Spatial Omics

01 · Phenotypic Discovery

Find meaningful biological responses across large experimental spaces.

High-content experiments generate more images than scientists can examine directly, but scale alone does not produce insight. The analysis must preserve the relationship between perturbations, controls, cells, wells, plates, and batches while turning millions of individual observations into comparable phenotypic profiles.

Fovea Lab supports the full path from image correction and cellular segmentation to feature extraction, representation learning, batch correction, and biological readout. Each result remains connected to the conditions and quality checks that produced it, allowing researchers to distinguish reproducible phenotypes from technical variation and investigate rare responses that would otherwise disappear inside population averages.

02 · Space & Time

Follow biological systems as they form, move, and change.

Volumetric and time-resolved imaging reveals processes that cannot be understood from a single field or endpoint. Cells migrate, tissues reorganize, structures develop, and rare events emerge across large image volumes. Extracting those events requires more than processing individual frames: the analysis must remain consistent across depth, time, scale, and changing image quality.

Fovea Lab brings acquisition context, restoration, segmentation, registration, tracking, and quantitative measurement into one inspectable workflow. Researchers can follow an observation from a rendered volume or trajectory back to the source data and processing decisions that created it, while quality checks expose where depth, motion, density, or signal loss may have changed the reliability of the result.

03 · Tissue & Spatial Biology

Connect cellular organization with molecular and pathological context.

Tissue images contain information at several scales at once: cellular morphology, local neighborhoods, larger anatomical structures, molecular measurements, and clinically meaningful regions. These signals are often processed in separate systems, making it difficult to understand how a final classification, spatial association, or tissue-level measurement was formed.

Fovea Lab structures image processing, registration, object detection, spatial measurement, and model interpretation as one traceable analysis. Imaging and molecular information can remain aligned through each transformation, while provenance, cohort-aware validation, and reviewable outputs help researchers examine whether a finding is consistent across samples, scanners, staining conditions, and experimental sites.

Pipeline architectures, module by module.

WHAT IT IS
Molecules in place
Repeated imaging rounds are aligned into one mosaic, cells are segmented, and every cell carries its markers plus the identity of its neighbors.
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Multiplex tissue pipelines
Cross-cycle registration, segmentation and neighborhood analysis, with per-round alignment checks so a cell-type map reflects biology and not slide mechanics.
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RELATED
The run manifest
Without a locked panel manifest recorded with the run, comparing cell-type abundance across samples is guesswork rather than measurement.
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Pipeline backbone — reshapes per modality. Select a module for its sub-steps, tools, and deliverable.

Spatial Omics

High confidence

Ingest

Read raw multiplex tissue imaging outputs (cyclic-IF rounds, CODEX/PhenoCycler tiles, MERFISH/Xenium/CosMx transcript tables, Visium spot matrices, IMC MCD files) into a harmonized, chunked, coordinate-aligned representation — typically OME-Zarr for images and SpatialData/AnnData for molecules and matrices — preserving vendor metadata, codebooks, panel manifests, and ROI geometry.

Operations

Vendor-format parsingParse platform-native outputs — 10x Xenium output folders (cell_feature_matrix, transcripts.parquet, morphology images), Vizgen MERSCOPE CSV/HDF5/Parquet bundles, NanoString CosMx flat files, Akoya PhenoCycler QPTIFF tile stacks, CyCIF OME-TIFF cycles, IMC MCD/TXT files — using the appropriate loader or spatialdata-io reader.
Multi-round / multi-tile concatenationFor cyclic methods (CyCIF, CODEX, IMC), assemble per-cycle or per-tile files into a single multi-channel image stack, tracking round/channel indexing from the panel manifest.
Codebook and panel manifest ingestionLoad the gene codebook (MERFISH/Xenium/CosMx) or antibody panel (CyCIF/CODEX/IMC) associating each channel/barcode with a gene symbol (HGNC) or protein marker name, clone, conjugate, and cycle index.
Coordinate system registrationEstablish a unified physical coordinate system (micrometres) in SpatialData, linking image pixel grids, molecule coordinates, and ROI polygons to a common global-tissue reference frame.
OME-Zarr / SpatialData writeWrite images as chunked OME-Zarr (with multiscales pyramid), molecule tables as Parquet, and the full SpatialData Zarr store; validate OME-NGFF conformance.
Checksum and completeness validationVerify that all expected cycles/tiles/FOVs are present, file checksums match manifests, and no truncated files were ingested.

Inputs

Vendor-native raw outputs (cyclic-IF OME-TIFF stacks, MERFISH/Xenium/CosMx transcript tables and morphology images, Visium filtered_feature_bc_matrix, IMC MCD files)OME-TIFF · QPTIFF · MCD · CSV · Parquet · HDF5 · zarr
Panel manifest / codebook (gene-barcode or antibody-channel mapping)CSV · JSON · XLSX

Outputs

Harmonized multi-channel tissue image stackOME-Zarr · OME-TIFF
Molecule / transcript table with physical coordinatesParquet · CSV
SpatialData Zarr store (images + labels + shapes + tables unified)SpatialData Zarr
Panel / codebook manifest (canonical)JSON · CSV

QC checks

File completeness — Verify all expected cycle/tile/FOV files are present and non-truncated.
Checksum validation — Compare ingested file checksums against vendor-provided manifests.
Pixel size consistency — Confirm pixel size in OME metadata matches expected instrument value; flag discrepancies.
Panel completeness — Verify all channels in the panel manifest have corresponding image data; flag missing channels.
Coordinate system alignment — Spot-check that molecule coordinates (MERFISH/Xenium) fall within the image extent.

