# PRECAST: Four DLPFC Sample Analysis

#### Wei Liu

#### 2025-12-15

This vignette introduces the PRECAST workflow for the analysis of four spatial transcriptomics datasets. The workflow consists of three steps

- Independent preprocessing and model setting
- Probabilistic embedding and clustering using PRECAST model
- Downstream analysis (i.e. visualization of clusters and embeddings)

We demonstrate the use of PRECAST to four human dorsolateral prefrontal cortex Visium data. Download the data to R

    suppressPackageStartupMessages(library(Seurat))
    suppressPackageStartupMessages(library(SingleCellExperiment))
    name_ID4 <- as.character(c(151673, 151674, 151675, 151676))

    ### Read data in an online manner
    n_ID <- length(name_ID4)
    url_brainA <- "https://github.com/feiyoung/DR-SC.Analysis/raw/main/data/DLPFC_data/"
    url_brainB <- ".rds"
    seuList <- list()
    if (!require(ProFAST)) {
        remotes::install_github("feiyoung/ProFAST")
    }
    for (i in 1:n_ID) {
        # i <- 1
        cat("input brain data", i, "\n")
        # load and read data
        dlpfc <- readRDS(url(paste0(url_brainA, name_ID4[i], url_brainB)))
        # dlpfc <- readRDS((paste0('', name_ID4[i],url_brainB) ))
        count <- dlpfc@assays@data$counts
        row.names(count) <- ProFAST::transferGeneNames(row.names(count), species = "Human")
        row.names(count) <- make.unique(row.names(count))
        seu1 <- CreateSeuratObject(counts = count, meta.data = as.data.frame(colData(dlpfc)), min.cells = 10,
            min.features = 10)
        seuList[[i]] <- seu1
    }
    # saveRDS(seuList, file='seuList4.RDS')

The package can be loaded with the command:

    library(PRECAST)

## Compare PRECAST with DR-SC in analyzing one sample

First, we view the the spatial transcriptomics data with Visium platform.

    seuList <- readRDS("seuList4.RDS")
    seuList  ## a list including  Seurat objects

check the meta data that must include the spatial coordinates named “row” and “col”, respectively. If the names are not, they are required to rename them.

    metadataList <- lapply(seuList, function(x) x@meta.data)

    for (r in seq_along(metadataList)) {
        meta_data <- metadataList[[r]]
        cat(all(c("row", "col") %in% colnames(meta_data)), "\n")  ## the names are correct!

    }

### Prepare the PRECASTObject.

Create a PRECASTObj object to prepare for PRECAST models. Here, we only show the `HVGs` method to select the 2000 highly variable genes, but users are able to choose more genes or also use the `SPARK-X` to choose the spatially variable genes.

    set.seed(2023)
    preobj <- CreatePRECASTObject(seuList = seuList, selectGenesMethod = "HVGs", gene.number = 2000)  #

### Add the model setting

    ## check the number of genes/features after filtering step
    preobj@seulist
    ## Add adjacency matrix list for a PRECASTObj object to prepare for PRECAST model fitting.
    PRECASTObj <- AddAdjList(preobj, platform = "Visium")
    ## Add a model setting in advance for a PRECASTObj object. verbose =TRUE helps outputing the
    ## information in the algorithm. We provide two initial model to obtain initial values:
    ## 'mclust' and 'kmeans', and recommend using 'mclust' in LIBD data.
    PRECASTObj <- AddParSetting(PRECASTObj, Sigma_equal = TRUE, coreNum = 1, int.model = "mclust", maxIter = 30,
        verbose = TRUE)

### Fit PRECAST

For function `PRECAST`, users can specify the number of clusters \(K\) or set `K` to be an integer vector by using modified BIC(MBIC) to determine \(K\). Here, we use user-specified number of clusters.

    ### Given K
    PRECASTObj <- PRECAST(PRECASTObj, K = 7)

Use the function `SelectModel()` to re-organize the fitted results in PRECASTObj.

    ## backup the fitting results in resList
    resList <- PRECASTObj@resList
    PRECASTObj <- SelectModel(PRECASTObj)
    ari_precast <- sapply(1:length(seuList), function(r) mclust::adjustedRandIndex(PRECASTObj@resList$cluster[[r]],
        PRECASTObj@seulist[[r]]$layer_guess_reordered))
    mat <- matrix(round(ari_precast, 2), nrow = 1)
    name_ID4 <- as.character(c(151673, 151674, 151675, 151676))
    colnames(mat) <- name_ID4
    DT::datatable(mat)

Other options

Users are also able to set multiple K, then choose the best one

    PRECASTObj2 <- AddParSetting(PRECASTObj, Sigma_equal = FALSE, coreNum = 4, maxIter = 30, verbose = TRUE)  # set 4 cores to run in parallel.
    PRECASTObj2 <- PRECAST(PRECASTObj2, K = 5:8)
    ## backup the fitting results in resList
    resList2 <- PRECASTObj2@resList
    PRECASTObj2 <- SelectModel(PRECASTObj2)

Besides, user can also use different initialization method by setting `int.model`, for example, set `int.model=NULL`; see the functions `AddParSetting()` and `model_set()` for more details.

