Loads spatial transcriptomics data from Visium, Visium HD, Xenium, MERFISH/MERSCOPE, CosMx, Slide-seq/Curio, and Stereo-seq into AnnData or SpatialData using spatialdata-io and Squidpy. Use when deciding which platform class is in hand (imaging/in-situ vs sequencing/capture), which reader matches the platform (spatialdata_io.xenium/merscope/cosmx vs squidpy.read.visium/vizgen/nanostring), whether to work from the per-transcript molecule table (the re-segmentable source of truth) or the segmentation-derived per-cell matrix (quality-filtered, inherits all segmentation error), whether a molecule table even exists (spot platforms have none), and how to keep coordinate frames and units (pixel vs micron) registered to histology.
Loads spatial transcriptomics data from Visium, Visium HD, Xenium, MERFISH/MERSCOPE, CosMx, Slide-seq/Curio, and Stereo-seq into AnnData or SpatialData using spatialdata-io and Squidpy. Use when deciding which platform class is in hand (imaging/in-situ vs sequencing/capture), which reader matches the platform (spatialdata_io.xenium/merscope/cosmx vs squidpy.read.visium/vizgen/nanostring), whether to work from the per-transcript molecule table (the re-segmentable source of truth) or the segmentation-derived per-cell matrix (quality-filtered, inherits all segmentation error), whether a molecule table even exists (spot platforms have none), and how to keep coordinate frames and units (pixel vs micron) registered to histology.
Before using code patterns, verify installed versions match. If versions differ:
Python: pip show <package> then help(module.function) to check signatures
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
Spatial Data I/O
"Load my spatial data" -> Parse a platform's output bundle into one coordinate frame holding the expression matrix, coordinates, images, and (for imaging) the molecule table and segmentation shapes.
Imaging/in-situ (Xenium, MERSCOPE/MERFISH, CosMx, seqFISH): spatialdata_io.{xenium, merscope, cosmx} -> SpatialData with a per-transcript points table AND a derived per-cell tables matrix.
Sequencing/capture (Visium, Visium HD, Slide-seq/Curio, Stereo-seq): squidpy.read.visium or spatialdata_io.{visium, visium_hd, curio, stereoseq} -> spot/bin matrix + coordinates; NO molecule table.
Governing Principle
The single most consequential I/O fact is that the two platform classes emit different primary objects, and one class emits two of them that are easy to confuse.
Imaging/in-situ platforms emit TWO physically distinct primary objects. The first is a per-TRANSCRIPT molecule table -- one row per decoded molecule with x, y (and often z), gene, a decoding-quality value, and a cell-assignment-or-unassigned. The second is a per-CELL expression matrix, genes-by-cells, DERIVED by overlaying a segmentation and counting the molecules that fall inside each boundary. The matrix looks exactly like scRNA-seq and is therefore wrongly trusted as ground truth, but it is a downstream product: it inherits every segmentation error and is usually quality-filtered (Xenium keeps Q>=20 in the matrix while the transcript table keeps everything). The molecule table is the source of truth and the ONLY object that lets the analyst re-segment, recover unassigned molecules, or do subcellular work. A loader that returns only the cell matrix has silently discarded the re-segmentable layer.
Sequencing/capture platforms (Visium, Slide-seq, Stereo-seq) have NO molecule table -- a spot/bead/bin is mini-bulk over the cells beneath it, captured as a single barcoded profile. Do not go looking for a transcript table that does not exist; the only objects are the barcode-by-gene matrix, the coordinates, and the tissue image.
A second trap is coordinate frames. Images, spot/cell coordinates, and molecule points each live in their own intrinsic pixel or array axes; overlaying transcripts on histology, or building a neighbor graph with a micron radius, requires the right transform (Visium scalefactors; imaging micron-to-pixel matrices). SpatialData makes the frames explicit (intrinsic vs a shared extrinsic "global" system); the legacy AnnData layout hides them in uns['spatial'][library_id]['scalefactors']. Mixing frames silently places points off the image or builds a graph at the wrong scale.
