README.md

---

license: cc-by-nc-4.0
pretty_name: Z-Screen Source Data (June 2026 Release)
tags:
- drug-discovery
- single-cell
- transcriptomics
- high-content-imaging
- cheminformatics
- perturbation
size_categories:
- 100K<n<1M
configs:
- config_name: master_well_table
  data_files:
  - split: train
    path: raw_counts_data/MasterFile.parquet
- config_name: chemical_features
  data_files:
  - split: train
    path: chemical_features/compound_chemistry_feature_master.parquet
- config_name: image_embeddings
  data_files:
  - split: train
    path: normalized_image_embeddings/normalized_image_embeddings.parquet
- config_name: biomarker_intensity
  data_files:
  - split: train
    path: normalized_image_embeddings/biomarker_intensity_layer.parquet
- config_name: same_well_rna_image
  data_files:
  - split: train
    path: active_seq_same_well/joined_same_well_data_with_public_compound_id.parquet
- config_name: activeseq_compound_map
  data_files:
  - split: train
    path: active_seq_same_well/activeseq_control_name_public_compound_map.parquet
- config_name: ref_replogle_k562
  data_files:
  - split: train
    path: external_reference/crispr_perturbation_atlases/replogle_2022_k562_gwps/replogle_rank_signatures_top_genes.parquet
- config_name: ref_replogle_rpe1
  data_files:
  - split: train
    path: external_reference/crispr_perturbation_atlases/replogle_2022_rpe1/rpe1_rank_signatures_top_genes.parquet
- config_name: ref_norman_k562
  data_files:
  - split: train
    path: external_reference/crispr_perturbation_atlases/norman_weissman_2019_k562_crispra/norman_rank_signatures_top_genes.parquet
- config_name: ref_scperturb_thp1
  data_files:
  - split: train
    path: external_reference/crispr_perturbation_atlases/scperturb_thp1_selected/scperturb_rank_signatures_top_genes.parquet
- config_name: ref_lincs_l1000
  data_files:
  - split: train
    path: external_reference/compound_atlases/lincs_l1000/processed/lincs_l1000_rank_signatures_top_genes.parquet
---


# Z-Screen Source Data (June 2026 Release)

This deposit contains the curated public source-data layers for the Z-Screen platform, a
modality-agnostic single-nanowell drug-discovery screen that couples transcriptomic counts,
imaging morphology embeddings, and compound chemistry across a shared public compound identifier.

Each row of the primary data represents a single **nanowell** (90 microns diameter by 120 microns
tall, holding approximately 2 to 10 cells), not a single cell.

- **Nanowells profiled:** 768,162 across 49 devices (numbered 0 to 48)
- **Genes measured:** 46,944 (transcriptomic counts)
- **Cell lines / clone variants:** 6 (HEK293, HEK293-clone, A549, H1650, THP1, AEC7)
- **Libraries:** 14 (named-control compounds, OBOC combinatorial libraries, control beads)
- **Unique compounds / precursors:** 142,227 (78 named controls, 142,149 OBOC building blocks, plus controls)

The primary tables are joined 1-to-1 across `counts.h5ad`, `MasterFile.parquet`, and the chemistry
layer on the nanowell axis, and share a `public_compound_id` key that links biology to chemistry.

## Contents

```

raw_counts_data/               Raw transcriptomic counts + master well table

normalized_image_embeddings/   Normalized morphology features + biomarker intensities

chemical_features/             Public compound / building-block chemistry features

active_seq_same_well/          Same-well RNA + imaging calibration layer (ActiveSeq)

external_reference/            External benchmark atlases (ranked gene lists) + gene sets

README.md                      This file

MANIFEST.csv                   Per-file SHA256, size, and parquet row/column counts

```

### `raw_counts_data/` (~3.9 GB)

| File | Shape | Description |
|---|---|---|
| `counts.h5ad` | 768,162 wells x 46,944 genes | Raw transcriptomic counts (AnnData / HDF5). |
| `MasterFile.parquet` | 768,162 x 274 | Master per-well table (well/device/library/compound/QC metadata), 1-to-1 with `counts.h5ad`. |
| `data_shape_summary.md` | - | Human-readable data map (devices, cell lines, libraries, nomenclature). |
| `data_shape_visualization.html` | - | Interactive overview of dataset structure. |

> On the Zenodo record, each top-level folder above is provided as a single `.zip`
> (`raw_counts_data.zip` is 4.0 GB, MD5 `796235d30c4e7e821fe8453f07afae45`). On Hugging Face the
> same files are provided uncompressed for direct/streamed access.

