| # Data dictionary |
|
|
| Core schemas and principal annex objects, plus the contracts that make the layers join. Detailed module schemas are linked beside their data. Contexts (8): `zel024_hek293`, |
| `zel024_h1650`, `zel028_hek293`, `zel028_a549`, `zel028_h1650`, |
| `zel031_a549`, `zel031_thp1`, `zel039_aec7`. Compound counts per context: |
| 13,914 / 10,686 / 61,396 / 40,622 / 25,906 / 8,321 / 9,041 / 20,813 |
| (190,699 compound-context pairs; 162,914 distinct compounds). |
|
|
| ## Contracts (read first) |
|
|
| **Row-alignment contract.** Every `{context}.npy` matrix (in |
| [core/surfaces/](../core/surfaces) and [core/usages/](../core/usages)) is row-aligned to its sibling |
| `{context}_compounds.parquet`: row *i* of the matrix is the compound named |
| in row *i* of the parquet, and the parquet has exactly one column, |
| `public_compound_id`. Surface, usage, and compounds tables for the same |
| context have the same row count and the same row order. There is no other |
| join key and no index column; positional alignment is the contract. |
|
|
| **Panel index-space contract.** The harmonized panel is defined by |
| [core/surfaces/harmonized_6000_genes.parquet](../core/surfaces/harmonized_6000_genes.parquet). Its `gene_index` column |
| holds raw positional indices into the 46,944-gene pseudobulk |
| space. Gene symbols for panel positions come ONLY from this file's `gene` |
| column; never map panel positions through any context's released |
| 3,000-gene panel, which is a different index space. |
|
|
| **Usages/basis contract.** Usage column *j* (0-based) is program P*j*+1 of |
| the pinned basis in [core/basis/](../core/basis) for the core usages and aligned prediction dumps. Usages are valid only against that basis ([core/basis/basis_registry.json](../core/basis/basis_registry.json)); a refit changes program identities. The annotated program atlas uses a separate label namespace; follow its documented mapping and scope. Same-well `D00`–`D31` coordinates are a separate representation. |
|
|
| **Fold convention.** fold = SHA256(`public_compound_id`) mod 5, computed |
| on the full 64-hex-character digest as an integer. The shared basis and |
| the reference model were both fit with fold 0 held out. Train on folds |
| 1-4, evaluate on fold 0. Note: promoted usages can be slightly more |
| predictable on fold 0 in some exercises, so clean cross-exercise |
| comparisons use folds 1-4. |
|
|
| **Measured vs predicted.** Core surfaces are processed measurements; usages are their projection onto a learned basis. Recipes and splits are metadata; the basis is a fitted representation, and benchmark tables contain model-evaluation results. [models/](../models) outputs, the `y_pred_z` arrays in the imaging annex dumps, and |
| any table marked `grammar_level` or model-predicted are predictions, and |
| are labeled as such where they ship. |
|
|
| **Empirical-p floors.** Empirical p-values are floored at the resolution |
| of their null: 1/1001 in the phenomimicry validation tables (1,000-draw |
| nulls) and 1/201 wherever a 200-draw null is used. Permutation p-values |
| in the chemistry tables ship as separate `*_p_permutation` columns |
| alongside the empirical ones. A printed `p=0.000` in the hypothesis |
| ledger means the value fell below the 200-draw floor, not that it is |
| exactly zero. |
|
|
| ## core/ |
|
|
| ### core/usages/ |
|
|
| - `usages_{context}.npy`: float32, (n_compounds, 32), free sign. |
| Per-compound program usages (`program_usage_projection_v1`): the |
| free-sign least-squares projection of the context-scaled harmonized |
| surface onto the pinned k = 32 basis. |
| - `usages_{context}_compounds.parquet`: one column: |
| `public_compound_id` (string). One row per usage-matrix row, aligned |
| positionally. |
|
|
| ### core/surfaces/ |
|
|
| - `surfaces_{context}.npy`: float32, (n_compounds, 6000). Device-centered |
| log1p-CP10k expression on the harmonized panel |
| (`device_centered_harmonized_surface_v1`). Column *j* is panel position |
