Introduction
Spatial transcriptomics spans a range of technologies – from spot-based platforms like Visium and Slide-seq to subcellular-resolution methods like Xenium and MERFISH. scConvert handles spatial data from any platform that produces a Seurat object or h5ad file, preserving coordinates, images, and metadata through format conversion.
Supported spatial technologies
| Technology | Resolution | Coordinates | Images | h5ad roundtrip |
|---|---|---|---|---|
| 10x Visium | ~55 um spots | Gridded | H&E tissue | Full support |
| 10x Visium HD | 2–8 um bins | Dense grid | H&E tissue | Coordinates + image |
| Slide-seq v2 | ~10 um beads | Continuous | None | Coordinates |
| 10x Xenium | Subcellular | Molecule-based | DAPI/IF | Coordinates via FOV |
| MERFISH (Vizgen) | Subcellular | Molecule-based | DAPI | Coordinates via FOV |
| CosMx (NanoString) | Subcellular | Molecule-based | Morphology | Coordinates via FOV |
| CODEX (Akoya) | Cell-level | Centroids | Fluorescence | Coordinates |
| Stereo-seq (BGI) | ~0.5 um | Continuous | H&E | Coordinates |
All technologies store spatial coordinates in the h5ad
obsm/spatial field. Visium additionally stores tissue
images and scale factors in uns/spatial.
Visium example: mouse brain
We demonstrate a full roundtrip with the shipped Visium demo dataset (400 mouse brain spots, 1,500 genes, 15 clusters).
spatial_path <- system.file("extdata", "spatial_demo.rds", package = "scConvert")
brain <- readRDS(spatial_path)
cat("Spots:", ncol(brain), "\n")
#> Spots: 400
cat("Genes:", nrow(brain), "\n")
#> Genes: 1500
cat("Image:", Images(brain), "\n")
#> Image: anterior1
cat("Assay:", Assays(brain), "\n")
#> Assay: SpatialSpatial gene expression
Ttr (Transthyretin) is expressed in the choroid plexus and shows strong spatial localization.
SpatialFeaturePlot(brain, features = "Ttr", pt.size.factor = 1.6) +
ggplot2::ggtitle("Ttr expression (original)")
Convert and load back
h5ad_file <- tempfile(fileext = ".h5ad")
writeH5AD(brain, h5ad_file, overwrite = TRUE)
brain_rt <- readH5AD(h5ad_file, verbose = FALSE)
cat("Roundtrip spots:", ncol(brain_rt), "\n")
#> Roundtrip spots: 400
cat("Roundtrip genes:", nrow(brain_rt), "\n")
#> Roundtrip genes: 1500
cat("Image preserved:", length(Images(brain_rt)) > 0, "\n")
#> Image preserved: TRUEVerify coordinate preservation
cat("Barcodes match:", all(colnames(brain) == colnames(brain_rt)), "\n")
#> Barcodes match: TRUE
cat("Features match:", all(rownames(brain) == rownames(brain_rt)), "\n")
#> Features match: TRUE
cat("Clusters match:",
all(as.character(brain$seurat_clusters) ==
as.character(brain_rt$seurat_clusters)), "\n")
#> Clusters match: TRUEClusters preserved through roundtrip
DimPlot(brain_rt, reduction = "umap", group.by = "seurat_clusters",
label = TRUE, repel = TRUE) +
ggplot2::ggtitle("UMAP clusters (after h5ad roundtrip)")
Another gene: Nrgn
Nrgn (Neurogranin) is a cortical neuron marker with a different spatial pattern than Ttr.
FeaturePlot(brain_rt, features = "Nrgn", reduction = "umap") +
ggplot2::ggtitle("Nrgn expression (UMAP, after roundtrip)")
Working with other spatial platforms
For any h5ad file with spatial coordinates, the same workflow applies:
# Load spatial h5ad from any platform
obj <- readH5AD("merfish_data.h5ad")
# Check what was detected
cat("Images:", Images(obj), "\n")
# Convert to another format
scConvert("merfish_data.h5ad", "merfish_data.h5seurat")Technologies without tissue images (Slide-seq, MERFISH, CODEX) store
only coordinates in obsm/spatial. scConvert reads these
into the Seurat object and makes them available for plotting and
downstream analysis.
