# PRECAST: Human Breast Cancer Data Analysis

#### Wei Liu

#### 2025-12-15

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

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

We demonstrate the use of PRECAST to two sliced human breast cancer Visium data that are here, which can be downloaded to the current working path by the following command:

    githubURL <- "https://github.com/feiyoung/PRECAST/blob/main/vignettes_data/bc2.rda?raw=true"
    download.file(githubURL, "bc2.rda", mode = "wb")

Then load to R

    load("bc2.rda")

Download data from 10X: another method to access data

This data is also available at 10X genomics data website:

- Section1: https://support.10xgenomics.com/spatial-gene-expression/datasets/1.1.0/V1_Breast_Cancer_Block_A_Section_1
- Section2: https://support.10xgenomics.com/spatial-gene-expression/datasets/1.1.0/V1_Breast_Cancer_Block_A_Section_2

Users require the two folders for each dataset: spatial and filtered_feature_bc_matrix. Then the data can be read by the following commond.

    dir.file <- "Section"  ## the folders Section1 and Section2, and each includes two folders spatial and filtered_feature_bc_matrix
    seuList <- list()
    for (r in 1:2) {
        message("r = ", r)
        seuList[[r]] <- DR.SC::read10XVisium(paste0(dir.file, r))
    }
    bc2 <- seuList

The package can be loaded with the command:

    library(PRECAST)
    library(Seurat)

View human breast cancer Visium data from `DataPRECAST`

    bc2  ## a list including two Seurat object

Check the content in `bc2`

    head(bc2[[1]])

## Create a PRECASTObject object

We show how to create a PRECASTObject object step by step. First, we create a Seurat list object using the count matrix and meta data of each data batch. Although `bc2` is a prepared Seurat list object, we re-create it to show the details of the Seurat list object. At the same time, 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.

    ## Get the gene-by-spot read count matrices countList <- lapply(bc2, function(x)
    ## x[['RNA']]@counts)
    countList <- lapply(bc2, function(x) {
        assay <- DefaultAssay(x)
        GetAssayData(x, assay = assay, slot = "counts")

    })

    M <- length(countList)
    ## Get the meta data of each spot for each data batch
    metadataList <- lapply(bc2, function(x) x@meta.data)

    for (r in 1:M) {
        meta_data <- metadataList[[r]]
        all(c("row", "col") %in% colnames(meta_data))  ## the names are correct!
        head(meta_data[, c("row", "col")])
    }

    ## ensure the row.names of metadata in metaList are the same as that of colnames count matrix
    ## in countList

    for (r in 1:M) {
        row.names(metadataList[[r]]) <- colnames(countList[[r]])
    }

    ## Create the Seurat list object

    seuList <- list()
    for (r in 1:M) {
        seuList[[r]] <- CreateSeuratObject(counts = countList[[r]], meta.data = metadataList[[r]], project = "BreastCancerPRECAST")
    }

    bc2 <- seuList
    rm(seuList)
    head(meta_data[, c("row", "col")])

### Prepare the PRECASTObject with preprocessing step.

Next, we use `CreatePRECASTObject()` to create a PRECASTObject based on the Seurat list object `bc2`. This function will do three things:

- 1. Filter low-quality spots and genes, controlled by the arguments `premin.features` and `premin.spots`, respectively; the spots are retained in raw data (bc2) with at least premin.features number of nonzero-count features (genes), and the genes are retained in raw data (bc2) with at least `premin.spots` number of spots. To ease presentation, we denote the filtered Seurat list object as bc2_filter1.
- 2. Select the top 2,000 variable genes (by setting `gene.number=2000`) for each data batch using `FindSVGs()` function in `DR.SC` package for spatially variable genes or `FindVariableFeatures()` function in `Seurat` package for highly variable genes. Next, we prioritized genes based on the number of times they were selected as variable genes in all samples and chose the top 2,000 genes. Then denote the Seurat list object as bc2_filter2, where only 2,000 genes are retained.
- 3. Conduct strict quality control for bc2_filter2 by filtering spots and genes, controlled by the arguments `postmin.features` and `postmin.spots`, respectively; the spots are retained with at least `post.features` nonzero counts across genes; the features (genes) are retained with at least `postmin.spots` number of nonzero-count spots. Usually, no genes are filltered because these genes are variable genes.