| *j* of [harmonized_6000_genes.parquet](../core/surfaces/harmonized_6000_genes.parquet). |
| - `{context}_compounds.parquet`: one column: `public_compound_id`. |
| - [harmonized_6000_genes.parquet](../core/surfaces/harmonized_6000_genes.parquet): 6,000 rows, the panel definition |
| (`harmonized_panel_6000_v1`): |
| - `panel_position` (int64, 0-5999): column index in every |
| `surfaces_{context}.npy`. |
| - `gene_index` (int64): raw position in the 46,944-gene pseudobulk |
| space (full-width index space; see the contract above). |
| - `gene` (string): gene symbol. The only authoritative symbol mapping. |
| |
| ### core/basis/ |
|
|
| - [shared_basis_k32.npy](../core/basis/shared_basis_k32.npy): float32, (32, 6000), nonnegative |
| (`shared_program_basis_v1`). Row *j* is the gene-loading vector of |
| program P*j*+1, columns aligned to the panel. |
| - [shared_basis_k12.npy](../core/basis/shared_basis_k12.npy): float32, (12, 6000), nonnegative |
| (`shared_program_basis_k12_v1`), lower-resolution companion. |
| - [basis_registry.json](../core/basis/basis_registry.json): the version pin: |
| - `registry_name`, `basis_version` |
| (`shared_program_basis_v1`). |
| - `basis_fit_scope`: training folds 1-4 only; fold 0 untouched by any |
| fitting. |
| - `pinned_files`: list of `{package_name, file, k, sha256}` for the two |
| basis arrays. |
| - `validity_statement`: usages are valid only against the pinned basis |
| files. |
| |
| ### core/recipes.parquet |
|
|
| 162,914 rows, one per distinct `public_compound_id`: |
|
|
| - `public_compound_id` (string, `CPD_############`). |
| - `bb0`-`bb4` (string, `BB_##########`, or null when the position is |
| absent in that recipe). Position sets differ between libraries. |
| - `n_positions_occupied` (int64): how many of the five positions carry a |
| block. |
|
|
| ### core/splits/fold_assignments.parquet |
| |
| 190,699 rows (= total compound count across the 8 contexts): |
| |
| - `context` (string), `public_compound_id` (string), `fold` (int64, 0-4). |
| |
| ### core/benchmark/ |
| |
| [README.md](../core/benchmark/README.md) explains the two configurations (`per_context_expert`, |
| `context_token_trunk`), the two splits (`compound_5fold`, `bb_disjoint`), |
| and the two output spaces (program space vs decoded gene space). Tables: |
| |
| - [per_context_comparison_k32.csv](../core/benchmark/per_context_comparison_k32.csv) (16 rows): `context`, `split_type`, |
| `per_context_expert_mean`, `per_context_expert_sd_cells`, |
| `per_context_expert_n_compounds`, `context_token_trunk_mean`, |
| `context_token_trunk_sd_cells`, `context_token_trunk_n_compounds`, |
| `diff_mean`, `diff_ci_lo`, `diff_ci_hi`. Decoded gene-space mean |
| per-compound Pearson (mcPearson) per model with the paired difference |
| and its 95% confidence interval. |
| - [program_space_primary.csv](../core/benchmark/program_space_primary.csv) (90 rows): `arm`, `split_type`, |
| `target_variant`, `k`, `context`, `eval_only`, `n_runs`, |
| `prog_pearson_mean`, `prog_pearson_sd_programs`, `null_mean`, |
| `null_sd`, `z`. Mean per-program Pearson of predicted vs measured usage |
| coordinates against a permutation null, with the resulting z-score. |
| Includes the probe context `zel031_h1650` (never trained). |
| - [k_resolution.csv](../core/benchmark/k_resolution.csv) (64 rows): `context`, `split_type`, `arm`, `k`, |
| `decoded_mcpearson_mean`, `decoded_mcpearson_sd_cells`, |
| `prog_pearson_mean`, `prog_pearson_sd`. k = 12 vs k = 32. |
| - [correction_arm.csv](../core/benchmark/correction_arm.csv) (12 rows): `context`, `arm`, `target_variant` |
| (`device_centered_target` / `depth_regressed_target`), `k`, `n_cells`, |
| `prog_pearson_mean`, `prog_pearson_sd`, |
| `decoded_vs_device_centered_target_mean`, |
| `decoded_vs_device_centered_target_sd_cells`, |
| `decoded_vs_depth_regressed_target_mean`, |
| `decoded_vs_depth_regressed_target_sd_cells`. The depth-regressed |
