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KPMP Xenium virtual spatial transcriptomics

Virtual spatial transcriptomics for the 49 H&E sections of the 10x Xenium kidney biopsies of the Kidney Precision Medicine Project (KPMP), one section per participant: 19,338 genes in each of 1,637,759 cells, predicted with DeepSpot-M Kidney adapted to these sections on their measured Xenium panel genes. Each file also holds the measured Xenium panel of the section.

Model: DeepSpot-M Kidney, finetuned from ratschlab/DeepSpotM · Code: github.com/ratschlab/DeepSpotM · Paper: citation will be added on publication; DeepSpot-M: medRxiv 2026.06.19.26356060.

News

[09.2026] Introducing Aurora - a no-code platform for virtual spatial transcriptomics from H&E images. Try the interactive Aurora demo.

Enrollment group	hKidney sections	hMulti sections	Participants	Cells
AKI	6	–	6	238,177
CKD	9	22	31	1,140,540
DM-R	8	–	8	218,824
Healthy Reference	1	3	4	40,218
Total	24	25	49	1,637,759

data/<ENROLLMENT_GROUP>/<sample_id>.h5ad    one AnnData file per section, named by its KPMP specimen id (52.0 GB in total)
metadata.csv                                one row per section

Example

CKD biopsy (hMulti panel), per cell: H&E, measured and predicted PODXL (glomeruli; held out from the adaptation) and virtual ALDOB (proximal tubule; not on the panel).

Download

This is a gated dataset: log in first (pip install huggingface_hub).

import pandas as pd
from huggingface_hub import login, snapshot_download, hf_hub_download
login(token="YOUR_HF_TOKEN")

repo = "ratschlab/KPMP_Xenium_virtual_spatial_transcriptomics"

# one enrollment group (leave out allow_patterns for the whole dataset)
snapshot_download(repo, repo_type="dataset", local_dir="KPMP_Xenium_data", allow_patterns="data/CKD/*")

# or select sections in metadata.csv first
meta = pd.read_csv(hf_hub_download(repo, "metadata.csv", repo_type="dataset"))
sel = meta[meta["panel"] == "hKidney_480g"]
snapshot_download(repo, repo_type="dataset", local_dir="KPMP_Xenium_data", allow_patterns=sel.file_path.tolist())

Usage

import numpy as np
import scanpy as sc
import squidpy as sq

adata = sc.read_h5ad("KPMP_Xenium_data/data/CKD/S-2412-005611.h5ad")
adata.X = adata.X.astype(np.float32)
sq.pl.spatial_scatter(adata, color=[None, "PODXL", "ALDOB"], size=0.3, ncols=3)

Data format

X: virtual expression of 19,338 genes per cell, log1p counts per 10,000 (log1p CP10k) on the scale of the Xenium panel the decoder was adapted to, stored as float16 (cast to float32 before analysis).
layers['ground_truth']: measured Xenium expression (log1p counts per 10,000 over the panel genes), sparse, zero outside the panel genes.
obs: one row per cell: cell_id (Xenium cell id), sample_id (KPMP specimen id), participant_id, enrollment, panel, imaging_site, centroid in full-resolution H&E pixels (x_pixel, y_pixel) and in Xenium micrometres (x_um, y_um).
var: HGNC gene symbol, ensembl_ids, hgnc_id, in_panel_hKidney / in_panel_hMulti (measured on that Xenium panel; SLC9A3R1 and TMEM173 under their current symbols NHERF1 and STING1) and heldout (74 panel genes left out of the adaptation in every section).
obsm['spatial'] and uns['spatial']: cell centroids and an H&E overview image, as scanpy and squidpy expect.
uns['he2st']: section and processing metadata, with the adaptation and the H&E-to-Xenium registration (he_px_to_xenium_um, 3 × 3, and its QC distance).
metadata.csv: one row per section: file path, participant, specimen, enrollment group, panel, imaging site, number of cells (n_cells), in_kpmp_visium_training (participant in the KPMP Visium training data of DeepSpot-M Kidney), in_kpmp_he_atlas (participant in the KPMP virtual spatial transcriptomics atlas) and the KPMP download links of the Xenium bundle, the H&E image and the metadata file.

Citation

Please cite DeepSpot-M (the citation of the kidney paper will be added on publication):

@article{nonchev2026deepspotm,
  title   = {DeepSpot-M: a multimodal foundation model for transcriptome-wide virtual spatial transcriptomics from histology},
  author  = {Nonchev, Kalin and Dawo, Sebastian and Silina, Karina and Koelzer, Viktor H. and R{\"a}tsch, Gunnar},
  journal = {medRxiv},
  year    = {2026},
  doi     = {10.64898/2026.06.19.26356060},
  url     = {https://www.medrxiv.org/content/10.64898/2026.06.19.26356060v1}
}

KPMP acknowledgement

The H&E images and the Xenium data are KPMP open-access data. Work that uses this dataset must acknowledge KPMP (Citing KPMP data):

The results here are in whole or part based upon data generated by the Kidney Precision Medicine Project. Accessed Month Day, Year. https://www.kpmp.org.

The Kidney Precision Medicine Project (KPMP) is supported by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) through the following grants: U01DK133081, U01DK133091, U01DK133092, U01DK133093, U01DK133095, U01DK133097, U01DK114866, U01DK114908, U01DK133090, U01DK133113, U01DK133766, U01DK133768, U01DK114907, U01DK114920, U01DK114923, U01DK114933, U24DK114886, UH3DK114926, UH3DK114861, UH3DK114915, UH3DK114937, 1U01DK144965-01, 1U01DK144994-01. We gratefully acknowledge the essential contributions of our patient participants and the support of the American public through their tax dollars.

License

CC BY-NC-SA 4.0, non-commercial academic research only. The KPMP data included remain subject to KPMP's terms of use.

NB

Computational data analysis was performed at Leonhard Med, the secure trusted research environment at ETH Zurich. Our pipeline aligns with the specific cluster requirements and resources.

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