should be provided, if there is only one sample, we can add a column by sc_meta$sampleInfo = "sample1".

Usage

data(sc_meta)
data(sc_meta)

Format

An object of class data.frame with 1926 rows and 3 columns.

Quality control of scRNA-seq count data

Description

Quality control of scRNA-seq count data

Usage

sc_QC(
  counts_in,
  metadata,
  ct_varname,
  ct_select,
  sample_varname = NULL,
  min_cells = 0,
  min_genes = 0
)
sc_QC(
  counts_in,
  metadata,
  ct_varname,
  ct_select,
  sample_varname = NULL,
  min_cells = 0,
  min_genes = 0
)

Arguments

`counts_in`	Raw scRNAseq count data, each column is a cell and each row is a gene.
`metadata`	data frame, metadata with "ct_varname" specify the cell type annotation information and "sample_varname" specify the sample information
`ct_varname`	character, the name of the column in metadata that specifies the cell type annotation information
`ct_select`	vector of cell type names that you are interested in to deconvolute, default as NULL. If NULL, then use all cell types provided by single cell dataset;
`sample_varname`	character,the name of the column in metadata that specifies the sample information. If NULL, we just use the whole as one sample.
`min_cells`	numeric, we filtered out the non-expressed cells.
`min_genes`	numeric we filtered out the non-expressed genes

Value

Return the filtered scRNA-seq data and meta data stored in a S4 class (SingleCellExperiment)

Select Informative Genes used in the deconvolution

Description

Select Informative Genes used in the deconvolution

Usage

select_info(basis, sc_eset, commongene, ct_select, ct_varname)
select_info(basis, sc_eset, commongene, ct_select, ct_varname)

Arguments

`basis`	Reference basis matrix.
`sc_eset`	scRNAseq data along with meta data stored in the S4 class format (SingleCellExperiment).
`commongene`	common genes between scRNAseq count data and spatial resolved transcriptomics data.
`ct_select`	vector of cell type names that you are interested in to deconvolute, default as NULL. If NULL, then use all cell types provided by single cell dataset;
`ct_varname`	character, the name of the column in metaData that specifies the cell type annotation information

Value

a vector of informative genes selected

Show method for the CARD class

Description

This method provides a concise summary of an object of class CARD, displaying key information including the project name, the number of spots, the number of cell types, and a sample of the Proportion_CARD matrix.

Usage

## S4 method for signature 'CARD'
show(object)
## S4 method for signature 'CARD'
show(object)

Arguments

object

An object of class CARD.

Value

A concise summary of the CARD object is printed to the console.

Show method for the CARDfree class

Description

This method provides a concise summary of an object of class CARDfree, displaying key information including the project name, the number of spots, the number of cell types, and a sample of the Proportion_CARD matrix.

Usage

## S4 method for signature 'CARDfree'
show(object)
## S4 method for signature 'CARDfree'
show(object)

Arguments

object

An object of class CARDfree.

Value

A concise summary of the CARDfree object is printed to the console.

Calculate the variance covariance matrix used in the imputation of the new grided locations

Description

Calculate the variance covariance matrix used in the imputation of the new grided locations

Usage

Sigma(location_orig, train_ind, test_ind, optimal_phi, ineibor)
Sigma(location_orig, train_ind, test_ind, optimal_phi, ineibor)

Arguments

`location_orig`	Data frame, spatial location data frame of the original spatial resolved transcriptomics dataset, stored in the spatialCoords(CARD_object)
`train_ind`	Vector, index of the original spatial locations
`test_ind`	Vector, index of the newly grided spatial locations
`optimal_phi`	Numeric, the optimal phi value stored in CARD_object
`ineibor`	Numeric, number of neighbors used in the imputation on newly grided spatial locations, default is 10.

Value

Return a list with the imputed Cell type composition Vtest matrix on the newly grided spatial locations and predicted normalized gene expression

Spatial transcriptomics count data

Description

The spatial transcriptomics count data must be in the format of matrix or sparseMatrix, while each row represents a gene and each column represents a spatial location. The column names of the spatial data can be in the “XcoordxYcoord” (i.e., 10x10) format, but you can also maintain your original spot names, for example, barcode names.

Usage

data(spatial_count)
data(spatial_count)

Format

An object of class dgCMatrix with 11000 rows and 428 columns.

Spatial location data

Description

The spatial location data must be in the format of data frame while each row represents a spatial location, the first column represents the x coordinate and the second column represents the y coordinate. The rownames of the spatial location data frame should match exactly with the column names of the spatial_count.

Usage

data(spatial_location)
data(spatial_location)

Format

An object of class data.frame with 428 rows and 2 columns.

Package 'CARDspa'

Help Index

The function to assign the spatial location information for each single cell

Description

Usage

Arguments

Value

Spatially Informed Cell Type Deconvolution for Spatial Transcriptomics by CARD

Description

Usage

Arguments

Value

Examples

Construct an enhanced spatial expression map on the unmeasured tissue locations

Description

Usage

Arguments

Value

Examples

Extension of CARD into a reference-free version of deconvolution: CARDfree.

Description

Usage

Arguments

Value

Examples

Extension of CARD into performing single cell Mapping from non-single cell spatial transcriptomics dataset.

Description

Usage

Arguments

Value

Examples

Visualize the cell type proportion correlation

Description

Usage

Arguments

Value

Examples

Visualize the spatial distribution of cell type proportion

Description

Usage

Arguments

Value

Examples

Visualize the spatial distribution of cell type proportion in a geom scatterpie plot

Description

Usage

Arguments

Value

Examples

Visualize the spatial distribution of cell type proportion

Description

Usage

Arguments

Value

Examples

Visualize the spatial distribution of two cell type proportions on the same plot

Description

Usage

Arguments

Value

Examples

Each CARD object has a number of slots which store information. Key slots to access are listed below.

Description

Value

Slots

SpatialDeconv function based on Conditional Autoregressive model

Description

Usage

Arguments

Value

Each CARDfree object has a number of slots which store information. Key slots to access are listed below.

Description

Value

Slots

SpatialDeconv function based on Conditional Autoregressive model

Description

Usage

Arguments

Value

Construct the mean gene expression basis matrix (B), this is the faster version