### Integration and Visualization

Integrate the all samples using the `IntegrateSpaData` function. For computational efficiency, this function exclusively integrates the variable genes. Specifically, in cases where users do not specify the `PRECASTObj@seuList` or `seuList` argument within the `IntegrateSpaData` function, it automatically focuses on integrating only the variable genes. The default setting for `PRECASTObj@seuList` is `NULL` when `rawData.preserve` in `CreatePRECASTObject` is set to `FALSE`. For instance:

    print(PRECASTObj@seuList)
    seuInt <- IntegrateSpaData(PRECASTObj, species = "Human")
    seuInt
    ## The low-dimensional embeddings obtained by PRECAST are saved in PRECAST reduction slot.

Integrating all genes

There are two ways to use `IntegrateSpaData` integrating all genes, which will require more memory. We recommand running for all genes on server. The first one is to set value for `PRECASTObj@seuList`.

    ## assign the raw Seurat list object to it.  For illustration, we generate a new seuList with
    ## more genes; For integrating all genes, users can set `seuList <- bc2`.
    genes <- c(row.names(PRECASTObj@seulist[[1]]), row.names(seuList[[1]])[1:10])
    seuList_sub <- lapply(seuList, function(x) x[genes, ])
    PRECASTObj@seuList <- seuList_sub  #
    seuInt <- IntegrateSpaData(PRECASTObj, species = "Human")
    seuInt

The second method is to set a value for the argument `seuList`:

    PRECASTObj@seuList <- NULL
    ## At the same time, we can set subsampling to speed up the computation.
    seuInt <- IntegrateSpaData(PRECASTObj, species = "Human", seuList = seuList_sub, subsample_rate = 0.5)
    seuInt

First, user can choose a beautiful color schema using `chooseColors()`.

    cols_cluster <- chooseColors(palettes_name = "Classic 20", n_colors = 7, plot_colors = TRUE)

Show the spatial scatter plot for clusters

    p12 <- SpaPlot(seuInt, item = "cluster", batch = NULL, point_size = 1, cols = cols_cluster, combine = TRUE,
        nrow.legend = 7)
    p12

    # users can plot each sample by setting combine=FALSE

Users can re-plot the above figures for specific need by returning a ggplot list object. For example, we plot the spatial heatmap using a common legend.

    library(ggplot2)
    pList <- SpaPlot(seuInt, item = "cluster", batch = NULL, point_size = 2.5, cols = cols_cluster,
        combine = FALSE, nrow.legend = 7)
    pList <- lapply(pList, function(x) x + coord_flip() + scale_x_reverse())
    drawFigs(pList, layout.dim = c(2, 2), common.legend = TRUE, legend.position = "right", align = "hv")

Show the spatial UMAP/tNSE RGB plot to illustrate the performance in extracting features.

    seuInt <- AddUMAP(seuInt)
    p13List <- SpaPlot(seuInt, batch = NULL, item = "RGB_UMAP", point_size = 2, combine = FALSE, text_size = 15)
    p13List <- lapply(p13List, function(x) x + coord_flip() + scale_x_reverse())
    drawFigs(p13List, layout.dim = c(2, 2), common.legend = TRUE, legend.position = "right", align = "hv")
    # seuInt <- AddTSNE(seuInt) SpaPlot(seuInt, batch=NULL,item='RGB_TSNE',point_size=2,
    # combine=T, text_size=15)

Show the tSNE plot based on the extracted features from PRECAST to check the performance of integration.

    seuInt <- AddTSNE(seuInt, n_comp = 2)
    p1 <- dimPlot(seuInt, item = "cluster", point_size = 0.5, font_family = "serif", cols = cols_cluster,
        border_col = "gray10", nrow.legend = 14, legend_pos = "right")  # Times New Roman
    p2 <- dimPlot(seuInt, item = "batch", point_size = 0.5, font_family = "serif", legend_pos = "right")

    drawFigs(list(p1, p2), layout.dim = c(1, 2), legend.position = "right", align = "hv")

Combined differential expression analysis

    library(Seurat)
    dat_deg <- FindAllMarkers(seuInt)
    library(dplyr)
    n <- 5
    dat_deg %>%
        group_by(cluster) %>%
        top_n(n = n, wt = avg_log2FC) -> top10

Plot DE genes’ heatmap for each spatial domain identified by PRECAST.

    library(ggplot2)
    ## HeatMap
    p1 <- DotPlot(seuInt, features = unique(top10$gene), col.min = 0, col.max = 1) + theme(axis.text.x = element_text(angle = 45,
        hjust = 1, size = 8))
    p1

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       formatR_1.14      rlang_1.1.4
    #> [21] jsonlite_1.8.9