Failure modes it guards against

Truncated or missing cycle files — Checksum validation at ingest; fail-closed on incomplete file sets.
Incorrect pixel size in vendor metadata — Cross-check pixel size against instrument spec; store in OME-NGFF coordinate transform.
Channel/round index mismatch — Validate channel_to_marker_map against panel manifest at ingest; require explicit panel file.
Coordinate system mismatch between images and molecule tables — Enforce a single canonical coordinate system in SpatialData at ingest; test with spot overlap checks.

Tools (13)

spatialdata-ioPRIMARYOSS · BSD-3-Clause
Vendor-specific readers for Xenium, MERSCOPE/Vizgen, CosMx, Visium HD, PhenoCycler/CODEX, MACSima into SpatialData format — the recommended first-pass ingest layer.
SpatialDataPRIMARYOSS · BSD-3-Clause
Unified spatial omics data framework storing images, labels, shapes, points and tables with shared coordinate systems.
Bio-FormatsPRIMARYOSS · GPL-2.0-or-later
Read proprietary and OME microscopy files (OME-TIFF, QPTIFF, CZI, ND2, LIF, MCD); preserve vendor metadata.
ome-zarr-pyPRIMARYOSS · BSD-2-Clause
Write and validate OME-Zarr (OME-NGFF) image datasets with multiscales pyramids and coordinate transforms.
AICSImageIOOSS · BSD-3-Clause
Python reader/writer for OME-TIFF, CZI, ND2, LIF, and OME-Zarr; wraps Bio-Formats via bioformats_jar.
tifffileLibrary · BSD-3-Clause
Read and write multi-page TIFF and OME-TIFF files; used for CyCIF cycle stacks and tile assembly.
zarrLibrary · MIT
Chunked N-dimensional array storage underpinning OME-Zarr/SpatialData; direct use for custom array writes.
MCMICROOSS · MIT
End-to-end Nextflow pipeline for CyCIF/CODEX; handles ingest (Bio-Formats → OME-TIFF), registration (ASHLAR), segmentation and feature extraction.
sopaOSS · BSD-3-Clause
Technology-invariant SpatialData-native pipeline supporting Xenium, MERSCOPE, CosMx, PhenoCycler, Visium HD ingest and downstream analysis.
Xenium Ranger (10x Genomics)Commercial · 10x Genomics EULA (commercial; verify)
Off-instrument re-analysis of Xenium data including relabeling, re-segmentation, and output of canonical Xenium folder structure.
Vizgen Post-processing Tool (VPT)Commercial · Apache-2.0
Re-analysis and cell-boundary re-segmentation of MERSCOPE/MERFISH datasets from Vizgen.
steinbockOSS · MIT
IMC (Imaging Mass Cytometry) raw MCD data pre-processing, including image export, hot-pixel filtering and panel ingestion.
AnnDataLibrary · BSD-3-Clause
In-memory and on-disk (HDF5/h5ad) cell×gene/feature matrix with obs/var metadata; standard exchange format for spatial expression tables.

Metadata captured

Field	Type	Example	Standard
pipeline_run_id	string	spatial-omics-ingest-2026-06-05-ab3f	—
technology	enum	xenium	MITI
instrument_model	string	Xenium Analyzer (10x Genomics); MERSCOPE Ultra (Vizgen)	MITI
panel_name_version	string	Human Tissue Tumor Microenvironment Panel v1.0 (Xenium)	MITI
marker_or_gene_panel	array	CD3E, EPCAM, KRT8, PTPRC, MKI67	HGNC
n_cycles_or_rounds	int (count)	12	MITI
channel_to_marker_map	object	{'c0': 'DAPI', 'c1': 'CD3-AF488', 'c2': 'EPCAM-AF555'}	OME-NGFF
pixel_size_um	float (micrometer)	0.2125	OME-NGFF
roi_id	string	ROI_001	SpatialData
coordinate_system_name	string	global_tissue_um	SpatialData
input_file_checksums	object	{'morphology_mip.ome.tif': 'sha256:abcd1234'}	—
software_versions	object	{'spatialdata_io': '0.1.5', 'spatialdata': '0.2.2', 'python': '3.11', 'container': 'sha256:...'}	REMBI

You get

OME-Zarr image store · OME-Zarr
SpatialData Zarr store · SpatialData Zarr
Canonical panel manifest · JSON
Ingest QC report · HTML/JSON

References

Spatial Omics: a pipeline architecture for trustworthy imaging.

Find meaningful biological responses across large experimental spaces.

Follow biological systems as they form, move, and change.

Connect cellular organization with molecular and pathological context.

Pipeline architectures, module by module.

Ingest