| Title: | Spatial Component Analysis for Spatial Sequencing Data |
| Version: | 1.0.0 |
| Description: | Spatial components offer tools for dimension reduction and spatially variable gene detection for high dimensional spatial transcriptomics data. Construction of a projection onto low-dimensional feature space of spatially dependent metagenes offers pre-processing to clustering, testing for spatial variability and denoising of spatial expression patterns. For more details, see Koehler et al. (2026) <doi:10.1093/bioinformatics/btag052>. |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.3 |
| LinkingTo: | Rcpp, RcppEigen |
| Depends: | R (≥ 4.2.3) |
| Imports: | ggplot2, Seurat (≥ 5.3.0), tibble, ggforce, methods, rARPACK, tidyr, mgcv, scales, Matrix (≥ 1.5) |
| License: | MIT + file LICENSE |
| Suggests: | testthat (≥ 3.0.0) |
| Config/testthat/edition: | 3 |
| NeedsCompilation: | yes |
| Packaged: | 2026-03-26 15:46:00 UTC; koehler |
| Author: | David Köhler 
[aut, cre] |
| Maintainer: | David Köhler <koehler@imbie.uni-bonn.de> |
| Repository: | CRAN |
| Date/Publication: | 2026-03-31 10:00:14 UTC |
Orthogonalization
Description
Orthogonalization
Usage
.orthogonalizeA(X, A, nSpacs, tol = .Machine$double.eps^0.5)
Arguments
X |
Projection to orthogonalize |
A |
GraphLaplacian |
nSpacs |
number of Spacs to orthogonalize |
tol |
tolerance |
Value
An orthogonolized A matrix.
Compute the spatial variable genes of a SpaCoObject after runinng runSCA
Description
Compute the spatial variable genes of a SpaCoObject after runinng runSCA
Usage
SVGTest(SpaCoObject, adjustMethod = "holm")
Arguments
SpaCoObject |
SpaCoObject to compute spatially variable genes of. |
adjustMethod |
method for p-value adjustment. See p.adjust function. |
Value
returns a data frame of spatial variable genes and their p-Values.
SpaCo
Description
SpaCo
Usage
SpaCo(
SpaCoObject,
PC_criterion = "percent",
PC_value = 0.95,
set_nspacs = NULL,
nSim = 1000,
nSpacQuantile = 0.05,
reducedSpots = FALSE,
nReduce = 1000
)
Arguments
SpaCoObject |
Object of class |
PC_criterion |
criterion on which to select number of principal components for initial covariance matrix reconstruction; either "number" to select a number of PCs or "percent" to select number of PCs to explain specified amount of data variance |
PC_value |
Value to specify number of PCs or desired level of explained variance, see "PC_criterion" |
set_nspacs |
Boolean if number of relevant spacs is to be computed. Increases run time significantly |
nSim |
Number of simulations for computation of spac number |
nSpacQuantile |
Quantile to use as cutoff for spac number |
reducedSpots |
Should algorithm be run on a subset of spots? Default = FALSE |
nReduce |
Number of spots to sample to run algorithm on subset if |
Value
Returns a SpaCoObject filled with the result of the spatial component analysis.
Create a constructor function that creates an object of class SpaCoObject
Description
Create a constructor function that creates an object of class SpaCoObject
Usage
SpaCoObject(neighbours, data, coordinates)
Arguments
neighbours |
Binary matrix with weights describing if cells are to be considered neighbours or not depending on the defined distance. |
data |
Matrix with normalized and scaled gene counts. Rows as cells and genes as columns |
coordinates |
Matri with the cell coordinates on the slides. Rows and Columns in the 10x Visium case. |
Value
Returns a SpaCoObject with the given slots filled
Plot SPaCo meta genes.
Description
Plot SPaCo meta genes.
Usage
Spaco_plot(SpaCoObject, spac = 1)
Arguments
SpaCoObject |
SpacoObject with computed projections |
spac |
component to plot |
Value
returns a ggplot object with the meta gene expression.
create_SpaCoObject_from_KNN
Description
create_SpaCoObject_from_KNN
Usage
create_SpaCoObject_from_KNN(SeuratObject, n = 10)
Arguments
SeuratObject |
Seurat object to export kNN-graph from. |
n |
Number of neighbors to consider. |
Value
Returns a SPaCoObject with the SCT data and the kNN-graph as neighborhood matrix.
computes smoothed gene profiles of genes present in the data.