| variant did not improve; the device-centered target is canonical. |
| - [program_signal_concentration.csv](../core/benchmark/program_signal_concentration.csv) (4 rows): `analysis_layer`, `metric`, |
| `range_min`, `range_max`, `note`. Gene-space versus program-space |
| repeat-measurement ceilings and cross-cell-line agreement; the evidence |
| table for the "10-20× stronger in program space" claim. |
| - [cross_context_probe.csv](../core/benchmark/cross_context_probe.csv) (9 rows): `ctx_a`, `ctx_b`, `n_compounds`, |
| `n_cells`, `measured_transfer_mean`, |
| `per_context_expert_transfer_mean`, `per_context_expert_transfer_sd_cells`, |
| `per_context_expert_measured_pred_corr`, |
| `per_context_expert_measured_pred_corr_sd`, |
| `context_token_trunk_transfer_mean`, |
| `context_token_trunk_transfer_sd_cells`, |
| `context_token_trunk_measured_pred_corr`, |
| `context_token_trunk_measured_pred_corr_sd`, `target_variant`, `k`, |
| `prog_pearson_mean`, `prog_pearson_sd`, `null_mean`, `null_sd`, `z`. |
| `ctx_b = probe_only_never_trained` rows are the never-trained cell-line |
| probe. |
| - [fold0_baseline_comparison.csv](../core/benchmark/fold0_baseline_comparison.csv) (72 rows): `context`, `model`, `split`, |
| `n_train`, `n_test`, `tuning_parameter`, `tuning_value`, |
| `mean_program_pearson`, `median_program_pearson`, `min_program_pearson`, |
| `max_program_pearson`, `n_programs_with_defined_pearson`, |
| `mean_compound_pearson`, `global_r2_vs_train_mean`, `rmse`, |
| `source_or_fit`. Per-context fold-0 test scores for a context-only |
| train-mean control, three transparent recipe/building-block models, and |
| the three reference-transformer seeds with their ensemble. |
| - [fold0_reference_model_summary.csv](../core/benchmark/fold0_reference_model_summary.csv) (8 rows): one row per context with |
| the control score, the simple-model scores, the three-seed transformer |
| ensemble score, and the winner. |
|
|
| ## models/ |
|
|
| See [models/README.md](../models/README.md) for usage and the fold-0 convention. |
|
|
| - `context_token_trunk_reference_eval_v1_seed{0,1,2}.pt`: PyTorch |
| checkpoints (`torch.load(..., weights_only=True)`), each a plain dict: |
| - `format`: `context_token_trunk_reference_eval_v1`. |
| - `state_dict`: model weights (keys match [model_def.py](../models/model_def.py) exactly). |
| - `architecture`: `d_model` 128, `n_heads` 4, `n_layers` 2, |
| `feedforward` 512, `dropout` 0.1, `n_programs` 32, `n_tokens` 8, |
| `d_chemistry` 128, `d_identity` 64, `n_libraries`, `n_cell_lines`, |
| `vocabulary_sizes` (per bb slot), `n_parameters` 473,120. |
| - `training`: `held_out_fold` 0, `seed`, `split_convention` |
| `compound_5fold`, `target` `device_centered_program_usage`, |
| `basis_version` `shared_program_basis_v1`, `max_epochs`, |
| `batch_size`, `learning_rate`, `weight_decay`, `warmup_epochs`, |
| `patience`, `best_epoch`, `epochs_run`, `best_val_score`, |
| `training_contexts`. |
| - `vocabulary`: `bb_slots` (`bb0`-`bb4`); `level_to_index` (per-slot |
| maps public BB ID → identity-embedding row; row 0 = absent slot, |
| row 1 = unseen ID); `libraries`; `cell_lines`; `context_tokens` |
| (per context `{library, cell_line, trained}`; the probe context is |
| marked `trained: false`). |
| - `usage_scales`: per context `{mu, sd}` (32 values each): the |
| per-context standardization mapping the raw head output |
| (`usage_z_scored`) back to usage units comparable to [core/usages/](../core/usages) |
| (`usage = usage_z_scored * sd + mu`). |
| - `provenance`: `role` ("reference model, evaluation grade"). |
| - [bb_embedding_table.parquet](../models/bb_embedding_table.parquet): 629 rows: `public_bb_id` (string) + |
| `embedding_000`…`embedding_127` (float32), the 128-dimensional |
| pretrained chemistry embedding per public BB ID. Public IDs only. |
| - [golden_predictions.json](../models/golden_predictions.json): environment verification fixture: |
| `format`, `seed` 0, `model_file`, `tolerance` 2e-6, `note`, `entries` |
