| ---
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| license: cc-by-nc-4.0
|
| pretty_name: Z-Screen Source Data (June 2026 Release)
|
| tags:
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| - drug-discovery
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| - single-cell
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| - transcriptomics
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| - high-content-imaging
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| - cheminformatics
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| - perturbation
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| size_categories:
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| - 100K<n<1M
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| configs:
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| - config_name: master_well_table
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| data_files:
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| - split: train
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| path: raw_counts_data/MasterFile.parquet
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| - config_name: chemical_features
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| data_files:
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| - split: train
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| path: chemical_features/compound_chemistry_feature_master.parquet
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| - config_name: image_embeddings
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| data_files:
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| - split: train
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| path: normalized_image_embeddings/normalized_image_embeddings.parquet
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| - config_name: biomarker_intensity
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| data_files:
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| - split: train
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| path: normalized_image_embeddings/biomarker_intensity_layer.parquet
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| - config_name: same_well_rna_image
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| data_files:
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| - split: train
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| path: active_seq_same_well/joined_same_well_data_with_public_compound_id.parquet
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| - config_name: activeseq_compound_map
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| data_files:
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| - split: train
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| path: active_seq_same_well/activeseq_control_name_public_compound_map.parquet
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| - config_name: ref_replogle_k562
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| data_files:
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| - split: train
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| path: external_reference/crispr_perturbation_atlases/replogle_2022_k562_gwps/replogle_rank_signatures_top_genes.parquet
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| - config_name: ref_replogle_rpe1
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| data_files:
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| - split: train
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| path: external_reference/crispr_perturbation_atlases/replogle_2022_rpe1/rpe1_rank_signatures_top_genes.parquet
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| - config_name: ref_norman_k562
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| data_files:
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| - split: train
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| path: external_reference/crispr_perturbation_atlases/norman_weissman_2019_k562_crispra/norman_rank_signatures_top_genes.parquet
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| - config_name: ref_scperturb_thp1
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| data_files:
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| - split: train
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| path: external_reference/crispr_perturbation_atlases/scperturb_thp1_selected/scperturb_rank_signatures_top_genes.parquet
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| - config_name: ref_lincs_l1000
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| data_files:
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| - split: train
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| 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
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| modality-agnostic single-nanowell drug-discovery screen that couples transcriptomic counts,
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| 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
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| tall, holding approximately 2 to 10 cells), not a single cell.
|
|
|
| - **Nanowells profiled:** 768,162 across 49 devices (numbered 0 to 48)
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| - **Genes measured:** 46,944 (transcriptomic counts)
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| - **Cell lines / clone variants:** 6 (HEK293, HEK293-clone, A549, H1650, THP1, AEC7)
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| - **Libraries:** 14 (named-control compounds, OBOC combinatorial libraries, control beads)
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| - **Unique compounds / precursors:** 142,227 (78 named controls, 142,149 OBOC building blocks, plus controls)
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|
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| The primary tables are joined 1-to-1 across `counts.h5ad`, `MasterFile.parquet`, and the chemistry
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| layer on the nanowell axis, and share a `public_compound_id` key that links biology to chemistry.
|
|
|
| ## Contents
|
|
|
| ```
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| raw_counts_data/ Raw transcriptomic counts + master well table
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| normalized_image_embeddings/ Normalized morphology features + biomarker intensities
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| chemical_features/ Public compound / building-block chemistry features
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| active_seq_same_well/ Same-well RNA + imaging calibration layer (ActiveSeq)
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| external_reference/ External benchmark atlases (ranked gene lists) + gene sets
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| README.md This file
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| MANIFEST.csv Per-file SHA256, size, and parquet row/column counts
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| ```
|
|
|
| ### `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`. |
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| | `data_shape_summary.md` | - | Human-readable data map (devices, cell lines, libraries, nomenclature). |
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| | `data_shape_visualization.html` | - | Interactive overview of dataset structure. |
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|
|
| > On the Zenodo record, each top-level folder above is provided as a single `.zip`
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| > (`raw_counts_data.zip` is 4.0 GB, MD5 `796235d30c4e7e821fe8453f07afae45`). On Hugging Face the
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| > 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`. |
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| | `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)
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|
|
| The public layer supporting the claim that RNA and morphology were measured from the **same
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| physical nanowell**. The primary deliverable is self-contained; the remaining files are the full
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| pre-join latent spaces so the same-well matching can be reproduced from scratch.
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|
|
| | 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). |
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| | `imaging_lats.parquet` | 126,662 x 451 | Full imaging-patch latent space (pre-join). |
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| | `rnaseq_lats.parquet` | 61,101 x 33 | Full RNA-cell latent space (pre-join). |
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| | `assay_well_dataframe.parquet` | 126,662 x 35 | Imaging-patch geometry / detection metadata. |
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| | `rnaseq_obs.parquet` | 61,101 x 27 | RNA-cell observation metadata / QC. |
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| | `matches_table.parquet` | 20,222 x 8 | Imaging-to-RNA well matches used to build the joined table. |
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|
|
| > Note: the redundant `joined_same_well_data.parquet` (a strict subset of the
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| > `_with_public_compound_id` table, differing only by a pandas index column) is intentionally
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| > excluded from this deposit.
|
|
|
| ### `external_reference/` (~182 MB)
|
|
|
| Clean, reusable external-benchmark surface. The core assets are the **ranked gene-list signature
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| tables** (`*_rank_signatures_top_genes.parquet`) plus their metadata, catalogs, provenance, and
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| gene sets.
