| --- |
| pretty_name: SpatialDataAgent-DB Public Catalog |
| license: other |
| license_name: source-specific-data-terms |
| license_link: https://huggingface.co/datasets/Biogod/SpatialDataAgent-DB#data-use-and-source-terms |
| tags: |
| - biology |
| - spatial-transcriptomics |
| - metadata |
| configs: |
| - config_name: metadata_v2 |
| data_files: metadata/v2/samples_metadata_v2.parquet |
| default: true |
| - config_name: samples |
| data_files: catalog/samples.parquet |
| --- |
| # SpatialDataAgent-DB Public Catalog |
|
|
|  |
|
|
| *Left to right: H&E and GFAP from the same Visium section (GSM9721856), Xenium DAPI (GSM9301054), and a CosMx composite (GSM9372712). The GFAP panel displays all 3,416 recorded spots using the existing log1p preview values.* |
|
|
| Search 12,157 public spatial transcriptomics samples before you request any data. They come from 1,264 published |
| GEO series and hold 635,244,564 spatial observations (spots, bins, cells or beads). 8,196 samples have a released |
| tissue image: 5,247 H&E, 871 H&E-like (a pixel-based call that no reviewer confirmed), 1,424 DAPI, 427 other fluorescence and 227 other or |
| undetermined stains. Platforms include Visium, Xenium, legacy ST, CosMx, MERFISH/MERSCOPE, Slide-seq, Stereo-seq and Visium HD. |
|
|
| The samples were found and standardized from GEO by the [SpatialDataAgent](https://github.com/YSTLab/spatial-data-agent) |
| workflow (7,789 samples, each checked by an independent scorer) and by the earlier HESRT H&E collection (4,368 |
| samples, each reviewed by eye). On 2026-11-11, 12 samples were replaced and 6 were withdrawn after a check |
| against the GEO deposits, and the image of 9 misaligned samples was removed; see [Corrections 2026-11-11](#corrections-2026-11-11). The atlas is described in the |
| [bioRxiv preprint](https://doi.org/10.64898/2026.05.27.727615). |
|
|
| This repository needs **no account**. It holds the catalog of all 12,157 samples and the complete files of 86 |
| starter samples (below). The files of every sample are in the |
| [full dataset](https://huggingface.co/datasets/Biogod/SpatialDataAgent-DB), about 1.8 TB, where access is |
| approved manually. |
|
|
| ## Try it now, without an account: the starter pack |
|
|
|  |
|
|
| 86 samples of the release are in this repository and need **no login**. Each has the full three files: |
| `spatial.h5ad`, `sample.json` and, for 58 of them, `image.tif`. The pack covers: |
|
|
| - 12 platforms, 20 organ groups and 8 species; |
| - 43 H&E images (8 at cell resolution, ≤ 1 µm per pixel) and 15 DAPI or CosMx images; |
| - 41 samples with a reported disease and 77 from published studies; |
| - 5.1 GB in total. |
|
|
| The files are byte-identical to the full dataset, and each is checked against its SHA256 when downloaded. |
| `starter/starter.json` lists the samples. |
|
|
| [Open the five-minute notebook in Colab](https://colab.research.google.com/github/Biogod2020/spatial-data-agent-db/blob/main/examples/starter_pack.ipynb), |
| or: |
|
|
| ```bash |
| pip install "spatial-data-agent-db[analysis] @ git+https://github.com/Biogod2020/spatial-data-agent-db" |
| sda-db starter --has-image # list the pack |
| sda-db download GSM6963121 --release starter --include-image # human liver, Visium H&E at 0.88 µm per pixel |
| ``` |
|
|
| ```python |
| import sda_db |
| adata = sda_db.load("GSM6963121", release="starter", include_image=True) |
| ``` |
|
|
| From R, or any tool, download a starter sample's files directly, for example |
| `https://huggingface.co/datasets/Biogod/SpatialDataAgent-DB-Catalog/resolve/main/samples/GSE223559/GSM6963121/spatial.h5ad` |
| (also `sample.json` and `image.tif`), and read the H5AD with `zellkonverter::readH5AD`. |
|
|
| **All 12,157 samples.** Every file of the release has passed content verification. To download any sample: |
|
|
| 1. Request access on the [full dataset page](https://huggingface.co/datasets/Biogod/SpatialDataAgent-DB) with |
| your Hugging Face account and an institutional e-mail; requests are reviewed manually. |
| 2. After approval, run `hf auth login` (or set `HF_TOKEN`). The same commands then work without |
| `--release starter`. |
|
|
| ## Is my tissue here? |
|
|