| (20 fixed fold-0 recipes across 5 contexts, each `{context, |
| public_compound_id, recipe: {bb0..bb4}, prediction_usage_z_scored: |
| [32 floats]}`). |
| - [model_def.py](../models/model_def.py): self-contained architecture (PyTorch only). |
| - [predict.py](../models/predict.py): CPU inference CLI and `--check-golden` self-check. |
| |
| ## annex_imaging/ |
| |
| Full narrative: [annex_imaging/README.md](../annex_imaging/README.md). All per-compound tables key on |
| `public_compound_id`. |
| |
| - [zel024_compound_embeddings.parquet](../annex_imaging/zel024_compound_embeddings.parquet) (10,129 × 3,075) / |
| [zel031_compound_embeddings.parquet](../annex_imaging/zel031_compound_embeddings.parquet) (8,443 × 3,075): |
| `public_compound_id`, `n_fovs` (fields of view averaged), `n_panels`, |
| `rn50_0000`…`rn50_2047` (ResNet-50 embedding means), |
| `clip_0000`…`clip_1023` (CLIP embedding means). |
| - [zel024_compound_intensity.parquet](../annex_imaging/zel024_compound_intensity.parquet) (10,129 × 8): |
| `public_compound_id`, `n_fovs`, `n_panels`, `brightfield_cell_mean`, |
| `dapi_cell_mean`, `p21_cell_mean`, `p62_cell_mean`, |
| `phalloidin_cell_mean`. |
| - [zel031_compound_intensity.parquet](../annex_imaging/zel031_compound_intensity.parquet) (8,443 × 8): same key and support |
| columns; marker set `brd4_cell_mean`, `brightfield_cell_mean`, |
| `cona_cell_mean`, `dapi_cell_mean`, `phalloidin_cell_mean`. The two |
| libraries are different panels (p62 only in zel024; BRD4/ConA only in |
| zel031): pool only with panel-aware normalization. |
| - [zel039_imaging_latents.parquet](../annex_imaging/zel039_imaging_latents.parquet) (78,896 detections × 457): |
| `public_compound_id` (empty for unmapped detections; see |
| `mapped_to_compound_master`), `public_bb0_id`…`public_bb2_id`, |
| `mapped_to_compound_master` (bool), `zlibrary`, `cell_line_context`, |
| `scantype` (cell painting or immunofluorescent staining), `if_target` |
| (immunofluorescence target where applicable), `D0`…`D447` (per-detection |
| 448-dimensional image latents). |
| - [reliability/embedding_reliability.json](../annex_imaging/reliability/embedding_reliability.json): `min_fovs`, `n_splits`, `libraries` (per library: `n_fovs`, |
| `n_compounds`, and per backbone `rn50`/`clip`: |
| `split_half_sb_median_raw`, `null_median_raw`, |
| `split_half_sb_median_centered`, `null_median_centered`, |
| `n_compounds_evaluated`). Split-half = two half-averages of a |
| compound's FOVs correlated across compounds, Spearman-Brown corrected. |
| - [reliability/marker_reliability.json](../annex_imaging/reliability/marker_reliability.json): `libraries` (per library: `n_fovs`, `n_compounds`, `markers` with |
| per-marker `split_half_sb`; plus the marker-marker |
| correlation matrix). |
| - `image_to_program_predictions/{context}/` and |
| `chemistry_to_program_predictions/{context}/`: fold-clean test |
| predictions, one `.npz` per fold × seed × model. NPZ keys: |
| - `public_compound_id`: (n,) unicode strings, held-out test compounds |
| only. |
| - `y_true_z`: float32 (n, 32), measured usages, z-scored per program. |
| - `y_pred_z`: float32 (n, 32), predicted usages, same z-scored space. |
| |
| Filename grammar: `{features}_{model}__fold{f}_seed{s}.npz` for |
| image→program (features `clip`, `rn50` for zel024/zel031; `cp448`, |
| `if448`, `mean448`, `scantype896` for zel039; models `ridge`, `mlp`), |
| and `fold{f}_seed{s}.npz` for chemistry→program (a cross-attention |
| model over the building blocks). Contexts covered: zel024_hek293, |
| zel031_a549, zel031_thp1, zel039_aec7. Column *j* of either matrix is |
| program P*j*+1 of the pinned basis. |
| - [prediction_score_summary.csv](../annex_imaging/prediction_score_summary.csv) (175 rows): `family` |
| (`image_to_program` / `chemistry_to_program`), `context`, `model`, |
| `fold`, `seed`, `n_test_compounds`, `mean_program_pearson`. Derived |
| from the dumps; recomputing a row is shown in |
| [examples/04_join_imaging.ipynb](../examples/04_join_imaging.ipynb). |