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|
|
| | Atlas | Key ranked-gene-list table |
|
| |---|---|
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| | Replogle 2022 K562 genome-wide CRISPR KO | `crispr_perturbation_atlases/replogle_2022_k562_gwps/replogle_rank_signatures_top_genes.parquet` |
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| | Replogle 2022 RPE1 CRISPR KO | `crispr_perturbation_atlases/replogle_2022_rpe1/rpe1_rank_signatures_top_genes.parquet` |
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| | Norman & Weissman 2019 K562 CRISPRa | `crispr_perturbation_atlases/norman_weissman_2019_k562_crispra/norman_rank_signatures_top_genes.parquet` |
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| | Selected THP1 scPerturb CRISPR | `crispr_perturbation_atlases/scperturb_thp1_selected/scperturb_rank_signatures_top_genes.parquet` |
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| | LINCS L1000 compound signatures | `compound_atlases/lincs_l1000/processed/lincs_l1000_rank_signatures_top_genes.parquet` |
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| | Tahoe-100M | `compound_atlases/tahoe_100m/` (metadata + capmatinib slice) |
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| | Control transcriptome expansion (2026-05-25) | `compound_atlases/control_transcriptome_expansion_20260525/` |
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|
|
| Also included: `benchmark_outputs/` (prior LINCS vs Z-Screen overlap/correlation outputs),
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| `catalog/` (dataset catalog, file manifest with hashes, provenance), `gene_sets/` (Enrichr KEGG /
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| Hallmark / Reactome GMTs, disease-reversal and aging signature JSONs), and the provenance/fetch
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| scripts documenting how the tables were produced.
|
|
|
| > Curation note: the raw external `.h5ad` matrices (`scperturb_downloads/`, ~6.3 GB) are **not**
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| > bundled here. They are publicly re-downloadable from scPerturb's own Zenodo record; see
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| > `crispr_perturbation_atlases/scperturb_thp1_selected/source_manifest/` and
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| > `external_reference/fetch_zenodo.py` for retrieval. Only the processed rank-signature tables are
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| > included, so downstream comparisons do not require rebuilding them.
|
|
|
| ## Loading
|
|
|
| Parquet (Python):
|
|
|
| ```python
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| import pandas as pd
|
| master = pd.read_parquet("raw_counts_data/MasterFile.parquet")
|
| chem = pd.read_parquet("chemical_features/compound_chemistry_feature_master.parquet")
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| # link biology to chemistry on the shared key
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| merged = master.merge(chem, on="public_compound_id", how="left")
|
| ```
|
|
|
| Counts (AnnData):
|
|
|
| ```python
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| import anndata as ad
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| 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
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| named configs for the dataset viewer and `load_dataset`:
|
|
|
| ```python
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| from datasets import load_dataset
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| ds = load_dataset("Zafrens/zscreen_pilot_data", "master_well_table", split="train", streaming=True)
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| print(next(iter(ds)))
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| ```
|
|
|
| Available configs: `master_well_table`, `chemical_features`, `image_embeddings`,
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| `biomarker_intensity`, `same_well_rna_image`, `activeseq_compound_map`, `ref_replogle_k562`,
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| `ref_replogle_rpe1`, `ref_norman_k562`, `ref_scperturb_thp1`, `ref_lincs_l1000`. The `counts.h5ad`
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| matrix is not exposed to the viewer (load it with AnnData as shown above); all other files remain
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| 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,
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| aligned 1-to-1 with `counts.h5ad`:
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| - Identifiers: `obs_id`, `device_id`, `sample_id`, `cell_line`, `zlibrary`, `condition_role`
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| - Compound / chemistry: `public_compound_id` (+ `_source`), `public_bb0_id`..`public_bb4_id`,
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| `chemistry_grain`, `name`, `molecular_formula`, `inchi_key`
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| - 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_*`,
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| `public_primary_bb0_id`..`bb4_id`, `molecular_formula`, `inchi_key`
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| - Embeddings: whole-molecule Chemeleon and ChemBERTa feature blocks, plus per-building-block
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| Chemeleon blocks for BB0 through BB4
|
|
|
| **`image_embeddings`** (21,795 x 39) - compound-level morphology:
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| - Keys: `aggregate_id`, `public_compound_id`, `zlibrary`, `image_dataset`, `is_control`,
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| `cell_line_context`, `public_condition_level_mrna_join_key`
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| - 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,
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| author = {Vijayan, Swamy},
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| title = {{Z-Screen Source Data: single-nanowell transcriptomics,
|
| image embeddings, and compound chemistry}},
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| year = {2026},
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| publisher = {Zenodo},
|
| doi = {10.5281/zenodo.21195738},
|
| url = {https://doi.org/10.5281/zenodo.21195738}
|
| }
|
| ```
|
|
|
