# DR-SC: simulation

#### Wei Liu

#### 2025-10-03

## Generate the simulated data

First, we analyze a simulated spatial transcriptomics data from ST platform, which is a Seurat object format. It is noted that the `meta.data` must include spatial coordinates in columns named “row” (x coordinates) and “col” (y coordinates)! This data is available here.

    load('sim_seu.rds')
    library(DR.SC)
    head(seu@meta.data)

## Fit DR-SC using simulated data

### Data preprocessing

This preprocessing includes Log-normalization and feature selection. Here we select highly variable genes for example first. The selected genes’ names are saved in “seu@assays$RNA@var.features”.

    ### Given K
    library(Seurat)
    seu <- NormalizeData(seu)
    # choose highly variable features using Seurat
    seu <- FindVariableFeatures(seu, nfeatures = 400)

### Fit DR-SC based on highly variable genes(HVGs)

For function `DR.SC`, users can specify the number of clusters \(K\) or set `K` to be an integer vector by using modified BIC(MBIC) to determine \(K\). First, we try using user-specified number of clusters. Then we show the version chosen by MBIC.

    ### Given K
    seu2 <- DR.SC(seu, q=15, K=4, platform = 'ST',  verbose=F, approxPCA=T)

After finishing model fitting, we use ajusted rand index (`ARI`) to check the performance of clustering

    mclust::adjustedRandIndex(seu2$spatial.drsc.cluster, seu$true_clusters)

Next, we show the application of DR-SC in visualization. First, we can visualize the clusters from DR-SC on the spatial coordinates.

    spatialPlotClusters(seu2)

We can also visualize the clusters from DR-SC on the two-dimensional tSNE based on the extracted features from DR-SC.

    drscPlot(seu2)

Show the UMAP plot based on the extracted features from DR-SC.

    drscPlot(seu2, visu.method = 'UMAP')

Use MBIC to choose number of clusters:

    seu2 <- DR.SC(seu, q=10, K=2:6, platform = 'ST', verbose=F,approxPCA=T)
    mbicPlot(seu2)

### Fit DR-SC based on spatially variable genes(SVGs)

First, we select the spatilly variable genes using funciton `FindSVGs`.

    ### Given K
    seu <- NormalizeData(seu, verbose=F)
    # choose 400 spatially variable features using FindSVGs
    seus <- FindSVGs(seu, nfeatures = 400, verbose = F)
    seu2 <- DR.SC(seus, q=15, K=4, platform = 'ST', verbose=F)

Using ARI to check the performance of clustering

    mclust::adjustedRandIndex(seu2$spatial.drsc.cluster, seu$true_clusters)

### DR-SC can enhance visualization

Show the spatial scatter plot for clusters

    spatialPlotClusters(seu2)

Show the tSNE plot based on the extracted features from DR-SC.

    drscPlot(seu2)

Show the UMAP plot based on the extracted features from DR-SC.

    drscPlot(seu2, visu.method = 'UMAP')

### DR-SC can automatically determine the number of clusters

Use MBIC to choose number of clusters:

    seu2 <- DR.SC(seus, q=4, K=2:6, platform = 'ST',  verbose=F)
    mbicPlot(seu2)
    # or plot BIC or AIC
    # mbicPlot(seu2, criteria = 'BIC')
    # mbicPlot(seu2, criteria = 'AIC')
    # tune pen.const
    seu2 <- selectModel(seu2, pen.const = 0.7)
    mbicPlot(seu2)

## DR-SC can help differentially expression analysis

Conduct visualization of marker gene expression. ### Ridge plots Visualize single cell expression distributions in each cluster from Seruat.

    dat <- FindAllMarkers(seu2)
    suppressPackageStartupMessages(library(dplyr) )
    # Find the top 1 marker genes, user can change n to access more marker genes
    dat %>%group_by(cluster) %>%
        top_n(n = 1, wt = avg_log2FC) -> top
    genes <- top$gene
    RidgePlot(seu2, features = genes, ncol = 2)

### Violin plot

Visualize single cell expression distributions in each cluster

    VlnPlot(seu2, features = genes, ncol=2)

### Feature plot

We extract tSNE based on the features from DR-SC and then visualize feature expression in the low-dimensional space

    seu2 <- RunTSNE(seu2, reduction="dr-sc", reduction.key='drsctSNE_')
    FeaturePlot(seu2, features = genes, reduction = 'tsne' ,ncol=2)

### Dot plots

The size of the dot corresponds to the percentage of cells expressing the feature in each cluster. The color represents the average expression level

    DotPlot(seu2, features = genes)

### Heatmap plot

Single cell heatmap of feature expression

    # standard scaling (no regression)
    dat %>%group_by(cluster) %>%
        top_n(n = 30, wt = avg_log2FC) -> top
    ### select the marker genes that are also the variable genes.

    genes <- intersect(top$gene, VariableFeatures(seu2))
    ## Change the HVGs to SVGs
    #  <- topSVGs(seu2, 400)
    seu2 <- ScaleData(seu2, verbose = F)
    DoHeatmap(subset(seu2, downsample = 500),features = genes, size = 5)

## Session information

    sessionInfo()