| - [decomposition.csv](../annex_imaging/decomposition.csv) (128 rows: 4 contexts × 32 programs): `context`, |
| `program` (P01-P32), `image_features`, `chemistry_model`, `folds`, |
| `n_test_compounds`, `r2_chem`, `r2_img`, `r2_joint`, `chem_only`, |
| `img_only`, `shared`, `unexplained`, `img_only_fold_sd`, |
| `chem_only_fold_sd`, `img_only_null_mean`, `img_only_null_sd`, |
| `img_only_z`, `img_only_tail`, `chem_only_null_mean`, |
| `chem_only_null_sd`, `chem_only_z`, `chem_only_tail`, `rel_img`, |
| `img_only_rel_ceiling`, `r2_img_rel_ceiling`, `r_ceiling_img`. |
| Per-program variance ownership (chemistry alone / imaging alone / |
| shared / neither) with permutation nulls and reliability ceilings. |
| - [decision_grade_image_to_program_panels.csv](../annex_imaging/decision_grade_image_to_program_panels.csv) (35 rows): `context`, |
| `image_backbone` (clip / rn50), `model` (ridge / mlp), `fold`, `seed`, |
| `n_train_compounds`, `n_test_compounds`, `prog_pearson_mean`, |
| `prog_pearson_median`, `pairing_null_mean`, `pairing_null_sd`, |
| `pairing_null_z`, `pairing_null_tail_frac`. Cell-level decision-grade |
| image→program evaluation for zel024_hek293, zel031_thp1, zel031_a549. |
| - [decision_grade_image_to_program_zel039_aec7.csv](../annex_imaging/decision_grade_image_to_program_zel039_aec7.csv) (60 rows): `context`, |
| `model_variant` (mean448/scantype896 × ridge/mlp), `fold`, `seed`, |
| train/val/test counts, `prog_pearson_mean/median/min/max`, |
| `null_mean/sd/q95/tail_frac_ge`, `above_floor_q95`, |
| `margin_over_q95`, `model_hyperparameter`. The 5-fold × 3-seed × |
| 4-variant decision-grade zel039 evaluation. |
| - [decode_through_comparison.csv](../annex_imaging/decode_through_comparison.csv) (30 rows): `context`, `fold`, `seed`, |
| train/test counts, `bottleneck_mcpearson_*` (decode through the |
| 32-program bottleneck), `direct_mcpearson_*` (direct image→gene |
| prediction), `oracle_mcpearson_*` (usage-space ceiling), null q95s, |
| paired `diff_mean` with CI, `fraction_of_oracle_filled`. |
| - [marker_program_association.csv](../annex_imaging/marker_program_association.csv) (768 rows): `context`, `library`, |
| `marker`, `program`, `n_test`, `r_raw`, `z_raw`, `tail_raw`, |
| `r_partial`, `z_partial`, `tail_partial`, `marker_rel_sb`, `r_ceiling`, |
| `r_chem_pred`, `z_chem_pred`, `r_img_pred`, `z_img_pred`, `fw_flag`. |
| Marker intensity vs measured usages (`r_raw`; `r_partial` removes the |
| shared cell-density axis) and vs image-predicted / chemistry-predicted |
| usages, with nulls and reliability ceilings. |
| - [marker_program_depth_stratified.csv](../annex_imaging/marker_program_depth_stratified.csv) (3 rows): `context`, `marker`, |
| `program`, `depth_stratum`, `n_test`, `r_partial`, `r_raw`. |
|
|
| ## annex_hypotheses/ |
| |
| Read [annex_hypotheses/README.md](../annex_hypotheses/README.md) and [HOW_TO_READ.md](../annex_hypotheses/HOW_TO_READ.md) first. All tables are |
| hypothesis-generating. |
| |
| - [anchor_leads.csv](../annex_hypotheses/anchor_leads.csv) (16 rows): `hypothesis_id` (ZSH-####, stable), |
| `bb_level_ids`, `contexts`, `biological_program`, `matched_control`, |
| `moa_class`, `external_triangulation`, `evidence_summary`, |
| `confidence_tier` (A/B/C), `caveats` (controlled vocabulary: |
| `weak_null`, `singleton`, `triage_grade`, `low_coverage`), |
| `kill_confirm_experiment`. |
| - [program_atlas.csv](../annex_hypotheses/program_atlas.csv) (26 rows): `program_group_id`, `context`, |
| `primary_program`, `n_programs_merged`, `member_programs`, |
| `theme_family`, `named_theme`, `top_genes`, `top_gene_sets`, |
| `driving_bb_levels`, `n_bb_levels_q05`, `anchor_control`, |
| `anchor_top3_controls`, `crispr_ko_links`, |
| `member_compounds_usageZ_ge1`, `mean_abs_usage`, `frac_deep_top500`, |
| `library_frac_deep`, `depth_tier_skew_flag`, `ica_median_abs_cos`, |
