Upload README.md with huggingface_hub

README.md

@@ -1,3 +1,126 @@
 ---
 license: cc-by-4.0
+tags:
+  - single-cell
+  - flow-cytometry
+  - spectral-flow-cytometry
+  - haematopoiesis
+  - experimental-design
+size_categories:
+  - 10M<n<100M
 ---
+
+# LabCompass — Spectral Flow Cytometry haematopoiesis dataset
+
+Measurements underlying **LabCompass**, a method for generative modeling of experimental design in
+single-cell data. This dataset contains Spectral Flow Cytometry (SFC) profiles of *in vitro*
+haematopoietic differentiation cultures, collected over successive rounds of a closed-loop
+experimental design cycle.
+
+Each round — a **loop** — proposes new culture protocols, runs them at the bench, and measures the
+resulting cells. The measurements from each loop are published here as a separate file.
+
+- **Code and full reproduction pipeline:** <https://github.com/theislab/LabCompass>
+- **Wet-lab experiments and measurements:** Göttgens Lab
+- **License:** CC-BY-4.0
+
+## ⚠️ These files are per-loop, not cumulative
+
+`loops/loop3.h5ad` contains **only the cells measured in loop 3** — not loops 0–3 together. Models in
+the paper are trained on the *accumulated* data, so a loop's training set is the concatenation of
+every loop up to and including it:
+
+```
+dataset(N) = concat(dataset(N-1), loopN)
+```
+
+Concatenating them yourself is a few lines of `anndata`, but the exact chain matters (one loop
+introduces new protocol axes that must be zero-filled on the earlier data — see below). The
+reproduction repository ships a script that does it correctly:
+
+```bash
+git clone https://github.com/theislab/LabCompass.git
+python scripts/data/build_loop_datasets.py              # downloads from this repo and builds the chain
+python scripts/data/build_loop_datasets.py --variants 500k   # subsampled only: far smaller and faster
+```
+
+## Files
+
+Every loop is published in two variants: the full measurement set, and a subsampled version
+(`_500k` suffix) intended for fast iteration. The suffix is a naming convention carried over from
+the source data, not a guaranteed cell count — the subsampled files vary in size.
+
+| Loop | Full | Subsampled | Approx. size (full) |
+| --- | --- | --- | --- |
+| 0 (baseline) | `loops/loop0.h5ad` | `loops/loop0_500k.h5ad` | 36 GB |
+| 1 | `loops/loop1.h5ad` | `loops/loop1_500k.h5ad` | 2.5 GB |
+| 2 | `loops/loop2.h5ad` | `loops/loop2_500k.h5ad` | 3.9 GB |
+| 2.5 | `loops/loop2p5.h5ad` | `loops/loop2p5_500k.h5ad` | 2.7 GB |
+| 3 | `loops/loop3.h5ad` | `loops/loop3_500k.h5ad` | 6.3 GB |
+| 4 | `loops/loop4.h5ad` | `loops/loop4_500k.h5ad` | 0.9 GB |
+| 4.5 | `loops/loop4p5.h5ad` | `loops/loop4p5_500k.h5ad` | 0.5 GB |
+| 5 | `loops/loop5.h5ad` | `loops/loop5_500k.h5ad` | 6.3 GB |
+
+Loop 0 is the baseline screen and is by far the largest. The half-steps (2.5, 4.5) are follow-up
+rounds within a design cycle and accumulate like any other loop, giving the chain
+
+```
+loop0 → loop1 → loop2 → loop2p5 → loop3 → loop4 → loop4p5 → loop5
+```
+
+The full set is roughly 60 GB; the subsampled set is a few GB.
+
+## Format
+
+Each file is an [AnnData](https://anndata.readthedocs.io/) `.h5ad` object:
+
+- **`X`** — logicle-transformed SFC intensities: fluorescence channels and morphological scatter
+  features, one row per cell.
+- **`obs`** — per-cell metadata, in three groups:
+  - *Acquisition:* `experiment_number`, `experiment_id`, `replicate`, `date`, `well_id`,
+    `cytometer`, `cytometer_serial_no`, `count_beads`, `cell_counts`, `source_id`.
+  - *Protocol axes* — the culture recipe, and the space LabCompass searches over. Cytokines and small
+    molecules carry their units in the column name, e.g. `scf_[ng_ml]`, `tpo_[ng_ml]`,
+    `il3_[ng_ml]`, `gm-csf_[ng_ml]`, `rhflt3l_[ng_ml]`, `ldl_[ng_ml]`, `sr1_[nm]`, `um171_[nm]`,
+    `um729_[µm]`, `butyzamide_[nm]`, `retinoic_acid_[µm]`, `mtg_[µm]`, `740-yp_[µm]`, alongside
+    culture conditions such as `o2_[%]` and `hydrogel_type`.
+  - *Annotation:* cell-type labels, where available.
+
+`experiment_number` identifies the physical experiment a cell came from (loop 1, for instance, spans
+experiments 206–210), which makes it a convenient way to check which loops are present in a
+concatenated object.
+
+### The protocol schema grows across loops
+
+Later loops vary axes that earlier loops never did. Loop 3 introduces `il7_[ng_ml]`,
+`mcsf_[ng_ml]` and `ly_cocktail_[ul/well]`, which are absent from loops 0–2.5. When concatenating,
+these must be **zero-filled on the earlier data** (they were held at zero, not missing) so both sides
+share an `obs` schema. `build_loop_datasets.py` does this; a naive `anndata.concat` will silently
+drop the columns instead.
+
+## Loading
+
+```python
+import anndata as ad
+from huggingface_hub import hf_hub_download
+
+path = hf_hub_download(
+    repo_id="theislab/LabCompass",
+    filename="loops/loop3_500k.h5ad",
+    repo_type="dataset",
+)
+adata = ad.read_h5ad(path)
+```
+
+## Citation
+
+<!-- TODO: replace with the published reference before release. -->
+
+```bibtex
+@article{labcompass,
+  title   = {TODO},
+  author  = {Consoli, Lorenzo and Palma, Alessandro and others},
+  journal = {TODO},
+  year    = {TODO},
+}
+```