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Eva-data
Multiplexed imaging patches for the Eva study. One .npz per region, named by its
acquisition id, under a per-cohort directory. K varies per region and the channel
order follows that file's own biomarkers, so index by marker name, never by position.
| key | ||
|---|---|---|
mif |
(n, 224, 224, K) float16 |
multiplexed patches |
he |
(n, 224, 224, 3) uint8 |
paired H&E, same patches; absent where not acquired |
coords |
(n, 2) int64 |
top-left pixel of each patch within the region |
biomarkers |
(K,) str |
that region's panel, in channel order |
Patches tile the region on a regular 224-pixel grid. Where a cohort has paired H&E, only the H&E-paired regions are included.
| cohort | regions | patches | K | H&E | size |
|---|---|---|---|---|---|
UPMC-HNC |
327 | 33,123 | 41 | yes | 42.6 GB |
Stanford-CRC |
406 | 13,461 | 40 | no | 14.6 GB |
Stanford-HCC |
35 | 9,017 | 41 | yes | 9.3 GB |
DFCI-HNC |
58 | 2,266 | 40 | no | 2.9 GB |
MDACC-HCC |
30 | 16,116 | 48 | yes | 17.3 GB |
import numpy as np
d = np.load("UPMC-HNC/UPMC_c001_v001_r001_reg001.npz")
mif, coords, markers = d["mif"], d["coords"], d["biomarkers"]
h, w = coords.max(0) + 224
region = np.zeros((h, w, mif.shape[-1]), dtype=np.float16)
for patch, (y, x) in zip(mif, coords):
region[y:y + 224, x:x + 224] = patch
cd8 = region[..., list(markers).index("CD8")]
from huggingface_hub import snapshot_download
snapshot_download("yandrewl/Eva-data", repo_type="dataset",
allow_patterns="UPMC-HNC/*", local_dir="data")
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