# FAST: two DLPFC sections

#### Wei Liu

#### 2026-04-29

This vignette introduces the FAST workflow for the analysis of two humn dorsolateral prefrontal cortex (DLPFC) spatial transcriptomics dataset. FAST workflow is based on the `PRECASTObj` object created in the `PRECAST` R package and the workflow of FAST is similar to that of PRECAST; see the vignette (https://feiyoung.github.io/PRECAST/articles/PRECAST.BreastCancer.html) for the workflow of PRECAST. In this vignette, the workflow of FAST consists of three steps

- Independent preprocessing and model setting
- Spatial dimension reduction using FAST
- Downstream analysis (i.e. , embedding alignment and spatial clustering using iSC-MEB, visualization of clusters and embeddings, remove unwanted variation, combined differential expression analysis)
- Except for the above downstream analyses, cell-cell interaction analysis and trajectory inference can also be performed on the basis of the embeddings obtained by FAST.

We demonstrate the use of FAST to two DLPFC Visium data that are here, which can be downloaded to the current working path by the following command:

    githubURL <- "https://github.com/feiyoung/FAST/blob/main/vignettes_data/seulist2_ID9_10.RDS?raw=true"
    download.file(githubURL,"seulist2_ID9_10.RDS",mode='wb')

Then load to R

    dlpfc2 <- readRDS("./seulist2_ID9_10.RDS")

The package can be loaded with the command:

    library(ProFAST) # load the package of FAST method
    library(PRECAST)
    library(Seurat)

## View the DLPFC data

First, we view the the two spatial transcriptomics data with Visium platform. There are ~15000 genes for each data batch and ~7000 spots in total.

    dlpfc2 ## a list including three Seurat object with default assay: RNA

Check the content in `dlpfc2`.

    head(dlpfc2[[1]])

We observed the genes are Ensembl IDs. In the following, we will transfer the Ensembl IDs to gene symbols for matching the housekeeping genes in the downstream analysis for removing the unwanted variations.

    row.names(dlpfc2[[1]])[1:10]

## 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.

- Note: the spatial coordinates must be contained in the meta data and named as `row` and `col`, which benefits the identification of spaital coordinates by FAST

    ## Get the gene-by-spot read count matrices
    countList <- lapply(dlpfc2, function(x) x[["RNA"]]@counts)
    M <- length(countList)
    ### transfer the Ensembl ID to symbol
    for(r in 1:M){
      row.names(countList[[r]]) <- transferGeneNames(row.names(countList[[r]]), now_name = "ensembl",
                                                     to_name="symbol",
                                                     species="Human", Method='eg.db')
    }

    ## Check the spatial coordinates: Yes, they are named as "row" and "col"!
    print(head(dlpfc2[[1]]@meta.data))

    ## Get the meta data of each spot for each data batch
    metadataList <- lapply(dlpfc2, function(x) x@meta.data)

    ## 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 = "FASTdlpfc2")
    }
    print(seuList)
    row.names(seuList[[1]])[1:10]

### Prepare the PRECASTObject with preprocessing step.

Next, we use `CreatePRECASTObject()` to create a PRECASTObject object based on the Seurat list object `seuList`. Here, we select the top 2000 highly variable genes for followed analysis. Users are able to see https://feiyoung.github.io/PRECAST/articles/PRECAST.BreastCancer.html for what is done in this function.

    ## Create PRECASTObject
    set.seed(2023)
    PRECASTObj <- CreatePRECASTObject(seuList, project = "FASTdlpfc2", gene.number = 2000, selectGenesMethod = "HVGs",
        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)
    ## check the number of genes/features after filtering step
    PRECASTObj@seulist

## Fit FAST for this data

### Add the model setting

Add adjacency matrix list and parameter setting of FAST. More model setting parameters can be found in `model_set_FAST()`.

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

    ## Add adjacency matrix list for a PRECASTObj object to prepare for FAST 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 <- AddParSettingFAST(PRECASTObj, verbose=TRUE)
    ## Check the parameters
    PRECASTObj@parameterList

### Fit FAST

For function `FAST`, users can specify the number of factors `q` and the fitted model `fit.model`. The `q` sets the number of spatial factors to be extracted, and a lareger one means more information to be extracted but higher computaional cost. The `fit.model` specifies the version of FAST to be fitted. The Gaussian version (`gaussian`) models the log-count matrices while the Poisson verion (`poisson`) models the count matrices; default as `poisson`. For a fast implementation, we run Gaussian version of FAST here. (Note: The computational time required to run the analysis on personal PCs is approximately ~1 minute.)

