Last updated: 2022-03-22

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Loading data

Slide-seq data is available at Broad Institute’s single-cell repository with ID SCP354. We also saved the raw data that we used in our examples in RData format, which can be downloaded from here.

load("./data/slideseq.rds") 
print(dim(sp_count)) # The count matrix
print(dim(location)) # The location matrix

Create SpatialPCA Object

SpatialPCA takes the raw count data and location coordinates as inputs. We first create a S4 object in the CreateSpatialPCAObject function, then select spatial genes using spark (in Visium data or ST data with small sample size) or sparkx (in Slide-seq or Slide-seq V2 data with large sample size).

# location matrix: n x 2, count matrix: g x n.
# here n is spot number, g is gene number.
# here the column names of sp_count and rownames of location should be matched
slideseq = CreateSpatialPCAObject(counts=sp_count, location=location, project = "SpatialPCA",gene.type="spatial",sparkversion="sparkx",numCores_spark=5, customGenelist=NULL,min.loctions = 20, min.features=20)

Estimate spatial PCs

SpatialPCA constructs a kernel matrix to model the spatial pattern of spatial PCs.

slideseq = SpatialPCA_buildKernel(slideseq, kerneltype="gaussian", bandwidthtype="Silverman",bandwidth.set.by.user=NULL,sparseKernel=TRUE,sparseKernel_tol=1e-20,sparseKernel_ncore=10)
slideseq = SpatialPCA_EstimateLoading(slideseq,fast=TRUE,SpatialPCnum=20)
slideseq = SpatialPCA_SpatialPCs(slideseq, fast=TRUE)

In this data (n>20,000), we select “Silverman” to use Silverman’s ‘rule of thumb’ method (1986) to calculate the kernel bandwidth. The user can also specify the bandwidth on their own if needed. After specifying the kernel matrix, SpatialPCA estimates the loading matrix and the spatial PCs. The users can select “TRUE” if they want to use low-rank approximation on the kernel matrix to accelerate the algorithm, otherwise select “FALSE”.

Detect spatial domains

SpatialPCA detects spatial domains through clustering on the spatial PCs. Here we use louvain’s clustering method, which is faster than walktrap method in large sample datasets.

clusterlabel= louvain_clustering(clusternum=8,latent_dat=slideseq@SpatialPCs,knearest=round(sqrt(dim(slideseq@SpatialPCs)[2])) )

Spatial domains detected by SpatialPCA.

cbp=c("#9C9EDE" ,"#5CB85C" ,"#E377C2", "#4DBBD5" ,"#FED439" ,"#FF9896", "#FFDC91")
plot_cluster(location=xy_coords,clusterlabel_refine,pointsize=1.5,textsize=20 ,title_in=paste0("SpatialPCA"),color_in=cbp,legend="right")


sessionInfo()
R version 4.1.3 (2022-03-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.5 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [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       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] workflowr_1.6.2

loaded via a namespace (and not attached):
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 [9] stringr_1.4.0    tools_4.1.3      xfun_0.27        utf8_1.2.2      
[13] git2r_0.28.0     jquerylib_0.1.4  htmltools_0.5.2  ellipsis_0.3.2  
[17] rprojroot_2.0.2  yaml_2.2.1       digest_0.6.28    tibble_3.1.5    
[21] lifecycle_1.0.1  crayon_1.4.1     later_1.3.0      sass_0.4.0      
[25] vctrs_0.3.8      promises_1.2.0.1 fs_1.5.0         glue_1.4.2      
[29] evaluate_0.14    rmarkdown_2.11   stringi_1.7.5    bslib_0.3.1     
[33] compiler_4.1.3   pillar_1.6.4     jsonlite_1.7.2   httpuv_1.6.3    
[37] pkgconfig_2.0.3