Session Info
sessionInfo()
#> R version 4.5.2 (2025-10-31)
#> Platform: aarch64-apple-darwin20
#> Running under: macOS Tahoe 26.3
#>
#> Matrix products: default
#> BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.1
#>
#> locale:
#> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#>
#> time zone: America/Indiana/Indianapolis
#> tzcode source: internal
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] ggplot2_4.0.2 Seurat_5.4.0 SeuratObject_5.3.0 sp_2.2-1
#> [5] scConvert_0.1.0
#>
#> loaded via a namespace (and not attached):
#> [1] RColorBrewer_1.1-3 jsonlite_2.0.0 magrittr_2.0.4
#> [4] spatstat.utils_3.2-2 farver_2.1.2 rmarkdown_2.30
#> [7] fs_1.6.7 ragg_1.5.0 vctrs_0.7.1
#> [10] ROCR_1.0-12 spatstat.explore_3.7-0 htmltools_0.5.9
#> [13] sass_0.4.10 sctransform_0.4.3 parallelly_1.46.1
#> [16] KernSmooth_2.23-26 bslib_0.10.0 htmlwidgets_1.6.4
#> [19] desc_1.4.3 ica_1.0-3 plyr_1.8.9
#> [22] plotly_4.12.0 zoo_1.8-15 cachem_1.1.0
#> [25] igraph_2.2.2 mime_0.13 lifecycle_1.0.5
#> [28] pkgconfig_2.0.3 Matrix_1.7-4 R6_2.6.1
#> [31] fastmap_1.2.0 MatrixGenerics_1.22.0 fitdistrplus_1.2-6
#> [34] future_1.69.0 shiny_1.13.0 digest_0.6.39
#> [37] S4Vectors_0.48.0 patchwork_1.3.2 tensor_1.5.1
#> [40] RSpectra_0.16-2 irlba_2.3.7 GenomicRanges_1.62.1
#> [43] textshaping_1.0.4 labeling_0.4.3 progressr_0.18.0
#> [46] spatstat.sparse_3.1-0 httr_1.4.8 polyclip_1.10-7
#> [49] abind_1.4-8 compiler_4.5.2 bit64_4.6.0-1
#> [52] withr_3.0.2 S7_0.2.1 fastDummies_1.7.5
#> [55] MASS_7.3-65 tools_4.5.2 lmtest_0.9-40
#> [58] otel_0.2.0 httpuv_1.6.16 future.apply_1.20.2
#> [61] goftest_1.2-3 glue_1.8.0 nlme_3.1-168
#> [64] promises_1.5.0 grid_4.5.2 Rtsne_0.17
#> [67] cluster_2.1.8.2 reshape2_1.4.5 generics_0.1.4
#> [70] hdf5r_1.3.12 gtable_0.3.6 spatstat.data_3.1-9
#> [73] tidyr_1.3.2 data.table_1.18.2.1 XVector_0.50.0
#> [76] BiocGenerics_0.56.0 BPCells_0.2.0 spatstat.geom_3.7-0
#> [79] RcppAnnoy_0.0.23 ggrepel_0.9.7 RANN_2.6.2
#> [82] pillar_1.11.1 stringr_1.6.0 spam_2.11-3
#> [85] RcppHNSW_0.6.0 later_1.4.8 splines_4.5.2
#> [88] dplyr_1.2.0 lattice_0.22-9 survival_3.8-6
#> [91] bit_4.6.0 deldir_2.0-4 tidyselect_1.2.1
#> [94] miniUI_0.1.2 pbapply_1.7-4 knitr_1.51
#> [97] gridExtra_2.3 Seqinfo_1.0.0 IRanges_2.44.0
#> [100] scattermore_1.2 stats4_4.5.2 xfun_0.56
#> [103] matrixStats_1.5.0 UCSC.utils_1.6.1 stringi_1.8.7
#> [106] lazyeval_0.2.2 yaml_2.3.12 evaluate_1.0.5
#> [109] codetools_0.2-20 tibble_3.3.1 cli_3.6.5
#> [112] uwot_0.2.4 xtable_1.8-8 reticulate_1.45.0
#> [115] systemfonts_1.3.1 jquerylib_0.1.4 GenomeInfoDb_1.46.2
#> [118] dichromat_2.0-0.1 Rcpp_1.1.1 globals_0.19.1
#> [121] spatstat.random_3.4-4 png_0.1-8 spatstat.univar_3.1-6
#> [124] parallel_4.5.2 pkgdown_2.2.0 dotCall64_1.2
#> [127] listenv_0.10.1 viridisLite_0.4.3 scales_1.4.0
#> [130] ggridges_0.5.7 purrr_1.2.1 crayon_1.5.3
#> [133] rlang_1.1.7 cowplot_1.2.0