CompEpigen/syto.1.0

SYTO v1.0 publication data

Data underlying "Data-driven soft labeling scales DNA read classification to whole-body cell-type deconvolution". SYTO is a framework for read-level DNA methylation classification and whole-body cell-type deconvolution. This deposit contains the trained models, generated pseudobulk mixtures, training data, source reads and final result tables behind every figure and table in the paper.

This repository is a mirror. The canonical, citable version of record is TBD. This mirror exists to make the data easier to fetch programmatically; contents are identical to the planed citable deposit.

• Size: 190.4 GB across 89 files (84 .zip archives + 4 acompanying metadata files + 1 csv file with Syto variants and baselines ranking)
• Unpacked: 82,010 files
• Largest file: 5.66 GB

Contents

The deposit is organised in five tiers of decreasing necessity, so you can take only the depth you need.

Tier	Contents	Size
tier0-results/	Final result tables underlying important published figures and tables	0.5 GB
tier1-models/	Trained classifiers, deconvolvers and calibrators	24.0 GB
tier2-pseudobulks/	Generated pseudobulk mixtures (columnar Parquet)	141.5 GB
tier3-training-data/	Marker atlases, target proportions, training datasets	12.6 GB
tier4-source-data/	Staged and recovered reads, hg19/hg38 reference genomes	11.8 GB

tier0-results/ alone (0.5 GB) is enough to inspect the final results of the paper. Tiers 1–4 exist so the analysis can be independently re-run and verified.

Metadata files are readable without downloading anything large:

File	Purpose
MANIFEST.csv	One row per archive: description, size, file count, SHA-256
MANIFEST_CONTENTS.csv	One row per file inside the archives — inspect an archive's contents without downloading it
runs.csv	One row per experiment run, linking tier1 ↔ tier2 ↔ tier3 paths
FILE_NAMING_CONVENTION.txt	Full naming scheme and every abbreviation used

Downloading

Archives are stored as-is; there is no dataset viewer. Fetch selectively.

Inspect what exists first — both manifests are small:

import pandas as pd
pd.read_csv("hf://datasets/CompEpigen/syto.1.0/MANIFEST.csv")
pd.read_csv("hf://datasets/CompEpigen/syto.1.0/MANIFEST_CONTENTS.csv")

Just the results (0.5 GB):

hf download CompEpigen/syto.1.0 --repo-type=dataset \
  --include "tier0-results/*" "*.csv" "*.txt" --local-dir ./syto-data

One specific run:

hf download CompEpigen/syto.1.0 --repo-type=dataset \
  --include "tier1-models/ood-rrbs/SYTO_tier1_models_oodrrbs_dismir_softlabelpooled_v1.zip" \
  --local-dir ./syto-data

Everything (190 GB):

hf download CompEpigen/syto.1.0 --repo-type=dataset --local-dir ./syto-data

Unpacking

Always unpack from the deposit root. Archive members are stored with paths relative to the root, so unpacking an archive from inside its own folder nests the tree a second time (tier1-models/ood-rrbs/tier1-models/...)

cd ./syto
find . -name 'SYTO_*.zip' -exec unzip -o -q {} ';'

The reference-genome archives contain relative symlinks (genome.fa.gz -> hg38.fa.gz) matching the wgbs_tools layout. Unpack them with a tool that preserves symlinks — unzip on Linux and macOS does.

Verifying

MANIFEST.csv carries a SHA-256 for every archive:

python - <<'PY'
import csv, hashlib, pathlib
for r in csv.DictReader(open("MANIFEST.csv")):
    p = pathlib.Path(r["item"])
    if not p.exists():
        continue
    h = hashlib.sha256()
    with p.open("rb") as f:
        for chunk in iter(lambda: f.read(1 << 20), b""):
            h.update(chunk)
    print("OK " if h.hexdigest() == r["sha256"] else "BAD", p)
PY

Naming convention

See FILE_NAMING_CONVENTION.txt for the complete scheme, including feature sets, atlas names and reference-genome codes.

What is not included

Redundant, regenerable and training-only artefacts were removed before deposit: duplicated inputs, per-calibrator predictions regenerable from the calibrator plus features, optimizer states, intermediates superseded by the deposited aggregate tables, and diagnostic plots not part of the published results.

Two inputs are referenced by the published configs but not redistributed here, marked by placeholders:

• ${LOYFER_RECOVERED_READS} — Loyfer recovered read tables; outputs of CompEpigen/wgbs_atlas_simulation
• ${SYTO_MLFLOW} — an intermediate store for the *_predicted.pkl files (data splits enriched with trained-classifier outputs, used as pseudobulk inputs). The pseudobulk pipeline runs in predictions_only mode to produce them; two-stage configs are included where relevant.

Experiments were originally recorded in a local MLflow registry. MLflow-specific files were stripped for release, but each tier1-models element retains its recorded metrics, parameters and tags.

A note on configs: published YAML configs use paths relative to the deposit root and were automatically redacted from their original HPC paths. Experiments were not re-run after repackaging, so treat the configs as authoritative for parameters while expecting to adjust paths to match your unpacking layout. For MethylBERT you would need to re-point corresponding configs to the pretrained foundational model (which is not included in this dataset).

Citation

@article{rizdvanetskyi2026data,
  title={Data-Driven Soft Labeling Scales DNA Read Classification to Whole-Body Cell-Type Deconvolution},
  author={Rizdvanetskyi, Dmytro and Roos, Nathan and Lutsik, Pavlo},
  journal={arXiv preprint arXiv:2607.04987},
  year={2026}
}

Thee dataset DOI is TBD.

License

Released under CC BY 4.0.