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] "2024-05-01 19:34:38 EDT"
#> R version 4.4.0 beta (2024-04-15 r86425)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 22.04.4 LTS
#> 
#> Matrix products: default
#> BLAS:   /home/biocbuild/bbs-3.19-bioc/R/lib/libRblas.so 
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
#> 
#> 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_1.14.0    SingleCellExperiment_1.26.0
#>  [3] SummarizedExperiment_1.34.0 Biobase_2.64.0             
#>  [5] GenomicRanges_1.56.0        GenomeInfoDb_1.40.0        
#>  [7] IRanges_2.38.0              S4Vectors_0.42.0           
#>  [9] BiocGenerics_0.50.0         MatrixGenerics_1.16.0      
#> [11] matrixStats_1.3.0           ggplot2_3.5.1              
#> [13] spatialDE_1.10.0            BiocStyle_2.32.0           
#> 
#> loaded via a namespace (and not attached):
#>  [1] gtable_0.3.5            dir.expiry_1.12.0       rjson_0.2.21           
#>  [4] xfun_0.43               bslib_0.7.0             ggrepel_0.9.5          
#>  [7] lattice_0.22-6          vctrs_0.6.5             tools_4.4.0            
#> [10] generics_0.1.3          parallel_4.4.0          tibble_3.2.1           
#> [13] fansi_1.0.6             highr_0.10              pkgconfig_2.0.3        
#> [16] Matrix_1.7-0            checkmate_2.3.1         lifecycle_1.0.4        
#> [19] GenomeInfoDbData_1.2.12 farver_2.1.1            compiler_4.4.0         
#> [22] tinytex_0.50            munsell_0.5.1           htmltools_0.5.8.1      
#> [25] sass_0.4.9              yaml_2.3.8              pillar_1.9.0           
#> [28] crayon_1.5.2            jquerylib_0.1.4         cachem_1.0.8           
#> [31] DelayedArray_0.30.0     magick_2.8.3            abind_1.4-5            
#> [34] basilisk_1.16.0         tidyselect_1.2.1        digest_0.6.35          
#> [37] dplyr_1.1.4             bookdown_0.39           labeling_0.4.3         
#> [40] fastmap_1.1.1           grid_4.4.0              colorspace_2.1-0       
#> [43] cli_3.6.2               SparseArray_1.4.0       magrittr_2.0.3         
#> [46] S4Arrays_1.4.0          utf8_1.2.4              withr_3.0.0            
#> [49] backports_1.4.1         filelock_1.0.3          scales_1.3.0           
#> [52] UCSC.utils_1.0.0        rmarkdown_2.26          XVector_0.44.0         
#> [55] httr_1.4.7              gridExtra_2.3           reticulate_1.36.1      
#> [58] png_0.1-8               evaluate_0.23           knitr_1.46             
#> [61] basilisk.utils_1.16.0   viridisLite_0.4.2       rlang_1.1.3            
#> [64] Rcpp_1.0.12             glue_1.7.0              BiocManager_1.30.22    
#> [67] jsonlite_1.8.8          R6_2.5.1                zlibbioc_1.50.0