1 Introduction

SpatialDE by Svensson et al., 2018, is a method to identify spatially variable genes (SVGs) in spatially resolved transcriptomics data.

2 Installation

You can install spatialDE from Bioconductor with the following code:

if (!requireNamespace("BiocManager", quietly = TRUE)) {
    install.packages("BiocManager")
}
BiocManager::install("spatialDE")

3 Example: Mouse Olfactory Bulb

Reproducing the example analysis from the original SpatialDE Python package.

library(spatialDE)
library(ggplot2)

3.1 Load data

Files originally retrieved from SpatialDE GitHub repository from the following links: https://github.com/Teichlab/SpatialDE/blob/master/Analysis/MouseOB/data/Rep11_MOB_0.csv https://github.com/Teichlab/SpatialDE/blob/master/Analysis/MouseOB/MOB_sample_info.csv

# Expression file used in python SpatialDE. 
data("Rep11_MOB_0")

# Sample Info file used in python SpatialDE
data("MOB_sample_info")

The Rep11_MOB_0 object contains spatial expression data for 16218 genes on 262 spots, with genes as rows and spots as columns.

Rep11_MOB_0[1:5, 1:5]
#>         16.92x9.015 16.945x11.075 16.97x10.118 16.939x12.132 16.949x13.055
#> Nrf1              1             0            0             1             0
#> Zbtb5             1             0            1             0             0
#> Ccnl1             1             3            1             1             0
#> Lrrfip1           2             2            0             0             3
#> Bbs1              1             2            0             4             0
dim(Rep11_MOB_0)
#> [1] 16218   262

The MOB_sample_info object contains a data.frame with coordinates for each spot.

head(MOB_sample_info)

3.1.1 Filter out pratically unobserved genes

Rep11_MOB_0 <- Rep11_MOB_0[rowSums(Rep11_MOB_0) >= 3, ]

3.1.2 Get total_counts for every spot

Rep11_MOB_0 <- Rep11_MOB_0[, row.names(MOB_sample_info)]
MOB_sample_info$total_counts <- colSums(Rep11_MOB_0)
head(MOB_sample_info)

3.1.3 Get coordinates from MOB_sample_info

X <- MOB_sample_info[, c("x", "y")]
head(X)

3.2 stabilize

The SpatialDE method assumes normally distributed data, so we stabilize the variance of the negative binomial distributed counts data using Anscombe’s approximation. The stabilize() function takes as input a data.frame of expression values with samples in columns and genes in rows. Thus, in this case, we have to transpose the data.

norm_expr <- stabilize(Rep11_MOB_0)
norm_expr[1:5, 1:5]
#>         16.92x9.015 16.945x11.075 16.97x10.118 16.939x12.132 16.949x13.055
#> Nrf1       1.227749     0.8810934    0.8810934     1.2277491     0.8810934
#> Zbtb5      1.227749     0.8810934    1.2277491     0.8810934     0.8810934
#> Ccnl1      1.227749     1.6889027    1.2277491     1.2277491     0.8810934
#> Lrrfip1    1.484676     1.4846765    0.8810934     0.8810934     1.6889027
#> Bbs1       1.227749     1.4846765    0.8810934     1.8584110     0.8810934

3.3 regress_out

Next, we account for differences in library size between the samples by regressing out the effect of the total counts for each gene using linear regression.

resid_expr <- regress_out(norm_expr, sample_info = MOB_sample_info)
resid_expr[1:5, 1:5]
#>         16.92x9.015 16.945x11.075 16.97x10.118 16.939x12.132 16.949x13.055
#> Nrf1    -0.75226761    -1.2352000   -1.0164479    -0.7903289    -1.0973214
#> Zbtb5    0.09242373    -0.3323719    0.1397144    -0.2760560    -0.2533134
#> Ccnl1   -2.77597164    -2.5903783   -2.6092013    -2.8529340    -3.1193883
#> Lrrfip1 -1.92331333    -2.1578718   -2.3849405    -2.5924072    -1.7163300
#> Bbs1    -1.12186064    -1.0266476   -1.3706460    -0.5363646    -1.4666155

3.4 run

To reduce running time, the SpatialDE test is run on a subset of 1000 genes. Running it on the complete data set takes about 10 minutes.

# For this example, run spatialDE on the first 1000 genes
sample_resid_expr <- head(resid_expr, 1000)

results <- spatialDE::run(sample_resid_expr, coordinates = X)
head(results[order(results$qval), ])

3.6 spatial_patterns

Furthermore, we can group spatially variable genes (SVGs) into spatial patterns using automatic expression histology (AEH).

sp <- spatial_patterns(
    sample_resid_expr,
    coordinates = X,
    de_results = de_results,
    n_patterns = 4L, length = 1.5
)
sp$pattern_results

3.7 Plots

Visualizing one of the most significant genes.

gene <- "Pcp4"

ggplot(data = MOB_sample_info, aes(x = x, y = y, color = norm_expr[gene, ])) +
    geom_point(size = 7) +
    ggtitle(gene) +
    scale_color_viridis_c() +
    labs(color = gene)

3.7.1 Plot Spatial Patterns of Multiple Genes

As an alternative to the previous figure, we can plot multiple genes using the normalized expression values.