|  |
|
|
| *Samples whose GEO tissue text matches each organ, by platform. The separate right column counts the human |
| samples within each row, across all platforms. Counts are approximate because tissue labels are free text; use the search for exact cohorts.* |
|
|
| <details><summary>The same numbers as a table</summary> |
|
|
| | Organ | Samples | Human | Studies | With H&E | |
| |---|---:|---:|---:|---:| |
| | Brain | 2,562 | 999 | 241 | 1,260 | |
| | Intestine / colon | 1,416 | 735 | 81 | 241 | |
| | Lung | 901 | 649 | 124 | 346 | |
| | Liver | 760 | 184 | 101 | 271 | |
| | Spinal cord | 637 | 182 | 22 | 251 | |
| | Kidney | 563 | 289 | 71 | 203 | |
| | Skin | 375 | 238 | 72 | 179 | |
| | Heart | 309 | 89 | 54 | 142 | |
| | Pancreas | 304 | 200 | 38 | 181 | |
| | Lymph node / spleen | 254 | 127 | 58 | 99 | |
| | Breast | 215 | 153 | 28 | 150 | |
| | Prostate | 107 | 72 | 15 | 89 | |
|
|
| </details> |
|
|
| **No code needed.** Filter the `tissue`, `platform` or `organism` column in the Dataset Viewer on this page, or |
| open `catalog/samples.csv` in Excel or R: |
|
|
| ```r |
| samples <- read.csv("https://huggingface.co/datasets/Biogod/SpatialDataAgent-DB-Catalog/resolve/main/catalog/samples.csv") |
| kidney <- subset(samples, grepl("kidney|renal", tissue, ignore.case = TRUE) & organism == "Homo sapiens") |
| nrow(kidney) # 289 |
| ``` |
|
|
| The `sda-db` commands work from any terminal, also alongside an R workflow. For example, which kidney diseases |
| are covered (no login): |
|
|
| ```bash |
| sda-db values disease --tissue kidney --tissue renal --organism human |
| ``` |
|
|
| This lists 30 reported values in about 290 human kidney samples. Among them are ANCA-associated |
| glomerulonephritis (20 samples), diabetic kidney disease (11), renal cell cancer (8) and acute kidney injury (6); |
| 178 samples report no disease. |
|
|
| ## What one sample contains |
|
|
| Every sample folder in the full dataset has three files: |
|
|
| - `spatial.h5ad`: expression for every spot, cell, bin or bead, with its coordinates; |
| - `image.tif`: a registered pyramidal tissue image, when one was released; |
| - `sample.json`: metadata, including the transform that places each observation on the image. |
|
|
|  |
|
|
| *a, the H&E image of GSM8714178 (human pancreatic cancer, Visium, 0.83 µm per pixel). b, KRT19 counts per spot, |
| placed on the image by the transform in `sample.json`. c, a patch around the circled spot; patches like this, |
| paired with the spot's expression, are the input of image-to-expression models.* |
|
|
|  |
|
|
| *a, the DAPI image of GSM9448988 (human glioblastoma, Xenium, 0.50 µm per pixel). b, GFAP counts per cell from |
| the 541-gene panel, on the same image.* |
|
|
| ## Search in one minute |
|
|
| With the [spatial-data-agent-db](https://github.com/Biogod2020/spatial-data-agent-db) Python package and CLI |
| (no login): |
|
|
| ```bash |
| pip install "spatial-data-agent-db @ git+https://github.com/Biogod2020/spatial-data-agent-db" |
| sda-db search alzheimer hippocampus --organism human --has-image # ranked over study text and sample fields |
| sda-db search --platform Visium --stain "H&E" --split train --output cohort.json |
| sda-db search --gene GFAP --gene AQP4 --platform Xenium # samples whose panel measures both genes |
| ``` |
|
|
| Or read the table directly: |
|
|
| ```python |
| import pandas as pd |
| from huggingface_hub import hf_hub_download |
| |
| path = hf_hub_download("Biogod/SpatialDataAgent-DB-Catalog", "catalog/samples.parquet", repo_type="dataset") |
| samples = pd.read_parquet(path) |
| he = samples[(samples.platform == "Visium") & (samples.image_stain == "H&E")] |
| print(len(he), he[["gsm", "tissue", "n_obs", "image_um_per_px", "split"]].head()) |
| # sample_dir is the folder in the full dataset: <sample_dir>/spatial.h5ad, sample.json, image.tif |
| ``` |
|
|
| After access is approved, the same package plans download sizes (`sda-db download --from cohort.json --dry-run`), |
| checks every file against its SHA256, and reads samples as AnnData. Streaming-ready training shards remain a |
| planned follow-up. AI agents can use the same tools through its |
| MCP server (`sda-db mcp`). |
|
|
