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PyTorch
biology
spatial-transcriptomics
image-to-expression
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Organize fusion weights by organ and panel width

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Files changed (24) hide show
  1. README.md +29 -1
  2. beta.json +0 -0
  3. beta.meta.json +2 -2
  4. beta/bone/narrow.json +471 -0
  5. beta/brain/narrow.json +467 -0
  6. beta/breast/narrow.json +771 -0
  7. beta/cervix/wide.json +0 -0
  8. beta/colon/narrow.json +824 -0
  9. beta/colon/wide.json +0 -0
  10. beta/heart/narrow.json +371 -0
  11. beta/kidney/narrow.json +397 -0
  12. beta/liver/narrow.json +467 -0
  13. beta/liver/wide.json +0 -0
  14. beta/lung/narrow.json +975 -0
  15. beta/lung/wide.json +0 -0
  16. beta/lymph_node/wide.json +0 -0
  17. beta/manifest.json +186 -0
  18. beta/ovary/narrow.json +723 -0
  19. beta/ovary/wide.json +0 -0
  20. beta/pancreas/narrow.json +467 -0
  21. beta/prostate/wide.json +0 -0
  22. beta/skin/narrow.json +572 -0
  23. beta/skin/wide.json +0 -0
  24. beta/tonsil/narrow.json +371 -0
README.md CHANGED
@@ -73,7 +73,7 @@ python predict/predict.py --image /path/to/image.png --mpp 0.25 \
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  | --- | --- |
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  | `--cells cells.npz` | Use existing cell boundaries instead of automatic segmentation |
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  | `--prepared /path/to/slide` | Read prepared cell crops and boundaries instead of `--image` |
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- | `--tissue xenium_lung` | Select tissue-specific fusion weights; omitted or unknown tissue uses global weights |
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  | `--genes all` | Predict all 18,580 model genes; this is the default |
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  | `--genes genes.txt` | Predict an ordered list of gene symbols, one per line |
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  | `--save-embeddings` | Save image and spatial features |
@@ -83,6 +83,34 @@ python predict/predict.py --image /path/to/image.png --mpp 0.25 \
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  CUDA is the default device. CPU inference is available with `--device cpu --amp fp32`. Prediction matrices are written to disk in batches; output size scales with the number of cells and requested genes. Automatic segmentation produces nuclei; use supplied cell boundaries when available.
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  ## Output
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  ```python
 
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  | --- | --- |
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  | `--cells cells.npz` | Use existing cell boundaries instead of automatic segmentation |
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  | `--prepared /path/to/slide` | Read prepared cell crops and boundaries instead of `--image` |
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+ | `--tissue lung/wide` | Select organ and panel-width fusion weights; `--tissue lung` selects width automatically |
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  | `--genes all` | Predict all 18,580 model genes; this is the default |
78
  | `--genes genes.txt` | Predict an ordered list of gene symbols, one per line |
79
  | `--save-embeddings` | Save image and spatial features |
 
83
 
84
  CUDA is the default device. CPU inference is available with `--device cpu --amp fp32`. Prediction matrices are written to disk in batches; output size scales with the number of cells and requested genes. Automatic segmentation produces nuclei; use supplied cell boundaries when available.
85
 
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+ ## Fusion weights
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+
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+ All 15 reference organs and all 20 available organ/panel groups are included. The table links to individual gene-weight tables and shows the number of genes with fitted weights.
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+
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+ | Organ | narrow | wide |
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+ | --- | ---: | ---: |
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+ | bone | [469](beta/bone/narrow.json) | — |
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+ | brain | [465](beta/brain/narrow.json) | — |
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+ | breast | [769](beta/breast/narrow.json) | — |
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+ | cervix | — | [5,030](beta/cervix/wide.json) |
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+ | colon | [822](beta/colon/narrow.json) | [18,539](beta/colon/wide.json) |
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+ | heart | [369](beta/heart/narrow.json) | — |
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+ | kidney | [395](beta/kidney/narrow.json) | — |
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+ | liver | [465](beta/liver/narrow.json) | [6,107](beta/liver/wide.json) |
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+ | lung | [973](beta/lung/narrow.json) | [4,954](beta/lung/wide.json) |
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+ | lymph_node | — | [4,577](beta/lymph_node/wide.json) |
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+ | ovary | [721](beta/ovary/narrow.json) | [8,512](beta/ovary/wide.json) |
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+ | pancreas | [465](beta/pancreas/narrow.json) | — |
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+ | prostate | — | [4,958](beta/prostate/wide.json) |
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+ | skin | [570](beta/skin/narrow.json) | [4,959](beta/skin/wide.json) |
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+ | tonsil | [369](beta/tonsil/narrow.json) | — |
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+
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+ `narrow` means fewer than 1,000 measured model genes in the fitting reference; `wide` means at least 1,000. Each gene's weight is the equal mean of its fitted weights across contributing reference slides in that organ/panel group. Single-reference groups are included: fitting retrieves from other reference slides across tissues, excluding the query reference and its related donor/block samples. [beta/manifest.json](beta/manifest.json) lists reference counts, fitted-gene counts and checksums.
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+
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+ Use `--tissue lung/wide` to select an explicit group. With an organ alone, such as `--tissue lung`, width is selected from the number of requested output genes. Supply an explicit group to keep that choice fixed when changing the requested gene subset. A missing width uses another available width of the same organ; an omitted or unknown organ uses global weights.
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+
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+ The root `beta.json` combines all groups and is loaded automatically during prediction. Individual tables under `beta/ORGAN/WIDTH.json` contain the same weights. Global weights are fitted across the full reference set independently of organ grouping. Genes without reference coverage or a fitted weight use the direct prediction. No target expression is needed.
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+
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  ## Output
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116
  ```python
beta.json CHANGED
The diff for this file is too large to render. See raw diff
 
beta.meta.json CHANGED
@@ -1,5 +1,5 @@
1
  {
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- "gate_sha256": "b9e95092ff8b19c038c54ce4fb19f68eea600cc654e115d17c8ead3bb725032a",
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  "checkpoint_sha256": "4228704493c9da794ed7be5c6f98194f36eeb7c05d9ed618f69109091cf2a537",
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  "stage2_checkpoint_sha256": "130d8c61ca9607827c220e1e51b6d5a6a49deac0f66c0178302303545ffd944c",
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  "gene_space_sha256": "87ac6eb3e95da658e2993ea54b8043f4bb60604fa48100aeea8697c4b84bd938",
@@ -366,7 +366,7 @@
366
  },
367
  "R_dtype": "float32",
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  "tissue_casefold": true,
369
- "min_refs": 2,
370
  "panel_cut": 1000,
371
  "fallback": "same_tissue"
372
  }
 
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  {
2
+ "gate_sha256": "dd020eaaed4954dc074bcc68e4b215b505d2e260be34615c7675b22bc8ab1d4f",
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  "checkpoint_sha256": "4228704493c9da794ed7be5c6f98194f36eeb7c05d9ed618f69109091cf2a537",
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  "stage2_checkpoint_sha256": "130d8c61ca9607827c220e1e51b6d5a6a49deac0f66c0178302303545ffd944c",
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  "gene_space_sha256": "87ac6eb3e95da658e2993ea54b8043f4bb60604fa48100aeea8697c4b84bd938",
 
366
  },
367
  "R_dtype": "float32",
368
  "tissue_casefold": true,
369
+ "min_refs": 1,
370
  "panel_cut": 1000,
371
  "fallback": "same_tissue"
372
  }
beta/bone/narrow.json ADDED
@@ -0,0 +1,471 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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