### `normalized_image_embeddings/` (~6 MB)

| File | Shape | Description |
|---|---|---|
| `normalized_image_embeddings.parquet` | 21,795 x 39 | Compound-level normalized morphology (image) features, keyed by `public_compound_id`. |
| `biomarker_intensity_layer.parquet` | 18,942 x 11 | Protein/cellular-state marker intensities (BRD4, p21/CDKN1A, DAPI, actin, ConA, p62/SQSTM1). |

### `chemical_features/` (~295 MB)



| File | Shape | Description |

|---|---|---|

| `compound_chemistry_feature_master.parquet` | 144,645 x 961 | Compound + building-block chemistry, keyed by `public_compound_id`. Formula/InChIKey where available, plus Chemeleon and ChemBERTa embeddings (whole-molecule and per-building-block BB0 to BB4). |

### `active_seq_same_well/` (~440 MB)



The public layer supporting the claim that RNA and morphology were measured from the **same

physical nanowell**. The primary deliverable is self-contained; the remaining files are the full

pre-join latent spaces so the same-well matching can be reproduced from scratch.



| File | Shape | Role |

|---|---|---|

| `joined_same_well_data_with_public_compound_id.parquet` | 18,727 x 550 | **Primary deliverable.** Matched same-well rows carrying 32 RNA latent dims (`D00`-`D31`), 448 image latent dims (`img_lat_*`), well geometry, RNA QC, and public compound mapping. |

| `activeseq_control_name_public_compound_map.parquet` | 36 x 7 | Control-name to `public_compound_id` mapping (35 named compounds, 2 ActiveSeq devices). |

| `imaging_lats.parquet` | 126,662 x 451 | Full imaging-patch latent space (pre-join). |

| `rnaseq_lats.parquet` | 61,101 x 33 | Full RNA-cell latent space (pre-join). |

| `assay_well_dataframe.parquet` | 126,662 x 35 | Imaging-patch geometry / detection metadata. |

| `rnaseq_obs.parquet` | 61,101 x 27 | RNA-cell observation metadata / QC. |

| `matches_table.parquet` | 20,222 x 8 | Imaging-to-RNA well matches used to build the joined table. |



> Note: the redundant `joined_same_well_data.parquet` (a strict subset of the

> `_with_public_compound_id` table, differing only by a pandas index column) is intentionally

> excluded from this deposit.



### `external_reference/` (~182 MB)



Clean, reusable external-benchmark surface. The core assets are the **ranked gene-list signature

tables** (`*_rank_signatures_top_genes.parquet`) plus their metadata, catalogs, provenance, and
gene sets.

| Atlas | Key ranked-gene-list table |
|---|---|
| Replogle 2022 K562 genome-wide CRISPR KO | `crispr_perturbation_atlases/replogle_2022_k562_gwps/replogle_rank_signatures_top_genes.parquet` |
| Replogle 2022 RPE1 CRISPR KO | `crispr_perturbation_atlases/replogle_2022_rpe1/rpe1_rank_signatures_top_genes.parquet` |
| Norman & Weissman 2019 K562 CRISPRa | `crispr_perturbation_atlases/norman_weissman_2019_k562_crispra/norman_rank_signatures_top_genes.parquet` |
| Selected THP1 scPerturb CRISPR | `crispr_perturbation_atlases/scperturb_thp1_selected/scperturb_rank_signatures_top_genes.parquet` |
| LINCS L1000 compound signatures | `compound_atlases/lincs_l1000/processed/lincs_l1000_rank_signatures_top_genes.parquet` |
| Tahoe-100M | `compound_atlases/tahoe_100m/` (metadata + capmatinib slice) |
| Control transcriptome expansion (2026-05-25) | `compound_atlases/control_transcriptome_expansion_20260525/` |

Also included: `benchmark_outputs/` (prior LINCS vs Z-Screen overlap/correlation outputs),
`catalog/` (dataset catalog, file manifest with hashes, provenance), `gene_sets/` (Enrichr KEGG /
Hallmark / Reactome GMTs, disease-reversal and aging signature JSONs), and the provenance/fetch
scripts documenting how the tables were produced.

> Curation note: the raw external `.h5ad` matrices (`scperturb_downloads/`, ~6.3 GB) are **not**

> bundled here. They are publicly re-downloadable from scPerturb's own Zenodo record; see

> `crispr_perturbation_atlases/scperturb_thp1_selected/source_manifest/` and
> `external_reference/fetch_zenodo.py` for retrieval. Only the processed rank-signature tables are
> included, so downstream comparisons do not require rebuilding them.

