Datasets:

saarantras1
/

scMPRAforge_models

license: other
license_name: cc-by-4.0-and-cc-by-nc-4.0
license_link: LICENSE.md
tags:
  - biology
  - genomics
  - regulatory-genomics
  - mpra
  - single-cell
language:
  - en
pretty_name: scMPRA stratified negative binomial fits

scMPRA stratified negative binomial fits

Fitted parameters for every stratified negative binomial (NB) and zero-inflated negative binomial (ZINB) model reported in Modeling, calibration, and power analysis of single-cell massively parallel reporter assays. There are fifteen fits over three published scMPRA datasets: three canonical (one per dataset, the model the paper's analyses use) and twelve counterfactuals kept so the model-selection comparisons can be reproduced.

The parameter tables are plain parquet and need nothing but pandas. The orthos are the complete fitted objects, loadable with scMPRAforge. Bounds objects derived from these fits are included with scMPRAforge.

Files

file	rows	description
`fitted_means.parquet`	45,296	fitted mean and dispersion, one row per level
`zero_inflation.parquet`	6,016	fitted zero inflation, ZINB fits only
`fits.csv`	15	one row per fit: dataset, model family, zero handling, canonical flag
`export_fits.py`		the script that produced all three
`orthos/<fit>.tar.gz`	15	the complete fitted objects, one archive per fit

Schema

fitted_means.parquet

fit — fit name, joins to fits.csv
stratified_by — cell_type or cre
stratum — the level of the stratifying axis this model was fit on
level_axis / level — what the mean is indexed by, and which one
mu — fitted mean
theta — NB dispersion for this stratum

zero_inflation.parquet is the same minus theta, with zi in place of mu, and level_axis always rep_id.

Datasets

`dataset`	study	assay
`shendure`	Lalanne et al. 2024, Nat Methods 21:983, 10.1038/s41592-024-02260-3	scQers, developmental CREs in mouse embryoid bodies
`cohen`	Zhao et al. 2023, Nat Genet 55:346, 10.1038/s41588-022-01278-7	scMPRA, promoter variants in live mouse retinas
`seelig`	Yin et al. 2025, Cell Systems 16:101302, 10.1016/j.cels.2025.101302	scMPRA of designed enhancers in HepG2/K562

The lab names are kept as identifiers so these join to the analysis code. Make sure you cite the papers above in downstream analysis.

Loading

import pandas as pd

fits  = pd.read_csv("fits.csv")
means = pd.read_parquet("fitted_means.parquet")

canonical = set(fits.loc[fits.canonical, "fit"])
mu = means[means.fit.isin(canonical) & (means.stratified_by == "cell_type")]

Orthos

Each ortho holds its training data in training_data.scmpra/: the count table post filtering and negative-control pooling. ortho.load reads it from there, so an ortho loads and computes from any working directory.

The embedded UMI-wise tables use schema version 2, recorded in members.json, with signal-first count names: mpra_umis, mpra_reads, transfection_umis, transfection_reads, and dna_reads where available. Counts are non-negative integers; null optional counts mean unknown. cre_id_original retains the original identity of pooled negative controls. The paper's supplementary note defines the complete read-wise, UMI-wise, and coarse-reporter schemas.

Coarse transfection-reporter detections, when present, are stored separately as _coarse_reporter in members.pkl, with one presence-only row per (rep_id, cell_bc, cre_id). They are loaded with the training data and are not paired with individual MPRA UMIs. Zero-handling settings determine how these detections contribute to a fit; the main count table alone does not encode them.

Each fit is one archive, so download only the ones you need:

import tarfile
from huggingface_hub import hf_hub_download
from distributed import Client, LocalCluster
from scMPRAforge.core import ortho

name = "seelig_cm_moib_nb_phantom"
archive = hf_hub_download("saarantras1/scMPRAforge_models", f"orthos/{name}.tar.gz",
                          repo_type="dataset")
tarfile.open(archive).extractall("orthos", filter="data")

client = Client(LocalCluster())
o = ortho.load(client, "orthos", name)

These archives require the schema-version-2 reader. A compatible package revision is 6e67ed8:

pip install "scMPRAforge @ git+https://github.com/Reilly-Lab-Yale/scMPRAforge.git@6e67ed8c49609de57b64bd0b2b556f17b4b12467"

Licence and source data

files	licence	reason
`orthos/seelig_*.tar.gz`	CC-BY-NC-4.0	contains Yin et al.'s count table, published under CC-BY-NC-4.0
everything else	CC-BY-4.0	our fits, and the `shendure` and `cohen` count tables, whose sources are CC-BY-4.0

The count tables are processed from the depositing studies' public data:

dataset	deposit	source terms
`shendure`	GEO GSE217686	paper is CC-BY-4.0
`cohen`	GEO GSE188639, Zenodo 14907846	Zenodo deposit is CC-BY-4.0
`seelig`	GEO GSE269037 (scMPRA), GSE269036 (bulk)	paper is CC-BY-NC-4.0

Citation

TODO on acceptance.