If the argument `customGenelist` is not `NULL`, then this function only does (3) not (1) and (2). User can retain the raw seurat list object by setting `rawData.preserve = TRUE`.

    ## Create PRECASTObject.
    set.seed(2022)
    PRECASTObj <- CreatePRECASTObject(bc2, project = "BC2", gene.number = 2000, selectGenesMethod = "SPARK-X",
        premin.spots = 20, premin.features = 20, postmin.spots = 1, postmin.features = 10)

    ## User can retain the raw seuList by the following commond.  PRECASTObj <-
    ## CreatePRECASTObject(seuList, customGenelist=row.names(seuList[[1]]), rawData.preserve =
    ## TRUE)

### Add the model setting

Add adjacency matrix list and parameter setting of PRECAST. More model setting parameters can be found in .

    ## check the number of genes/features after filtering step
    PRECASTObj@seulist

    ## seuList is null since the default value `rawData.preserve` is FALSE.
    PRECASTObj@seuList

    ## Add adjacency matrix list for a PRECASTObj object to prepare for PRECAST model fitting.
    PRECASTObj <- AddAdjList(PRECASTObj, platform = "Visium")

    ## Add a model setting in advance for a PRECASTObj object. verbose =TRUE helps outputing the
    ## information in the algorithm.
    PRECASTObj <- AddParSetting(PRECASTObj, Sigma_equal = FALSE, verbose = TRUE, maxIter = 30)

## Fit PRECAST using this data

### 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 \(K\). First, we try using user-specified number of clusters. For convenience, we give the selected number of clusters by MBIC (K=14).

    ### Given K
    PRECASTObj <- PRECAST(PRECASTObj, K = 14)

Select a best model if \(K\) is an integer vector. Even if \(K\) is a scalar, this step is also neccessary to re-organize the results in `PRECASTObj` object.

    ## backup the fitting results in resList
    resList <- PRECASTObj@resList
    PRECASTObj <- SelectModel(PRECASTObj)

Integrate the two 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(bc2[[1]])[1:10])
    seuList <- lapply(bc2, function(x) x[genes, ])
    PRECASTObj@seuList <- seuList  #
    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, subsample_rate = 0.5)
    seuInt

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

    cols_cluster <- chooseColors(palettes_name = "Classic 20", n_colors = 14, plot_colors = FALSE)

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.

    pList <- SpaPlot(seuInt, item = "cluster", batch = NULL, point_size = 1, cols = cols_cluster, combine = FALSE,
        nrow.legend = 7)
    drawFigs(pList, layout.dim = c(1, 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)
    p13 <- SpaPlot(seuInt, batch = NULL, item = "RGB_UMAP", point_size = 2, combine = TRUE, text_size = 15)
    p13
    # 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 <- 10
    dat_deg %>%
        group_by(cluster) %>%
        top_n(n = n, wt = avg_log2FC) -> top10

    seuInt <- ScaleData(seuInt)
    seus <- subset(seuInt, downsample = 400)

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

    color_id <- as.numeric(levels(Idents(seus)))
    library(ggplot2)
    ## HeatMap
    p1 <- doHeatmap(seus, features = top10$gene, cell_label = "Domain", grp_label = F, grp_color = cols_cluster[color_id],
        pt_size = 6, slot = "scale.data") + theme(legend.text = element_text(size = 10), legend.title = element_text(size = 13,
        face = "bold"), axis.text.y = element_text(size = 5, face = "italic", family = "serif"))
    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