Description
computes smoothed gene profiles of genes present in the data.
Usage
denoise_profiles(SpaCoObject)
Arguments
SpaCoObject |
Spaco object to compute profiles of. |
Value
smoothed gene profiles in the SpaCoObject.
Plot denoised gene expression
Description
Plot denoised gene expression
Usage
denoised_projection_plot(SpaCoObject, feature = NULL)
Arguments
SpaCoObject |
SpacoObject with computed projections |
feature |
Gene for which to plot denoised projection |
Value
returns a ggplot object with the denoised gene expression.
Multiply two matrices using Eigen library
Description
This function multiplies two matrices using the Eigen library, which provides fast linear algebra operations.
Usage
eigenMapMatMult(A, B)
Arguments
A |
a matrix |
B |
a matrix |
Value
the product of A and B
Plot gene expression
Description
Plot gene expression
Usage
feature_plot(SpaCoObject, feature)
Arguments
SpaCoObject |
SpacoObject with computed projections |
feature |
Gene to plot |
Value
returns a ggplot object with gene expression.
Read in 10x Visium spatial transcriptomics data
Description
Read in 10x Visium spatial transcriptomics data
Usage
read_10x_for_spaco(
data_dir,
slice,
filename,
variable_features_n = variable_features_n,
spatial_file = spatial_file,
vars_to_regress = NULL
)
Arguments
data_dir |
Directory containing the H5 file specified by file name and the image data in a sub directory called spatial |
slice |
Name for the stored image of the tissue slice |
filename |
Filename of data to be read |
variable_features_n |
Number of most variable features to keep. |
spatial_file |
Name of csv file from which to read tissue positions. |
vars_to_regress |
Names of features to be regressed against using PercentageFeatureSet |
Value
Retuns a ready to run SPaCoObject.
Set projections of Spaco Object
Description
Set projections of Spaco Object
Usage
set_projection(SpaCoObject, data, neighborhood = SpaCoObject@neighbours)
Arguments
SpaCoObject |
Fitted object of class |
data |
gene expression data matrix; p genes as columns, n loci as rows |
neighborhood |
neighborhood matrix of the new data. Must be of dimension n times n, where n is the number of columns of |
Value
Returns the SpaCoObject with the updated data, neighborhood matrix, and projection matrix.
Wrapper to transform existing Seurat object into an SpaCoObject.
Description
Wrapper to transform existing Seurat object into an SpaCoObject.
Usage
seurat_to_spaco(SeuratObject, assay = "SCT", n_image = 1, layer = "scale.data")
Arguments
SeuratObject |
Seurat object to export |
assay |
Assay to export from the Seurat object. Default is SCT assay. |
n_image |
Number of the image to export from Seurat object. Only relevant if Seurat object contains multiple images. Default is 1. |
layer |
Which layer to export data from. Default is scale.data. |
Value
Returns a SpaCoObject with all slots filled necessary to perform the spatial component analysis.
transfer computed spatial components to existing Seurat object.
Description
transfer computed spatial components to existing Seurat object.
Usage
spacs_to_seurat(SpaCoObject, SeuratObject, nSpacs = SpaCoObject@nSpacs)
Arguments
SpaCoObject |
SpaCoObject to export spatial components from. |
SeuratObject |
Seurat object to add spatial components to. |
nSpacs |
Number of Spacs which are to be projected on for dimension reduction |
Value
Returns a Seurat Object with the spatial components projections in the dimensional reduction slot.
Filtering function to remove cells without neighbours in defined distance from existing Seurat object to be conformable with existing SpaCoObject.
Description
Filtering function to remove cells without neighbours in defined distance from existing Seurat object to be conformable with existing SpaCoObject.
Usage
subset_non_neighbour_cells(SpaCoObject, SeuratObject)
Arguments
SpaCoObject |
SpaCoObject to integrate into Seurat object. |
SeuratObject |
Seurat object to be filtered. |
Value
Returns a Seurat object with cells filtered to match SpaCoObject.