| Load a starter sample (no login) and cut an image patch paired with one spot: |
|
|
| ```python |
| import json |
| import sda_db |
| from sda_db.io import image_coordinates |
| from sda_db.image import read_patch |
| |
| adata = sda_db.load("GSM4284327", release="starter", include_image=True) # verified download, then AnnData |
| meta = json.loads(adata.uns["sda_db"]["sample_json"]) |
| pixels = image_coordinates(adata, meta) # image pixel position of every spot |
| patch, _ = read_patch(adata.uns["sda_db"]["directory"], pixels[0], 224) # H&E patch paired with adata.X[0] |
| ``` |
|
|
| The H5AD files also open in R, for example with Bioconductor's `zellkonverter::readH5AD`. |
|
|
| ## Files |
|
|
| | File | Content | |
| |---|---| |
| | `catalog/samples.parquet`, `catalog/samples.csv` | One row per sample: platform, organism, tissue, counts, units, image, QC, split, `sample_dir` path in the full dataset, file SHA256 | |
| | `catalog/atlas-study-metadata.jsonl` | Study title, summary, design and PubMed IDs | |
| | `catalog/atlas-sample-metadata.jsonl` | Reported disease, condition, age, sex, donor and publication; counts evidence and audit | |
| | `catalog/feature-index.sqlite.gz` | Genes and features measured in each sample (41.9 MB; provenance in `catalog/feature-index-provenance.json`) | |
| | `catalog/schema.json`, `catalog/summary.json`, `catalog/studies.csv` | Column dictionary, totals, per-study counts | |
|
|
| Source index SHA256: `909c4ec50e71c636eadc7bf01a9479fef1b4fd480ac348dfea6b785e566c7bed` (snapshot |
| `sda-unified-20261010-v3`, release tag `v1.0.0-20261025` of the full dataset). |
|
|
| ## Collection composition |
|
|
|  |
|
|
| *Sample counts by platform, image availability, source collection and retained split (n = 12,163, the paper release; source snapshot: sda-unified-20261010-v3).* |
|
|
| ## Images and resolution |
|
|
|  |
|
|
| *a, the 8,211 samples with an image, by stain. b, the micrometres per pixel of the 5,113 labelled H&E images |
| whose scale is recorded.* |
|
|
| Most H&E images are the processed images deposited in GEO, at about 4 µm per pixel, enough to see tissue |
| architecture. 561 H&E images have 1 µm per pixel or finer, enough to see cells (as in the patch above). |
| `image_um_per_px` in `samples.parquet` lets you choose by resolution. |
|
|
| ## Sample size |
|
|
|  |
|
|
| *Observations per sample (log scale). A sample is one GEO sample (GSM), usually one tissue section. Spots, bins, |
| beads and cells differ in size, so compare platforms by their own unit.* |
|
|
| ## Splits for machine learning |
|
|
| The `split` column assigns whole GEO series to train (9,249 samples), val (1,316) or test (1,598), so samples of |
| one study never cross splits. Of the H&E samples, 3,847 are in train, 358 in val and 1,058 in test. The split does not prove donor independence across studies; check |
| `content_group` and `aliases` (in `samples.parquet`) and `donor_id` (in `atlas-sample-metadata.jsonl`) for |
| your benchmark. |
|
|
| ## Read the annotations correctly |
|
|
| - **Vocabularies.** Organism, tissue, disease and age are kept as reported in GEO, not harmonized. |
| - **Missing values.** Missing values stay empty; an empty `review_decision` is not a PASS. |
| - **Separate checks.** Each check has its own column. Counts capability is `counts_ready` in |
| `atlas-sample-metadata.jsonl`. The others are columns of `samples.parquet`: |
| - source verification: `verification`; |
| - image registration: `image_registration`; |
| - human review: `review_decision`. |
| - **Image scale.** `image_um_per_px` gives micrometres per image pixel where known; 5,113 of the 5,263 H&E |
| images have it. `coordinate_units` and `um_per_unit` describe the spatial coordinates. |
| - **Observations.** Observations are spots, cells, bins or beads, not independent donors. |
|
|
| ## Terms |
|
|
| The catalog and the data follow the terms of the original studies. There is no blanket license, and the |
| catalog has no per-sample license column. See the |
| [full Dataset Card](https://huggingface.co/datasets/Biogod/SpatialDataAgent-DB#data-use-and-source-terms) for |
| reuse terms and citation, and cite the original studies with the data you use (`sda-db cite` writes BibTeX for |
