Last updated: 2022-03-22

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Rmd 251c36f shangll123 2022-03-22 Publish SpatialPCA tutorial

Loading data

The merfish mouse brain data from Vizgen is available at Vizgen MERFISH Mouse Receptor Map. We also saved the raw data that we used in our examples in RData format, which can be downloaded from here.

## Process the downloaded data:
#cd <- read.csv('datasets-mouse_brain_map-BrainReceptorShowcase-Slice2-Replicate1-cell_by_gene_S2R1.csv', row.names = 1)
#annot <- read.csv('datasets-mouse_brain_map-BrainReceptorShowcase-Slice2-Replicate1-cell_metadata_S2R1.csv', row.names = 1)
#pos <- annot[, c('center_x', 'center_y')]
#pos[,2] <- -pos[,2] ## flip Y coordinates for better visualization
#location = as.matrix(pos)
#raw_matrix = t(cd[,1:483])
#rownames(location) = colnames(raw_matrix) = paste0("cell",1:dim(location)[1])

load("./data/Vizgen_Merfish_count_location.RData") 
print(dim(raw_matrix)) # 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 sparkx in this dataset 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
Vizgen = CreateSpatialPCAObject(counts=raw_matrix, location=location, project = "SpatialPCA",gene.type="spatial",sparkversion="sparkx", gene.number=3000,customGenelist=NULL,min.loctions = 50, min.features=85) 
# Here I filtered genes and locations with a stringent cut-off to reduce the sample size and quickly obtain the results, you can also set a more relaxed cut-off here and get a larger sample size.

Estimate spatial PCs

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

Vizgen = SpatialPCA_buildKernel(Vizgen, kerneltype="gaussian", bandwidth.set.by.user=0.1,sparseKernel_tol=1e-5) 
# in this data we set bandwidth = 0.1.
# we also set sparseKernel_tol=1e-5 to obtain a sparse kernel matrix.
Vizgen = SpatialPCA_EstimateLoading(Vizgen,fast=TRUE,SpatialPCnum=20)
Vizgen = SpatialPCA_SpatialPCs(Vizgen, fast=TRUE)

In this data, we select “Silverman” to use Silverman’s ‘rule of thumb’ method (1986) to calculate the kernel bandwidth. The user can also specify other 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=30,latent_dat=Vizgen@SpatialPCs,knearest=round(sqrt(dim(Vizgen@SpatialPCs)[2])) )
# I set cluster number to be 30 here, you can also try other cluster numbers if needed.

Spatial domains detected by SpatialPCA.

# set color
D3=c("#1F77B4", "#FF7F0E", "#2CA02C" ,"#D62728", "#9467BD" ,"#8C564B", "#E377C2",
 "#7F7F7F", "#BCBD22", "#17BECF", "#AEC7E8" ,"#FFBB78" ,"#98DF8A", "#FF9896",
"#C5B0D5" ,"#C49C94", "#F7B6D2", "#C7C7C7" ,"#DBDB8D" ,"#9EDAE5", "#393B79",
"#637939", "#8C6D31", "#843C39", "#7B4173" ,"#5254A3" ,"#8CA252", "#BD9E39",
"#AD494A", "#A55194", "#6B6ECF", "#B5CF6B", "#E7BA52" ,"#D6616B", "#CE6DBD",
"#9C9EDE", "#CEDB9C" ,"#E7CB94", "#E7969C", "#DE9ED6" ,"#3182BD", "#E6550D",
"#31A354", "#756BB1" ,"#636363", "#6BAED6" ,"#FD8D3C" ,"#74C476", "#9E9AC8",
"#969696", "#9ECAE1" ,"#FDAE6B", "#A1D99B" ,"#BCBDDC" ,"#BDBDBD", "#C6DBEF",
"#FDD0A2" ,"#C7E9C0" ,"#DADAEB", "#D9D9D9")

plot_cluster(legend="none",location=SpatialPCA_result$location,clusterlabel,pointsize=0.5,text_size=20 ,title_in=paste0("SpatialPCA cluster"),color_in=D3)


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):
 [1] Rcpp_1.0.7       whisker_0.4      knitr_1.36       magrittr_2.0.1  
 [5] R6_2.5.1         rlang_0.4.12     fastmap_1.1.0    fansi_0.5.0     
 [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