    ### set q= 15 here
    set.seed(2023)
    PRECASTObj <- FAST(PRECASTObj, q=15, fit.model='gaussian')

    ### Check the results
    str(PRECASTObj@resList)

Run possion version

Users can also use the possion version by the following command:

    set.seed(2023)
    PRECASTObj <- FAST(PRECASTObj, q=15)
    ### Check the results
    str(PRECASTObj@resList)

### Evaluate the adjusted McFadden’s pseudo R-square

Next, we investigate the performance of dimension reduction by calculating the adjusted McFadden’s pseudo R-square for each data batch. The manual annotations are regarded as the groud truth in the `meta.data` of each component of `PRECASTObj@seulist`.

    ## Obtain the true labels
    yList <- lapply(PRECASTObj@seulist, function(x) x$layer_guess_reordered)
    ### Evaluate the MacR2
    MacVec <- sapply(1:length(PRECASTObj@seulist), function(r) get_r2_mcfadden(PRECASTObj@resList$FAST$hV[[r]], yList[[r]]))
    ### output them
    print(MacVec)

## Embedding alignment and spatial clustering using iSC-MEB

Based on the embeddings from FAST, we use `iSC-MEB` to jointly align the embeddings and perform clustering. In this downstream analysis, other methods for embedding alignment and clustering can be also used. In the vignette of the simulated ST data, we show another method (`Harmony`+`Louvain`) to perform embedding alignment and clustering.

First, we use the function `RunHarmonyLouvain()` in `ProFAST` R package to select the number of clusters

    PRECASTObj <- RunHarmonyLouvain(PRECASTObj, resolution = 0.3)
    ARI_vec_louvain <- sapply(1:M, function(r) mclust::adjustedRandIndex(PRECASTObj@resList$Louvain$cluster[[r]], yList[[r]]))
    print(ARI_vec_louvain)

Then, we use the function `RuniSCMEB()` in `ProFAST` R package to jointly perform embedding alignment and spatial clustering. The results are saved in the slot `PRECASTObj@resList$iSCMEB`.

    PRECASTObj <- RuniSCMEB(PRECASTObj, seed=1)
    str(PRECASTObj@resList$iSCMEB)
    sapply(1:M, function(r) mclust::adjustedRandIndex(PRECASTObj@resList$iSCMEB$cluster[[r]], yList[[r]]))

## Remove unwanted variations in the log-normalized expression matrices

In the following, we remove the unwanted variations in the log-normalized expression matrices to obtain a combined log-normalized expression matrix in a Seurat object. For this real data, we use housekeeping genes as negative control to remove the unwanted variations. Specifically, we leverage the batch effect embeddings estimated through housekeeping gene expression matrix to capture unwanted variations. Additionally, we utilize the cluster labels obtained via iSC-MEB to retain the desired biological effects. The posterior probability of cluster labels (RList) are in the slot `PRECASTObj@resList$iSCMEB`.

    str(PRECASTObj@resList$iSCMEB)

To capture the unwanted variation, we first select the select the housekeeping genes using the function `SelectHKgenes()`.

    ## select the HK genes
    HKgenes <- SelectHKgenes(seuList, species= "Human", HK.number=200)
    seulist_HK <- lapply(seuList, function(x) x[HKgenes, ])
    seulist_HK

Then, we integrate the two sections by removing the unwanted variations by using `seulist_HK=seulist_HK` and `Method = "iSC-MEB"` in the function `IntegrateSRTData()`. After obtaining `seuInt`, we will see there are three embeddings: `FAST`, `iscmeb` and `position`, in the slot `seuInt@reductions`. `FAST` are the embeddings obtained by FAST model fitting and are uncorrected embeddings that may includes the unwanted effects (i.e., batch effects); `iscmeb`are the aligned embeddings obtained by iSC-MEB fitting; and `position` are the spatial coordinates.

    seuInt <- IntegrateSRTData(PRECASTObj, seulist_HK=seulist_HK, Method = "iSC-MEB",
                               seuList_raw=NULL, covariates_use=NULL, verbose=TRUE)
    seuInt

## Visualization

First, user can choose a beautiful color schema using `chooseColors()` in the R package `PRECAST`.