## Loading

Parquet (Python):

```python

import pandas as pd

master = pd.read_parquet("raw_counts_data/MasterFile.parquet")

chem   = pd.read_parquet("chemical_features/compound_chemistry_feature_master.parquet")

# link biology to chemistry on the shared key

merged = master.merge(chem, on="public_compound_id", how="left")

```

Counts (AnnData):

```python

import anndata as ad

adata = ad.read_h5ad("raw_counts_data/counts.h5ad")   # 768,162 obs (nanowells) x 46,944 vars (genes)

```

Hugging Face `datasets` (streaming, no full download) - the main parquet tables are exposed as
named configs for the dataset viewer and `load_dataset`:

```python

from datasets import load_dataset

ds = load_dataset("Zafrens/zscreen_pilot_data", "master_well_table", split="train", streaming=True)

print(next(iter(ds)))

```

Available configs: `master_well_table`, `chemical_features`, `image_embeddings`,
`biomarker_intensity`, `same_well_rna_image`, `activeseq_compound_map`, `ref_replogle_k562`,
`ref_replogle_rpe1`, `ref_norman_k562`, `ref_scperturb_thp1`, `ref_lincs_l1000`. The `counts.h5ad`
matrix is not exposed to the viewer (load it with AnnData as shown above); all other files remain
directly downloadable.

## Data fields

Key columns of the headline tables (`public_compound_id` is the shared join key across all layers).

**`master_well_table`** (`raw_counts_data/MasterFile.parquet`, 768,162 x 274) - one row per nanowell,
aligned 1-to-1 with `counts.h5ad`:
- Identifiers: `obs_id`, `device_id`, `sample_id`, `cell_line`, `zlibrary`, `condition_role`
- Compound / chemistry: `public_compound_id` (+ `_source`), `public_bb0_id`..`public_bb4_id`,
  `chemistry_grain`, `name`, `molecular_formula`, `inchi_key`
- Chemistry embedding: `whole_chemeleon_chemeleon_proj_000`..`_NNN` (whole-molecule Chemeleon projection)

**`chemical_features`** (144,645 x 961) - one row per `public_compound_id`:

- Support / provenance: `source_*`, `condition_*`, `token_single_*`, `token_all_*`,

  `public_primary_bb0_id`..`bb4_id`, `molecular_formula`, `inchi_key`

- Embeddings: whole-molecule Chemeleon and ChemBERTa feature blocks, plus per-building-block

  Chemeleon blocks for BB0 through BB4



**`image_embeddings`** (21,795 x 39) - compound-level morphology:
- Keys: `aggregate_id`, `public_compound_id`, `zlibrary`, `image_dataset`, `is_control`,
  `cell_line_context`, `public_condition_level_mrna_join_key`
- Features: `pca_00`..`pca_31` (32 principal-component image features)

**`biomarker_intensity`** (18,942 x 11) - marker-state intensities (`*_channel_masked_mean_zrobust_mean`):

`BRD4_anti_target_curated`, `CDKN1A_p21`, `DAPI`, `PHALLOIDIN_ACTIN`, `CONCANAVALIN_A`, `SQSTM1_p62`.



**`same_well_rna_image`** (`joined_same_well_data_with_public_compound_id.parquet`, 18,727 x 550) -
matched same-nanowell rows: `D00`..`D31` (32 RNA latent dims), `img_lat_*` (448 image latent dims),
well geometry, RNA QC metrics, and the public compound mapping columns.

**`ref_*` tables** - external ranked gene-list signatures; each row is a (signature, gene) rank entry

used for gene-rank / phenomimic comparison. Treat a high similarity as a perturbation-state

hypothesis, not direct target identification.



## Integrity



`MANIFEST.csv` lists every file with its SHA256 checksum, byte size, and (for parquet) row and

column counts. Verify a download with, for example, `sha256sum -c` after reformatting, or by

recomputing hashes and comparing against the manifest.



## Excluded from this release



The following were deliberately left out of the deposit: the `Manuscripts_June2026` folder, the

`zscreen_v2` / `v3` / `v4` working directories, the raw external `scperturb_downloads/` H5AD

matrices, and the redundant `active_seq_same_well/joined_same_well_data.parquet`.



## License and citation



License: **CC BY-NC 4.0** (Creative Commons Attribution-NonCommercial 4.0 International).
You may share and adapt the data with attribution, for non-commercial purposes only.

This dataset is archived on Zenodo. Cite the **concept DOI**, which always resolves to the latest
version: **[10.5281/zenodo.21195738](https://doi.org/10.5281/zenodo.21195738)**. To pin the exact
files of a specific release for reproducibility, use that release's version DOI instead (v1,
`2026.06` = `10.5281/zenodo.21195739`). Please also cite the associated Z-Screen manuscript once
available.

```bibtex

@dataset{vijayan_zscreen_2026,

  author    = {Vijayan, Swamy},

  title     = {{Z-Screen Source Data: single-nanowell transcriptomics,

               image embeddings, and compound chemistry}},

  year      = {2026},

  publisher = {Zenodo},

  doi       = {10.5281/zenodo.21195738},

  url       = {https://doi.org/10.5281/zenodo.21195738}

}

```