ordered_de_results <- de_results[order(de_results$qval), ]

multiGenePlots(norm_expr,
    coordinates = X,
    ordered_de_results[1:6, ]$g,
    point_size = 4,
    viridis_option = "D",
    dark_theme = FALSE
)

3.8 Plot Fraction Spatial Variance vs Q-value

FSV_sig(results, ms_results)
#> Warning: ggrepel: 8 unlabeled data points (too many overlaps). Consider
#> increasing max.overlaps

4 SpatialExperiment integration

The SpatialDE workflow can also be executed with a SpatialExperiment object as input.

library(SpatialExperiment)

# For SpatialExperiment object, we neeed to transpose the counts matrix in order
# to have genes on rows and spot on columns. 
# For this example, run spatialDE on the first 1000 genes

partial_counts <- head(Rep11_MOB_0, 1000)

spe <- SpatialExperiment(
  assays = list(counts = partial_counts),
  spatialData = DataFrame(MOB_sample_info[, c("x", "y")]),
  spatialCoordsNames = c("x", "y")
)

out <- spatialDE(spe, assay_type = "counts", verbose = FALSE)
head(out[order(out$qval), ])

4.1 Plot Spatial Patterns of Multiple Genes (using SpatialExperiment object)

We can plot spatial patterns of multiples genes using the spe object.

spe_results <- out[out$qval < 0.05, ]

ordered_spe_results <- spe_results[order(spe_results$qval), ]

multiGenePlots(spe,
    assay_type = "counts",
    ordered_spe_results[1:6, ]$g,
    point_size = 4,
    viridis_option = "D",
    dark_theme = FALSE
)

4.2 Classify spatially variable genes with model_search and spatial_patterns

msearch <- modelSearch(spe,
    de_results = out, qval_thresh = 0.05,
    verbose = FALSE
)

head(msearch)
spatterns <- spatialPatterns(spe,
    de_results = de_results, qval_thresh = 0.05,
    n_patterns = 4L, length = 1.5, verbose = FALSE
)

spatterns$pattern_results

sessionInfo

Session info

#> [1] "2025-10-07 20:18:25 EDT"
#> R version 4.5.1 Patched (2025-08-23 r88802)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.3 LTS
#> 
#> Matrix products: default
#> BLAS:   /home/biocbuild/bbs-3.22-bioc/R/lib/libRblas.so 
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0  LAPACK version 3.12.0
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=en_GB              LC_COLLATE=C              
#>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
#>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
#> 
#> time zone: America/New_York
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats4    stats     graphics  grDevices utils     datasets  methods  
#> [8] base     
#> 
#> other attached packages:
#>  [1] SpatialExperiment_1.19.1    SingleCellExperiment_1.31.1
#>  [3] SummarizedExperiment_1.39.2 Biobase_2.69.1             
#>  [5] GenomicRanges_1.61.5        Seqinfo_0.99.2             
#>  [7] IRanges_2.43.5              S4Vectors_0.47.4           
#>  [9] BiocGenerics_0.55.1         generics_0.1.4             
#> [11] MatrixGenerics_1.21.0       matrixStats_1.5.0          
#> [13] ggplot2_4.0.0               spatialDE_1.15.1           
#> [15] BiocStyle_2.37.1           
#> 
#> loaded via a namespace (and not attached):
#>  [1] gtable_0.3.6        dir.expiry_1.17.0   rjson_0.2.23       
#>  [4] xfun_0.53           bslib_0.9.0         ggrepel_0.9.6      
#>  [7] lattice_0.22-7      vctrs_0.6.5         tools_4.5.1        
#> [10] parallel_4.5.1      tibble_3.3.0        pkgconfig_2.0.3    
#> [13] Matrix_1.7-4        checkmate_2.3.3     RColorBrewer_1.1-3 
#> [16] S7_0.2.0            lifecycle_1.0.4     compiler_4.5.1     
#> [19] farver_2.1.2        tinytex_0.57        htmltools_0.5.8.1  
#> [22] sass_0.4.10         yaml_2.3.10         pillar_1.11.1      
#> [25] crayon_1.5.3        jquerylib_0.1.4     cachem_1.1.0       
#> [28] DelayedArray_0.35.3 magick_2.9.0        abind_1.4-8        
#> [31] basilisk_1.21.5     tidyselect_1.2.1    digest_0.6.37      
#> [34] dplyr_1.1.4         bookdown_0.45       labeling_0.4.3     
#> [37] fastmap_1.2.0       grid_4.5.1          cli_3.6.5          
#> [40] SparseArray_1.9.1   magrittr_2.0.4      S4Arrays_1.9.1     
#> [43] dichromat_2.0-0.1   withr_3.0.2         filelock_1.0.3     
#> [46] scales_1.4.0        backports_1.5.0     rmarkdown_2.30     
#> [49] XVector_0.49.1      reticulate_1.43.0   gridExtra_2.3      
#> [52] png_0.1-8           evaluate_1.0.5      knitr_1.50         
#> [55] viridisLite_0.4.2   rlang_1.1.6         Rcpp_1.1.0         
#> [58] glue_1.8.0          BiocManager_1.30.26 jsonlite_2.0.0     
#> [61] R6_2.6.1