| a selection). |
|
|
| ## Corrections 2026-11-11 |
|
|
| On 2026-11-11 the samples that come from HESRT, the earlier H&E collection that is part of this dataset (`source_pipeline = hesrt`), were compared value by value with the files deposited in GEO. The check found problems in 18 samples. We replaced 12 of them and withdrew 6. No other expression or coordinate file changed. On the same day an image-registration screen led to the removal of the image of nine more samples (see the end of this section). The numbers at the top of this card describe the current data. The figures and composition tables further down still show the paper release (`v1.0.0-20261025`, 12,163 samples); they count the six withdrawn samples and describe the 12 replaced samples by their old files. |
|
|
| **Replaced (12 samples).** Each sample was converted again from the deposited files. The new files were compared with the deposit value by value, and the author reviewed each sample by eye. |
|
|
| | Study | Samples | What was wrong | What changed | |
| | --- | --- | --- | --- | |
| | GSE205707 | `GSM6896311` to `GSM6896316` | The counts and gene names were generated by the conversion script. They were not read from the deposit. | New expression matrix from the deposited counts (32,285 genes for the two mouse samples and 32,704 for the four dog samples; more spots than before). Image unchanged. | |
| | GSE275125 | `GSM8465980` to `GSM8465983` | The counts and gene names were generated by the conversion script. They were not read from the deposit. | New expression matrix from the deposited counts. Image unchanged. | |
| | GSE294965 | `GSM8933093` | The cell positions were drawn at random. The image was a low-resolution copy that was labelled H&E. | Cell positions from the deposit, and the full-resolution DAPI image (0.5 µm per pixel). | |
| | GSE186290 | `GSM5643203` | The expression rows were in a different order from the spots, so each spot carried the counts of another spot. The spot positions were right. | Each spot now has its own expression row. Image unchanged. | |
|
|
| For these 12 samples the catalog (`catalog/samples.parquet`) now gives coordinates in micrometres (`coordinate_units = um`) and names the new conversion in `source_pipeline` and `source_release`. Two new values appear in `image_registration`: `DEPOSITED_ALIGNMENT_FIT` (the spot-to-image transform was fitted from the deposited alignment file) and `DEPOSITED_PIXEL_FRAME` (the deposited positions are already in image pixels). |
|
|
| **Withdrawn (6 samples).** Their folders are removed from `main`. A note for each is in `review-revocations/` of the main dataset. |
|
|
| | Study | Samples | Reason | |
| | --- | --- | --- | |
| | GSE298115 | `GSM9008651`, `GSM9008653` | The expression rows were in the wrong order. A corrected conversion has the right values, but the spots do not sit on the tissue in the image, because the deposit has no scale-factor file. It did not pass review. The other four samples of this study stay. | |
| | GSE244723 | `GSM7825036` to `GSM7825039` | The deposited count tables have no gene names. The values were equal to the deposit, but the samples cannot be used for any analysis by gene. The whole study is withdrawn. | |
|
|
| **Counts.** The paper release had 12,163 samples in 1,265 study folders. The current data has 12,157 samples in 1,264 study folders. The no-login starter pack has 86 samples (was 88): `GSM7825036` and `GSM7825037` are removed, and `GSM8933093` is replaced. |
|
|
| **If you used these samples.** Do not use results computed from the old files of the 12 replaced samples or of the two GSE298115 samples. For the 12 replaced samples, redo them with the new files. For the two GSE298115 samples, drop them. The four GSE244723 samples had correct values but placeholder gene names, so any result by gene from them has no meaning. |
|
|