| `confidence_tier`, `focus_experiment`. Program-atlas labels use the namespace documented in the [interpretation notes](../annex_hypotheses/INTERPRETATION_NOTES.md); they must not be used directly as current basis indices. |
| - [sharp_sar_candidates.csv](../annex_hypotheses/sharp_sar_candidates.csv) (133 rows): `candidate_id`, `context`, |
| `candidate_kind`, `n_members`, `within_phenotype_coherence`, |
| `coherence_null_z`, `coherence_permutation_p`, |
| `falloff_null_percentile`, `chemistry_within`, `ring1_similarity`, |
| `driver_genes`, `evidence_tier` (zel024 rows marked `grammar_level`), |
| `selection_note`. |
| - [hypothesis_ledger_full.csv](../annex_hypotheses/hypothesis_ledger_full.csv) (1,027 rows, triage-grade, |
| hypothesis-generating): `hypothesis_id`, `hypothesis_type`, `status` |
| (`hypothesis_anchor_validated` / `hypothesis_strong` / |
| `hypothesis_triage`), `confidence_tier`, `context`, `bb_level_ids`, |
| `claim`, `evidence`, `matched_control`, `moa_class`, `caveats`, |
| `evidence_rank_score`. |
|
|
| Current named-control target annotations and their primary citation are documented in [annotations](../annotations/README.md). |
|
|
| ## annex_phenomimicry/ |
| |
| Read [annex_phenomimicry/README.md](../annex_phenomimicry/README.md) first. These are |
| hypothesis-grade compound × CRISPR-knockout correspondence tables. Named-control |
| validation queries include random-target and hub-matched empirical comparisons; |
| consensus scores and family rankings have separate definitions. |
| |
| - [phenomimic_pairs.parquet](../annex_phenomimicry/phenomimic_pairs.parquet) (426,461 rows): `public_compound_id`, |
| `target_gene`, `n_contexts`, `contexts`, `best_z`, `mean_z`, |
| `best_rank`, `mean_rank`, `is_named_control`, `kd_delta_median`, |
| `kd_flag_weak`, `tier` (`B_consensus_strong` / `B_single_context` / |
| `C_moderate`), `bb0`-`bb4`, `hub_flag`. |
| - [antimimic_pairs.parquet](../annex_phenomimicry/antimimic_pairs.parquet) (561,355 rows): direction-adjusted opposition track, |
| selected by stored `best_z >= 5`. Context scores are the negative of the signed |
| consensus before maximum/mean aggregation; negative standardized consensus does |
| not prove negative unstandardized cosine or biological rescue. Columns: |
| `public_compound_id`, `target_gene`, `n_contexts`, `contexts`, `best_z`, `mean_z`, |
| `best_rank`, `mean_rank`, `is_named_control`, `kd_delta_median`, `kd_flag_weak`, |
| `hub_flag`. There is **no `tier` and no `bb0`–`bb4`**. [Score convention, |
| worked example and method](../annex_phenomimicry/SCORE_SEMANTICS.md). |
| - [showcase_hits.csv](../annex_phenomimicry/showcase_hits.csv) (2,000 rows): per-dataset evidence columns |
| (`n_datasets_scored`, `n_datasets_top5pct`, `n_datasets_top1pct`, |
| `best_percentile`, `max_cosine`, `median_percentile`, |
| `best_dataset_exact`) plus consensus columns and control annotations |
| (`name`, `moa_class`, `primary_target`). |
| - [top100_phenomimics.csv](../annex_phenomimicry/top100_phenomimics.csv) (101 rows): `rank`, `family_id`, |
| `target_gene`, `family_size`, `family_max_z`, `family_evidence`, |
| `public_compound_id`, `best_z`, `tier`, `contexts`, `kd_flag_weak`, |
| `is_family_representative`, `n_contexts`, `family_contexts`, |
| `total_family_size`, `hub_flag`, `kd_delta_median`, `mean_top3_z`, |
| `replication_factor`, `composite_score`. |
| - [top100_family_summary.csv](../annex_phenomimicry/top100_family_summary.csv) (31 rows): `family_id`, `target_gene`, |
| `family_size`, `family_max_z`, `family_evidence`, `n_in_top100`, |
| `total_family_size`, `n_contexts`, `family_contexts`, `hub_flag`, |
| `kd_delta_median`, `mean_top3_z`, `replication_factor`, |
| `composite_score`. |
| - [validation_empirical_p.csv](../annex_phenomimicry/validation_empirical_p.csv) (2,074 rows): `context`, `dataset`, |
| `public_compound_id`, `target_gene`, `cosine`, `rank_mimic`, |
| `n_targets`, `n_shared_genes`, `percentile`, `control_name`, `p_emp`, |
| `p_emp_hub`, `p_emp_20draw`. |
| - [ensemble_rescoring_panel.csv](../annex_phenomimicry/ensemble_rescoring_panel.csv) (42 rows): `public_compound_id`, |
