Datasets:

theislab
/

LabCompass

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:

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 .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

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

@article{labcompass,
  title   = {TODO},
  author  = {Consoli, Lorenzo and Palma, Alessandro and others},
  journal = {TODO},
  year    = {TODO},
}