The Platform-Class Fork
The first question of any spatial dataset is which side of the fork it sits on, because it decides what objects exist and what the rest of the pipeline must do.
spot/bin = 1-10-cell MIXTURE (GeoMx ROI = many-cell region; sub-cell bins = fraction of a cell)
deconvolution (or bin/segment-up for sub-cell bins)
Object-Model Landscape
Each toolkit is a strategy for co-storing four things in one frame: an expression matrix, geometry (spot circles, cell/nucleus polygons, centroids), raster images (H&E, DAPI, multiplex IF -- often gigapixel), and (imaging only) the molecule point cloud. They differ in how separately they keep these and which is the forward path.
Framework (language)
Core object
Molecule table
Per-cell matrix
Segmentation geometry
Images
Best when
SpatialData / scverse (Python)
SpatialData of elements
points (dask -> Parquet)
tables (AnnData)
labels (masks) + shapes (geopandas polygons)
images (xarray, OME-NGFF/Zarr, lazy)
Multimodal, larger-than-memory, multiple platforms in one store; re-segmentation
Squidpy + AnnData (Python)
AnnData
via SpatialData/readers
adata.X
in obs/external
uns['spatial'] (legacy)
Standard AnnData spatial graph stats on spot or cell matrices
One technology-agnostic object spanning molecule -> cell -> region
SpatialData is the forward-path Python standard because it is the only framework that natively keeps the molecule table, multiscale OME-NGFF images, and segmentation shapes as first-class, frame-aware elements. Because a SpatialData table IS an AnnData, all of squidpy.gr.* runs on it unchanged.
Platform-to-Reader Map
Platform
spatialdata-io reader
squidpy.read
Key I/O fact
Visium
visium
visium
spot, no molecule table; tissue_positions.csv gained a header at Space Ranger v2.0 (readers handle both)
Visium HD
visium_hd
--
bins (2/8/16um); tissue_positions.parquet (PARQUET, not CSV); 8um bin still spans ~2 cells
there is NO merfish reader -- merscope handles both; boundaries went hdf5-folder -> single cell_boundaries.parquet at instrument SW v232
CosMx
cosmx
nanostring
flat CSVs with a run/slide prefix; tx_file (molecule table) absent for protein-only panels
Slide-seq / Curio
curio
-- (none)
bead, no molecule table
Stereo-seq
stereoseq
--
DNB sub-cellular; binned up to cells
squidpy.read provides ONLY visium, vizgen, and nanostring -- it has no xenium or slideseq reader. For Xenium, Slide-seq/Curio, Stereo-seq, and Visium HD, use the spatialdata_io reader. scanpy.read_visium is deprecated as of scanpy 1.11 -- prefer squidpy.read.visium (identical obsm/uns layout) or spatialdata_io.visium.
Load Visium (Spot/Capture)
Goal: Read a Space Ranger bundle into an AnnData with coordinates, image, and scalefactors, without reaching for the deprecated scanpy reader.
Approach: Use squidpy.read.visium; coordinates land in obsm['spatial'] (pixels of the full-res image), image and scalefactors in uns['spatial'][library_id].
import squidpy as sq
adata = sq.read.visium('spaceranger_out/') # filtered matrix + spatial/; NOT a cell -- each spot is a 1-10-cell mixture
library_id = list(adata.uns['spatial'].keys())[0]
scalef = adata.uns['spatial'][library_id]['scalefactors']
# obsm['spatial'] is in FULL-RES pixels; multiply by tissue_hires_scalef to index the hires imageprint(adata.n_obs, 'spots', adata.n_vars, 'genes', '| spot diameter (px):', scalef['spot_diameter_fullres'])
Load Imaging Data and Keep the Molecule Table
Goal: Load Xenium (or MERSCOPE/CosMx) so BOTH the per-transcript molecule table and the derived cell matrix are available, not just the matrix.
