# CoFAST: NSCLC CosMx data coembedding

#### Wei Liu

#### 2025-12-14

This vignette introduces the CoFAST workflow for the analysis of NSCLC CosMx spatial transcriptomics dataset. In this vignette, the workflow of CoFAST consists of three steps

- Independent preprocessing and model setting
- Coembedding dimension reduction
- Downstream analysis (i.e. , signature gene analysis, visualization of cell types and coembeddings,)

## Load and view data

We demonstrate the use of CoFAST to NSCLC data, which can be downloaded 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 method
    data(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 CosMx data. First, we determine the dimension of coembeddings. Then, we select the 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, and observe that the `reductions` field acquires an additional component named `fast`.

    pos <- as.matrix(CosMx_subset@meta.data[,c("x", "y")]) # Extract the spatial coordinates
    Adj_sp <- AddAdj(pos) ## calculate the adjacency matrix
    CosMx_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 on comebeddings. 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_type
    df_sig_list <- find.signature.genes(CosMx_subset)
    str(df_sig_list)

Then, we obtain the top five signature genes and organize them into a data.frame. Next, we calculate the UMAP projections of coembeddings. The colname `distance` means the distance between gene (i.e., MS4A1) and cells with the specific cell type (i.e., B cell), which is calculated based on the coembedding of genes and cells in the coembedding space. The distance is smaller, the association between gene and the cell type is stronger. The colname `expr.prop` represents the expression proportion of the gene (i.e., MS4A1) within the cell type (i.e., B cell). The colname `label` means the cell types and colname `gene` denotes the gene name. By the data.frame object, we know `MS4A1` is the one of the top signature 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 and the 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 of tumor 5 in the UMAP space of coembedding. We observe that the UMAP projections of the two signature genes are near to B cells, which indicates these genes are enriched in B cells.

    ## choose beutifual colors
    cols_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 all involved cell types in the UMAP space of coembedding. We observe that the UMAP projections of the signature genes are near to the corresponding cell type, which indicates these genes are enriched in the corresponding 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 visualization functions in `Seurat` package for visualization. The following is an example that visualizes the cell types on the UMAP space.

    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 signature genes of Tumor 5 on UMAP space, in which we observed the high expression in B cells in constrast to other cell types.

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

Session Info

    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