FAST: single DLPFC section

Wei Liu


This vignette introduces the FAST workflow for the analysis of single-section rather than multi-secitons data, one humn dorsolateral prefrontal cortex (DLPFC) spatial transcriptomics dataset. In this vignette, the workflow of FAST consists of three steps

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

githubURL <- ""

Then load to R. Here, we only focus one section.

dlpfc2 <- readRDS("./seulist2_ID9_10.RDS")
dlpfc <- dlpfc2[[1]]

The package can be loaded with the command:

library(ProFAST) # load the package of FAST method

View the DLPFC data

First, we view the the spatial transcriptomics data with Visium platform. There are ~15000 genes and ~3600 spots.

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

We observed that 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.

count <- dlpfc[['RNA']]@counts
row.names(count) <- unname(transferGeneNames(row.names(count), now_name = "ensembl",
                                                 species="Human", Method='eg.db'))
seu <- CreateSeuratObject(counts = count, =


We show how to preprocessing before fitting FAST, including log-normalization (if user use the gaussian version of FAST), and select highly variable genes.

## Check the spatial coordinates: they are named as "row" and "col"!
seu <- NormalizeData(seu)
seu <- FindVariableFeatures(seu)

Find spatially variable genes Users can also use the spatially variable genes by the following command:

Fit FAST for this data

For function FAST_single, 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-normalized matrix while the Poisson verion (poisson) models the count matrix; default as poisson. (Note: The computational time required to run the analysis on personal PCs is approximately ~0.5 minute on a personal PC.)

Adj_sp <- AddAdj(as.matrix([,c("row", "col")]), platform = "Visium")
### set q= 15 here
seu <- FAST_single(seu, Adj_sp=Adj_sp, q= 15, fit.model='poisson')

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

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. The manual annotations are regarded as the ground truth in the of seu.

Clustering analysis based on FAST embedding

Based on the embeddings from FAST, we use Louvain to perform clustering. In this downstream analysis, other methods for clustering can be also used.

seu <- FindNeighbors(seu, reduction = 'fast')
seu <- FindClusters(seu, resolution = 0.4)
seu$fast.cluster <- seu$seurat_clusters <- mclust::adjustedRandIndex(y, seu$fast.cluster)
print(paste0("ARI of PCA is ", round(, 3)))

For comparison, we also run PCA to obtain PCA embeddings, and then conduct louvain clustering.

seu <- ScaleData(seu)
seu <- RunPCA(seu, npcs=15, verbose=FALSE)
Mac.pca <- get_r2_mcfadden(Embeddings(seu, reduction='pca'), y)
print(paste0("MacFadden's R-square of PCA is ", round(Mac.pca, 3)))
seu <- FindNeighbors(seu, reduction = 'pca', ="pca.graph")
seu <- FindClusters(seu, resolution = 0.8, = 'pca.graph') 
seu$pca.cluster <- seu$seurat_clusters
ARI.pca <- mclust::adjustedRandIndex(y, seu$pca.cluster)
print(paste0("ARI of PCA is ", round(ARI.pca, 3)))


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 DimPlot() in the R package Seurat. We observe that the clusters from PCA are more messy while the clusters from FAST are more smoothing in spatial coordinates.

seu <- PRECAST::Add_embed(embed = as.matrix([,c("row", "col")]), seu, embed_name = 'Spatial')
p1 <- DimPlot(seu, reduction = 'Spatial', = 'pca.cluster',cols = cols_cluster, pt.size = 1.5)
p2 <- DimPlot(seu, reduction = 'Spatial', = 'fast.cluster',cols = cols_cluster, pt.size = 1.5)
drawFigs(list(p1, p2),layout.dim = c(1,2) )

Next, we visualize the clusters from FAST on the UMAP space, and observe the clusters are well separated in general.

seu <- RunUMAP(seu, reduction = "fast", dims=1:15)
DimPlot(seu, reduction='umap', = "fast.cluster")

Differential epxression analysis

Finally, we condut the differential expression (DE) analysis. The function FindAllMarkers() in the Seurat R package is ued to achieve this analysis. And we extract the top five DE genes.

Idents(seu) <- seu$fast.cluster
dat_deg <- FindAllMarkers(seu)
n <- 5
dat_deg %>%
    group_by(cluster) %>%
    top_n(n = n, wt = avg_log2FC) -> top5

Session Info