Load and view data

We demonstrate the use of CoFAST to NSCLC data, which can bedownloaded to the current working path by the following command:

set.seed(2024)# set a random seed for reproducibility.library(ProFAST)# load the package of FAST methoddata(CosMx_subset)CosMx_subset

The package can be loaded with the command:

library(Seurat)

Preprocessing

First, we normalize the data.

CosMx_subset<-NormalizeData(CosMx_subset)

Then, we select the variable genes.

CosMx_subset<-FindVariableFeatures(CosMx_subset)

Coembedding using FAST

We introduce how to use FAST to perform coembedding for this CosMxdata. First, we determine the dimension of coembeddings. Then, we selectthe variable genes.

dat_cor<-diagnostic.cor.eigs(CosMx_subset)q_est<-attr(dat_cor,"q_est")cat("q_est = ", q_est,'\n')

Subsequently, we calculate coembeddings by utilizing FAST, andobserve that thereductions field acquires an additionalcomponent namedfast.

pos<-as.matrix(CosMx_subset@meta.data[,c("x","y")])# Extract the spatial coordinatesAdj_sp<-AddAdj(pos)## calculate the adjacency matrixCosMx_subset<-NCFM_fast(CosMx_subset,Adj_sp = Adj_sp,q = q_est)CosMx_subset

Downstream analysis

In the following, we show how to find the signature genes based oncomebeddings. First, we calculate the distance matrix.

CosMx_subset<-pdistance(CosMx_subset,reduction ="fast")

Next, we find the signature genes for each cell type

print(table(CosMx_subset$cell_type))Idents(CosMx_subset)<- CosMx_subset$cell_typedf_sig_list<-find.signature.genes(CosMx_subset)str(df_sig_list)

Then, we obtain the top five signature genes and organize them into adata.frame. Next, we calculate the UMAP projections of coembeddings. Thecolnamedistance means the distance between gene (i.e.,MS4A1) and cells with the specific cell type (i.e., B cell), which iscalculated based on the coembedding of genes and cells in thecoembedding space. The distance is smaller, the association between geneand the cell type is stronger. The colnameexpr.proprepresents the expression proportion of the gene (i.e., MS4A1) withinthe cell type (i.e., B cell). The colnamelabel means thecell types and colnamegene denotes the gene name. By thedata.frame object, we knowMS4A1 is the one of the topsignature gene of B cell.

dat<-get.top.signature.dat(df_sig_list,ntop =2,expr.prop.cutoff =0.1)head(dat)

Next, we calculate the UMAP projections of coembeddings of cells andthe selected signature genes.

CosMx_subset<-coembedding_umap(  CosMx_subset,reduction ="fast",reduction.name ="UMAP",gene.set =unique(dat$gene))

Furthermore, we visualize the cells and top two signature genes oftumor 5 in the UMAP space of coembedding. We observe that the UMAPprojections of the two signature genes are near to B cells, whichindicates these genes are enriched in B cells.

## choose beutifual colorscols_cluster<-c("black", PRECAST::chooseColors(palettes_name ="Blink 23",n_colors =21,plot_colors =TRUE))p1<-coembed_plot(   CosMx_subset,reduction ="UMAP",gene_txtdata =subset(dat, label=='tumor 5'),cols=cols_cluster,pt_text_size =3)p1

Then, we visualize the cells and top two signature genes of allinvolved cell types in the UMAP space of coembedding. We observe thatthe UMAP projections of the signature genes are near to thecorresponding cell type, which indicates these genes are enriched in thecorresponding cells.

p2<-coembed_plot(   CosMx_subset,reduction ="UMAP",gene_txtdata = dat,cols=cols_cluster,pt_text_size =3,alpha=0.2)p2

In addtion, we can fully take advantages of the visualizationfunctions inSeurat package for visualization. Thefollowing is an example that visualizes the cell types on the UMAPspace.

cols_type<- cols_cluster[-1]names(cols_type)<-sort(levels(Idents(CosMx_subset)))DimPlot(CosMx_subset,reduction ='UMAP',cols=cols_type)

Then, there is another example that we plot the first two signaturegenes of Tumor 5 on UMAP space, in which we observed the high expressionin B cells in constrast to other cell types.

FeaturePlot(CosMx_subset,reduction ='UMAP',features =c("PSCA","CEACAM6"))

sessionInfo()#> R version 4.4.1 (2024-06-14 ucrt)#> Platform: x86_64-w64-mingw32/x64#> Running under: Windows 11 x64 (build 26100)#>#> Matrix products: default#>#>#> locale:#> [1] LC_COLLATE=C#> [2] LC_CTYPE=Chinese (Simplified)_China.utf8#> [3] LC_MONETARY=Chinese (Simplified)_China.utf8#> [4] LC_NUMERIC=C#> [5] LC_TIME=Chinese (Simplified)_China.utf8#>#> time zone: Asia/Shanghai#> tzcode source: internal#>#> attached base packages:#> [1] stats     graphics  grDevices utils     datasets  methods   base#>#> loaded via a namespace (and not attached):#>  [1] digest_0.6.37     R6_2.5.1          fastmap_1.2.0     xfun_0.47#>  [5] cachem_1.1.0      knitr_1.48        htmltools_0.5.8.1 rmarkdown_2.28#>  [9] lifecycle_1.0.4   cli_3.6.3         sass_0.4.9        jquerylib_0.1.4#> [13] compiler_4.4.1    rstudioapi_0.16.0 tools_4.4.1       evaluate_1.0.0#> [17] bslib_0.8.0       yaml_2.3.10       rlang_1.1.4       jsonlite_1.8.9