| `compound_name`, `target_gene`, `ens_min_best_percentile`, |
| `cosine_best_percentile`, `frac_cells_top20`, `n_cells`, |
| `consistent_pair`. The per-pair table behind the ensemble-rescoring |
| section of the annex README. |
| - [target_hubness.csv](../annex_phenomimicry/target_hubness.csv) (18,789 rows): `target_gene`, |
| `n_compounds_hit`, `hub_frac`, `hub_flag`. |
|
|
| Additional records: |
|
|
| - [family_null_check.csv](../annex_phenomimicry/family_null_check.csv): 100 degree-weighted draws plus six summary rows (`draw`, `count_all_filtered`, `count_replicated_subset`). There are 166 observed filtered families versus a null mean of 108.79 and SD of 9.44277. The replicated-subset count is zero in both the observed set and every draw (empirical p = 1). [Null method and scope](../annex_phenomimicry/FAMILY_NULL_METHOD.md). |
| - [curation_summary.json](../annex_phenomimicry/curation_summary.json): current descriptive counts and known-control calibration summaries. |
|
|
| ## annex_chemistry/ |
| |
| Column-by-column definitions with interpretation are in |
| [annex_chemistry/README.md](../annex_chemistry/README.md); headers for reference: |
| |
| - [novel_bb_generalization.csv](../annex_chemistry/novel_bb_generalization.csv) (8 rows): `context`, |
| `structure_readout_model_r_mean`, `structure_readout_model_r_sd`, |
| `identity_readout_model_r_mean`, `structure_minus_identity_r_mean`, |
| `structure_minus_identity_r_ci95`, |
| `structure_minus_identity_frac_folds_positive`, |
| `reference_trunk_model_r_mean`, `structure_minus_trunk_r_mean`, |
| `structure_ridge_r_mean`, `additive_embedding_floor_r_mean`, |
| `structure_ridge_minus_floor_r_mean`, |
| `structure_ridge_minus_floor_r_ci95`, |
| `structure_ridge_minus_floor_frac_folds_positive`. |
| - [attribution_certificate.csv](../annex_chemistry/attribution_certificate.csv) (6 rows): `context`, `fold`, |
| `usage_target`, `structure_model_r`, `model_null_z`, |
| `n_building_blocks`, `bb_knockout_vs_measurement_median_spearman`, |
| `bb_knockout_null_mean`, `bb_knockout_null_sd`, `bb_knockout_z`, |
| `parts_sum_vs_direct_median_spearman`, `n_structure_parts`, |
| `negative_control_median_spearman` (empty where not applicable). |
| - [activity_cliffs.csv](../annex_chemistry/activity_cliffs.csv) (450 rows): `context`, `bb_pair_id`, |
| `n_members`, `within_phenotype_coherence`, |
| `within_candidate_chemical_coherence`, `ring1_phenotype_correlation`, |
| `ring2_phenotype_correlation`, `ring3_phenotype_correlation`, |
| `falloff`, `null_falloff_mean`, `null_falloff_sd`, |
| `falloff_null_percentile`. |
| - [chemotype_series.csv](../annex_chemistry/chemotype_series.csv) (2,727 rows): `context`, `evidence_level` |
| (`measured` / `grammar_level`), `bb_pair_id`, `position_a`, `bb_id_a`, |
| `position_b`, `bb_id_b`, `n_members`, `effect_strength_l2`, |
| `top20_gene_energy_fraction`, `participation_ratio`, `coherence`, |
| `sibling_max_cos`, `sibling_mean_cos`, `n_siblings`, |
| `null_coherence_mean`, `null_coherence_sd`, `coherence_z_vs_null`, |
| `coherence_p_empirical`, `coherence_q_fdr_bh`, |
| `effect_strength_z_vs_null`, `effect_strength_p_empirical`, |
| `coherence_p_permutation`, `mean_shrinkage_weight`, `mean_n_wells`, |
| `composite_score`, `composite_rank`, `is_positive_control`, |
| `driver_genes_up`, `driver_genes_down`, and per-depth-tier fractions |
| (`frac_*_depth_tier`, `library_base_frac_*_depth_tier`, |
| `delta_frac_*_depth_tier`). |
| - [bb_effect_rankings.csv](../annex_chemistry/bb_effect_rankings.csv) (431 rows): `context`, `position`, `bb_id`, |
| `n_carriers`, `in_sar_window`, `coherence`, `coherence_z`, |
| `coherence_p`, `sparse_coherence`, `sparse_coherence_z`, |
| `sibling_max_cos`, `sibling_max_sparse_j`, `sparse_falloff`, |
| `effect_strength_l2`, `top20_gene_energy_fraction`, |
| `delta_frac_deep_depth_tier`, `driver_genes_up`, `driver_genes_down`, |
| `is_positive_control`, `sparse_composite_score`, |