Approach: Use the spatialdata_io reader, which returns a SpatialData object; the molecule table lives in sdata.points, the cell matrix in sdata.tables, segmentation polygons in sdata.shapes, images in sdata.images. Inspect element names with print(sdata) -- they vary by platform and reader version.
import spatialdata_io as sdio
sdata = sdio.xenium('xenium_out/') # needs experiment.xenium manifestprint(sdata) # lists points/tables/shapes/images element names
transcripts = sdata.points['transcripts'] # dask DataFrame: x, y, z, feature_name, qv, cell_id -- ALL Q-scores
adata = sdata.tables['table'] # AnnData cell matrix -- Q>=20 filtered, inherits segmentation error# the matrix is a DERIVED product; the molecule table is the re-segmentable source of truthprint('molecules:', transcripts.shape[0].compute(), '| cells in matrix:', adata.n_obs)
For MERSCOPE substitute sdio.merscope('merscope_out/'); for CosMx sdio.cosmx('cosmx_out/'). As an AnnData-only alternative for MERSCOPE, sq.read.vizgen(path, counts_file='cell_by_gene.csv', meta_file='cell_metadata.csv') returns the cell matrix but discards the molecule table.
Load Other Capture Platforms
Goal: Load Visium HD bins, Slide-seq/Curio beads, or Stereo-seq into a SpatialData object.
Approach: Use the matching spatialdata_io reader; none of these has a molecule table, and Visium HD / Stereo-seq bins are smaller than a cell (the inverse-of-deconvolution regime -- see spatial-deconvolution).
import spatialdata_io as sdio
sdata_hd = sdio.visium_hd('visium_hd_out/') # tissue_positions are PARQUET; pick a bin (8um default still ~2 cells)
sdata_ss = sdio.stereoseq('stereoseq_out/') # DNB sub-cellular spots, binned up to cells
sdata_bead = sdio.curio('slideseq_out/') # Slide-seq/Curio beads; ~1 cell but ~1/3 carry >=2 types
Inspect and Register Coordinate Frames
Goal: Confirm whether coordinates are in pixels or microns before building a graph or overlaying on histology, so a neighbor radius or a plotted point lands at the right scale.
Approach: In SpatialData read the element transformations (intrinsic vs the shared "global" extrinsic frame); in the AnnData layout read the scalefactors. Never assume obsm['spatial'] units -- Visium is full-res pixels, most imaging readers place a micron "global" frame.
from spatialdata.transformations import get_transformation
# SpatialData: every element carries transforms into shared coordinate systemsprint(sdata.coordinate_systems) # e.g. ['global']print(get_transformation(sdata['transcripts'], get_all=True)) # intrinsic -> global (often micron scaling)
Convert SpatialData to AnnData
Goal: Extract the cell/spot matrix as a plain AnnData for tools that expect one, while keeping coordinates.
Approach: Copy the table, set obsm['spatial'] from the matching shapes/centroids. Persist via sdata.write(...) ONLY to a scratch path -- a .zarr store is a DIRECTORY, not a file, so it must never be committed.
