Spatial Transcriptomics Analysis
Tissue microenvironment & metabolic zonation
Dormancy Focus: Intestinal villus atrophy zones, BAT activation regions, hypothalamic nucleus metabolic gradients
Example / Code
Reproducible workflow generator—not an online compute job

This page generates analysis code in your browser. It does not upload data or execute the workflow on the Dormanomics server. Review the generated parameters, software references and input requirements, then run and validate the code in your own computing environment.

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Load Visium/Slide-seq data, spot QC and filtering

Data Load & QC Parameters

Path to spaceranger_count/
Space Ranger output directory
Sample identifier
Spatial platform
Minimum UMI per spot
R Spatial Data Loading (Seurat) 
library(Seurat)
library(SeuratData)
library(ggplot2)

# Step 1.1: Load Visium data
so <- Load10X_Spatial(data.dir="{spaceranger_dir}", slice="{sample_id}")

# Step 1.2: QC
so[["percent.mt"]] <- PercentageFeatureSet(so, pattern="^mt-")
so <- subset(so, subset=nCount_Spatial > {min_counts} & percent.mt < 30)

# Step 1.3: Normalize
so <- SCTransform(so, assay="Spatial", verbose=FALSE)

# Step 1.4: Visualize H&E overlay
SpatialFeaturePlot(so, features="nCount_Spatial", pt.size.factor=1.5) + theme(legend.position="right")
ggsave("01_spatial_qc.pdf", width=8, height=6)

saveRDS(so, "01_spatial_qc.rds")
Configure parameters on the left, then click "Generate Code" to produce customized commands
Spatial clustering and domain identification with SVG analysis

Clustering & Domain Parameters

Upload .rds from Step 1
QC-filtered spatial object
Clustering resolution
Spatially variable gene detection
R Spatial Clustering & SVG 
library(Seurat)
library(SPARK)

so <- readRDS("01_spatial_qc.rds")

# Step 2.1: Dimension reduction and clustering
so <- RunPCA(so, assay="SCT", verbose=FALSE)
so <- FindNeighbors(so, reduction="pca", dims=1:30)
so <- FindClusters(so, verbose=FALSE, resolution={resolution})
so <- RunUMAP(so, reduction="pca", dims=1:30)

# Step 2.2: Spatial cluster visualization
SpatialDimPlot(so, label=TRUE, label.size=3, pt.size.factor=1.5)
ggsave("02_spatial_clusters.pdf", width=8, height=6)

# Step 2.3: Find SVGs with SPARK
raw_count <- GetAssayData(so, layer="counts")
info <- cbind.data.frame(x=so@images[[Images(so)[1]]]@coordinates$col,
                         y=so@images[[Images(so)[1]]]@coordinates$row)
rownames(info) <- colnames(raw_count)

spark <- CreateSPARKObject(counts=raw_count, location=info, percentage=0.1, min_total_counts=100)
spark <- spark.vc(spark, covariates=NULL, lib_size=NULL, num_core=4, verbose=FALSE)
spark <- spark.test(spark, check_positive=TRUE, verbose=FALSE)
write.csv(spark@res_mtest, "02_SVG_results.csv")

saveRDS(so, "02_spatial_clustered.rds")
Configure parameters on the left, then click "Generate Code" to produce customized commands
Cell-cell communication and niche interaction analysis

Niche & Interaction Parameters

Upload clustered .rds
Clustered spatial object
Upload scRNA-seq .rds
Reference scRNA-seq for deconvolution
Cell type deconvolution method
R Niche Analysis (RCTD Deconvolution) 
library(RCTD)
library(Seurat)

so <- readRDS("02_spatial_clustered.rds")
sc_ref <- readRDS("{sc_reference}")

# Step 3.1: Prepare RCTD reference
counts <- GetAssayData(sc_ref, layer="counts")
cell_types <- sc_ref$cell_type
names(cell_types) <- colnames(sc_ref)
nUMI <- sc_ref$nCount_RNA
names(nUMI) <- colnames(sc_ref)

reference <- Reference(counts, cell_types, nUMI)

# Step 3.2: Prepare spatial data
spatial_counts <- GetAssayData(so, layer="counts")
spatial_coords <- so@images[[Images(so)[1]]]@coordinates[,c("col","row")]
colnames(spatial_coords) <- c("x","y")

puck <- SpatialRNA(spatial_coords, spatial_counts, colSums(spatial_counts))

# Step 3.3: Run RCTD
my_rctd <- create.RCTD(puck, reference, max_cores=4)
my_rctd <- run.RCTD(my_rctd, doublet_mode="doublet")

# Step 3.4: Extract results
results <- my_rctd@results
weights <- as.data.frame(results$weights)
weights_norm <- weights / rowSums(weights)
so@meta.data <- cbind(so@meta.data, weights_norm)

# Step 3.5: Visualize deconvolution
SpatialFeaturePlot(so, features=colnames(weights_norm)[1:4], pt.size.factor=1.5, ncol=2)
ggsave("03_deconvolution.pdf", width=12, height=8)

saveRDS(so, "03_spatial_niche.rds")
Configure parameters on the left, then click "Generate Code" to produce customized commands
Integration with MALDI-MSI, IF staining, or morphology

Multi-modal Parameters

Upload niche .rds
Spatial object with deconvolution
Additional modality
Multi-modal integration method
R Multi-modal Integration 
library(Seurat)
library(ggplot2)

so <- readRDS("03_spatial_niche.rds")

# Step 4.1: Integrate metabolite data (MALDI-MSI)
maldi_data <- read.csv("{maldi_csv}", row.names=1)
so[["MALDI"]] <- CreateAssayObject(counts=maldi_data[, colnames(so)])
so <- NormalizeData(so, assay="MALDI")

# Step 4.2: Weighted Nearest Neighbor analysis
so <- FindMultiModalNeighbors(so, reduction.list=list("pca","MALDI.pca"),
                              dims.list=list(1:30, 1:10), modality.weight.name="MALDI.weight")
so <- RunUMAP(so, nn.name="weighted.nn", reduction.name="wnn.umap", reduction.key="wnnUMAP_")

# Step 4.3: Visualize
DimPlot(so, reduction="wnn.umap", label=TRUE)
ggsave("04_multimodal_umap.pdf", width=8, height=6)

# Step 4.4: Correlation between gene and metabolite
DefaultAssay(so) <- "SCT"
gene_expr <- FetchData(so, vars="Hmgcs2")
met_expr <- FetchData(so, vars="beta_hydroxybutyrate")
cor_result <- cor.test(gene_expr$Hmgcs2, met_expr$beta_hydroxybutyrate)
print(paste("Hmgcs2 vs beta-HB correlation:", round(cor_result$estimate, 3)))

saveRDS(so, "04_spatial_multimodal.rds")
Configure parameters on the left, then click "Generate Code" to produce customized commands
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