    cols_cluster <- chooseColors(palettes_name = "Nature 10", n_colors = 8, plot_colors = TRUE)

Then, we plot the spatial scatter plot for clusters using the function `SpaPlot()` in the R package `PRECAST`.

    p12 <- SpaPlot(seuInt, item = "cluster", batch = NULL, point_size = 1, cols = cols_cluster, combine = FALSE)
    library(ggplot2)
    p12 <- lapply(p12, function(x) x+ coord_flip() + scale_x_reverse())
    drawFigs(p12, layout.dim = c(1,2), common.legend = T)

We use the function `AddUMAP()` in the R package `PRECAST` to obtain the three-dimensional components of UMAP using the aligned embeddings in the reduction `harmony`.

    seuInt <- AddUMAP(seuInt, n_comp=3, reduction = 'iscmeb', assay = 'RNA')
    seuInt

We plot the spatial tNSE RGB plot to illustrate the performance in extracting features.

    p13 <- SpaPlot(seuInt, batch = NULL, item = "RGB_UMAP", point_size = 1, combine = FALSE, text_size = 15)
    p13 <- lapply(p13, function(x) x+ coord_flip() + scale_x_reverse())
    drawFigs(p13, layout.dim = c(1, 2), common.legend = TRUE, legend.position = "right", align = "hv")

We use the function `AddUTSNE()` in the R package `PRECAST` to obtain the two-dimensional components of UMAP using the aligned embeddings in the reduction `iscmeb`, and plot the tSNE plot based on the extracted features to check the performance of integration.

    seuInt <- AddTSNE(seuInt, n_comp = 2, reduction = 'iscmeb', assay = 'RNA')
    p1 <- dimPlot(seuInt, item = "cluster", point_size = 0.5, font_family = "serif", cols = cols_cluster,
        border_col = "gray10",  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 epxression analysis

Finally, we condut the combined differential expression analysis using the integrated log-normalized expression matrix saved in the `seuInt` object. The function `FindAllMarkers()` in the `Seurat` R package is ued to achieve this analysis.

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

Plot dot plot of normalized expressions of the layer-specified marker genes for each spatial domain identified by using the FAST embeddings.

    col_here <- c("#F2E6AB", "#9C0141")
    library(ggplot2)
    marker_Seurat <- readRDS("151507_DEGmarkerTop_5.rds")
    marker_Seurat <- lapply(marker_Seurat, function(x) transferGeneNames(x, Method='eg.db'))
    maker_genes <- unlist(lapply(marker_Seurat, toupper))
    features <- maker_genes[!duplicated(maker_genes)]
    p_dot <- DotPlot(seuInt, features=unname(features), cols=col_here, #  idents = ident_here,
                  col.min = -1, col.max = 1) + coord_flip()+ theme(axis.text.y = element_text(face = "italic"))+
      ylab("Domain") + xlab(NULL) + theme(axis.text.x = element_text(size=12, angle = 25, hjust = 1, family='serif'),
                                          axis.text.y = element_text(size=12, face= "italic", family='serif'))
    p_dot
    # table(unlist(yList), Idents(seuInt))

Based on the marker genes, we annotate the Domains 1-9 as Layer5/6, Layer3/4, Layer5, Layer2/3, Layer2/3, Layer6, WM, Layer5, Layer1. Then, we rename the Domains using the function `RenameIdents()`.

    seuInt2 <- RenameIdents(seuInt, '1' = 'Layer6', '2' = 'Layer2/3', '3'="Layer5", '4'="WM",
                               '5'='Layer3/4', '6'= 'Layer1')
    levels(seuInt2)  <- c("Layer1", "Layer2/3", "Layer3/4", "Layer5", "Layer6", "WM")
    p_dot2 <- DotPlot(seuInt2, features=unname(features), cols=col_here, #  idents = ident_here,
                  col.min = -1, col.max = 1) + coord_flip()+ theme(axis.text.y = element_text(face = "italic"))+
      ylab("Domain") + xlab(NULL) + theme(axis.text.x = element_text(size=12, angle = 25, hjust = 1, family='serif'),
                                          axis.text.y = element_text(size=12, face= "italic", family='serif'))
    p_dot2

Session Info

    sessionInfo()
    #> R version 4.4.1 (2024-06-14 ucrt)
    #> Platform: x86_64-w64-mingw32/x64
    #> Running under: Windows 11 x64 (build 26200)
    #>
    #> 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