| **Samples whose spots do not sit on the tissue (9 samples, image removed).** On the same day the image registration of the same HESRT samples was screened, and the author looked at every flagged sample. In nine samples the spots do not match the image: `GSM7156292` to `GSM7156295` (GSE229175), `GSM9097318` and `GSM9097320` (GSE302160), `GSM8925216` (GSE294844), `GSM8770960` (GSE288607) and `GSM9317397` (GSE311123). Their image was removed from the data on 2026-11-11. Their expression values and coordinates are equal to the deposit and stay in the data. This differs from the HESRT dataset, where release `2026-09-17-v1.6` withdraws these nine samples completely. In the catalog these nine samples now have `has_image = False`, `image_registration = WITHHELD` and the flag `image_review_fail`. If you paired image patches with expression for these samples, drop those results. In 16 further HESRT samples the spots match the tissue, but the image covers only part of the spots (5% to 50% of the spots fall outside it). They carry the flag `image_partial_coverage`; filter spots by the image bounds before you extract patches. The samples that do not come from HESRT have not been screened in this way yet. |
|
|
| **Versions.** The current data is tag `v1.3.0-20261111` of the main dataset. Tag `v1.2.0-20261111` holds the data after the 12 replacements and 6 withdrawals, but it still has the nine removed images. The paper tag `v1.0.0-20261025` is unchanged and still holds the old files, so earlier work can be traced. The gene search index was updated for the changed samples. The sample metadata is version 2.2.4: it drops the six withdrawn samples and marks the nine samples that lost their image; the curated annotations of all other samples did not change. The `sda-db` toolkit reads the paper tag by default, so it still returns the old files of these 27 samples (12 replaced, 6 withdrawn, 9 with the image removed) until its next release; to get the current files now, download them with `huggingface_hub` and `revision="v1.3.0-20261111"`. |
|
|
| ## Curated sample metadata v2 (version 2.2.4, 2026-11-11) |
|
|
| `metadata/v2/` adds a curated table on top of the current 12,157 samples. The paper release is still tag |
| `v1.0.0-20261025`; the corrected data and the current metadata are tag `v1.3.0-20261111` of the main dataset. Version 2.2.4 |
| marks the nine samples whose image was removed on 2026-11-11 (`has_image`, `image_stain`, `image_registration`, `flags` and the review columns). Version 2.2.3 |
| drops the six samples withdrawn on 2026-11-11 and updates the file-derived columns (for example `n_obs`, |
| `coordinate_units`, `image_registration`) of the 12 replaced samples; no curated annotation changed. Version 2.2.2 |
| (tag `metadata-v2.2.2-20261108`) only moved the most-used columns to the front. |
|
|
| In the Dataset Viewer, `metadata_v2` (the default) shows this table and `samples` shows the catalog |
| (`catalog/samples.parquet`). |
|
|
| - `samples_metadata_v2.parquet` / `.csv` has one row per sample: |
| - organ (43 sites); |
| - UBERON tissue term (11,605 samples); |
| - MONDO disease terms (6,479); |
| - sample kind, disease status (normal / disease / perturbation / not stated), life stage and sex; |
| - NCBI taxonomy, and the species implied by the gene IDs; |
| - corrected platform family and assay name. |
| The raw GEO text is kept next to every field. |
| - `schema_v2.json` defines each column. `changes_v2.csv` lists every value that differs from the release, with its |
| basis. `provenance.json` gives the model, the ontology versions and the file hashes. |
| - **How it was made.** A language model could only pick from fixed answers or from ontology terms found in each |
| sample's GEO record, so it could not invent identifiers. Fixed rules handle unambiguous ages. |
| - **Quality, estimated on 300 random samples.** An independent model judged each label and every "wrong" verdict |
| was read by hand. Estimated wrong rates: organ 0.3%, tissue term 0.7%, sample kind 0.7%, life stage 1.7%, |
| disease 3.0%. Labels judged correct were not re-read, so these are estimates. A human reader then reviewed 200 samples, and the errors found were corrected in version 2.2. |
| - **Known limit.** A study's disease sometimes lands on a control whose own record does not say it is a control |
| (about 1.5% of samples). Use `annotation_low_confidence`, `metadata_flags` and the raw fields when this matters. |
| - Full description: `metadata/v2/README.md`. |
|
|