adata = sdata.tables['table'].copy()
# write a zarr STORE (a directory) to scratch, never the repo; rm -rf when done# sdata.write('/tmp/scratch/store.zarr')
Common Errors
Symptom
Cause
Fix
AttributeError: module 'squidpy.read' has no attribute 'xenium' (or slideseq)
squidpy.read only has visium, vizgen, nanostring
Use spatialdata_io.xenium / spatialdata_io.curio for those platforms
spatialdata_io has no merfish reader
The reader is named for the instrument, not the chemistry
Use spatialdata_io.merscope (handles MERFISH and MERSCOPE)
DeprecationWarning / future removal on scanpy.read_visium
Deprecated as of scanpy 1.11
Use squidpy.read.visium or spatialdata_io.visium (same layout)
Cell matrix has far fewer transcripts than the molecule table
Imaging cell matrix is Q>=20 filtered and segmentation-derived
Treat the matrix as provisional; use sdata.points (all Q) to re-segment or audit
Trusting the cell matrix as ground truth; weird co-expression
The matrix inherits all segmentation/spillover error
Validate against the molecule table; re-segment (see image-analysis)
Looking for a transcript table in Visium/Slide-seq and finding none
Capture platforms have no molecule table
Stop -- a spot is mini-bulk; there is nothing to re-segment
Visium HD reader fails reading tissue_positions.csv
Visium HD positions are PARQUET (tissue_positions.parquet)
Use spatialdata_io.visium_hd, which expects the parquet bundle
Older Visium positions parse with a shifted header
tissue_positions.csv gained a header at Space Ranger v2.0
Current readers handle both; upgrade spatialdata-io/squidpy if parsing legacy files
MERSCOPE boundaries not found
hdf5-folder boundaries became single cell_boundaries.parquet at SW v232
Match reader version to instrument software; point at the parquet if present
Seurat @coordinates slot missing (R interop)
Seurat 5.1 VisiumV2 has no coordinates slot (V1 did)
Use GetTissueCoordinates(); there is no V1->V2 converter
Transcripts plot off the image
Points and image in different frames/units (pixel vs micron)
Apply the reader's transform / scalefactor before overlaying
A stray .zarr directory left in the repo after writing
sdata.write makes a directory store; git status hides untracked dirs
Write to scratch; rm -rf the store; run `git status --porcelain
Related Skills
spatial-preprocessing - QC floors and normalization that differ by platform class after loading
image-analysis - re-segment the molecule table; the cell matrix is a segmentation hypothesis
spatial-deconvolution - recover cell-type proportions from spot mixtures that have no molecule table
high-resolution-binning - bin/segment-up sub-cellular Visium HD and Stereo-seq captures
spatial-visualization - plot spots vs imaging FOVs with the correct coordinate frame
single-cell/data-io - non-spatial scRNA-seq loading for the deconvolution/label-transfer reference
References
Marconato L, Palla G, Yamauchi KA, et al. (2025) SpatialData: an open and universal data framework for spatial omics. Nature Methods 22(1):58-62. DOI 10.1038/s41592-024-02212-x
Palla G, Spitzer H, Klein M, et al. (2022) Squidpy: a scalable framework for spatial omics analysis. Nature Methods 19(2):171-178. DOI 10.1038/s41592-021-01358-2
Wolf FA, Angerer P, Theis FJ (2018) SCANPY: large-scale single-cell gene expression data analysis. Genome Biology 19:15. DOI 10.1186/s13059-017-1382-0
Virshup I, Rybakov S, Theis FJ, Angerer P, Wolf FA (2024) anndata: Access and store annotated data matrices. Journal of Open Source Software 9(101):4371. DOI 10.21105/joss.04371
Hao Y, Stuart T, Kowalski MH, et al. (2024) Dictionary learning for integrative, multimodal and scalable single-cell analysis. Nature Biotechnology 42(2):293-304. DOI 10.1038/s41587-023-01767-y
Righelli D, Weber LM, Crowell HL, et al. (2022) SpatialExperiment: infrastructure for spatially-resolved transcriptomics data in R using Bioconductor. Bioinformatics 38(11):3128-3131. DOI 10.1093/bioinformatics/btac299
Moore J, Allan C, Besson S, et al. (2021) OME-NGFF: a next-generation file format for expanding bioimaging data-access strategies. Nature Methods 18:1496-1498. DOI 10.1038/s41592-021-01326-w
Janesick A, Shelansky R, Gottscho AD, et al. (2023) High resolution mapping of the tumor microenvironment using integrated single-cell, spatial and in situ analysis (Xenium). Nature Communications 14:8353. DOI 10.1038/s41467-023-43458-x