| `coherence_p_permutation`, `sparse_rank`, |
| `within_candidate_chemical_coherence`, `ring1_phenotype_correlation`, |
| `ring3_phenotype_correlation`, `falloff`, `falloff_null_percentile`. |
|
|
| ## annex_same_well/ |
|
|
| Full narrative: [annex_same_well/README.md](../annex_same_well/README.md). The well key is |
| (`batch_id`, `well_id`); `well_id` is unique only within a batch. Context |
| label: `same_well_hek293`. |
|
|
| - [same_well_wells.parquet](../annex_same_well/same_well_wells.parquet) (11,435 × 486): `batch_id` (`batch_1` / |
| `batch_2`), `well_id`, `public_compound_id`, `control_name`, |
| `cell_line`, `n_detections`, `img_lat_0_0`…`img_lat_3_63` (448 image |
| latents, mean over the well's detections; blocks 0-2 × 128 plus block 3 |
| × 64), `D00`…`D31` (32 RNA latents, constant within a well). |
| - [same_well_detections.parquet](../annex_same_well/same_well_detections.parquet) (14,757 × 486): the detection-level |
| table behind the wells table: same columns plus `detection_index` |
| (0-based within each well); image latents are per-detection, RNA latents |
| repeat per well. |
| - [control_compound_map.csv](../annex_same_well/control_compound_map.csv) (35 rows): `control_name`, |
| `public_compound_id`, `public_compound_name`. |
| - [evidence/cross_modal_regimes.csv](../annex_same_well/evidence/cross_modal_regimes.csv) (3 rows): `regime`, `n_wells`, |
| `alpha`, `mcPearson`, `R2_mean_perdim`, `R2_pooled`, `null_mean`, |
| `null_std`, `null_p95`, `p_value`. Image→RNA ridge predictability in |
| three regimes (raw / within-control / across-control LOGO). |
| - [evidence/per_control_coupling.csv](../annex_same_well/evidence/per_control_coupling.csv) (36 rows: 35 controls plus a |
| `GLOBAL_WITHIN_CONTROL` summary row): `control`, `n_wells`, |
| `pls1_corr`, `null_mean`, `null_std`, `null_p95`, `p_value`. |
| - [evidence/learning_curve.csv](../annex_same_well/evidence/learning_curve.csv) (6 rows): `n_wells`, `mcPearson_mean`, |
| `mcPearson_std`, `null_mean`, `null_std`, `null_p025`, `null_p975`. |
| - [evidence/learning_curve.png](../annex_same_well/evidence/learning_curve.png): the learning curve, plotted. |
|
|
| - [evidence/mantel_bootstrap.csv](../annex_same_well/evidence/mantel_bootstrap.csv): two resampling summaries with `estimate`, `mantel_r`, `perm_p`, `boot_ci_lo`, `boot_ci_hi`, `boot_mean`, `boot_std`. The Mantel r correlates pairwise correlation distances between 35 control means in image and RNA space (r = 0.77289; two-sided permutation p = 1/2,001). Controls and wells are different bootstrap units; the well bootstrap samples image and RNA wells independently within fixed controls. [Methods and runnable check](../annex_same_well/MANTEL_METHODS.md). |
|
|
|
|
|
|
| ## annex_clusters/ |
| |
| Read [annex_clusters/README.md](../annex_clusters/README.md) first. |
| |
| - [cluster_census.csv](../annex_clusters/cluster_census.csv) (1,007 rows): `context`, `cluster_id`, |
| `n_members`, `coherence`, `coherence_z`, `coherence_q`, `anchor_bb`, |
| `anchor_pos`, `anchor_frac`, `top_up_set`, `top_up_q`, `top_dn_set`, |
| `top_dn_q`, `prior_series_n` (count of matching chemistry-series records by context and anchor building block). One row per chemistry-constrained response-family |
| cluster recovered from the 32-program usage vectors; every row at |
| coherence q <= 0.01 against 200 size-matched random compound sets. |
|
|
| - [pathway_family_counts.csv](../annex_clusters/pathway_family_counts.csv): current pathway-family totals across the 1,007 census rows; 855 have a significant annotation and 152 do not. [Selection and grouping rule](../annex_clusters/pathway_family_method.json). |
|
|
| ## annotations/ |
|
|
| [Control target annotations](../annotations/control_target_annotations.csv) records current targets, mechanisms and primary citations. [Annotation guide](../annotations/README.md) explains the MSC1094308 VCP/p97 and VPS4B annotation and the distinction between chemical response similarity and direct target evidence. |
|
|
| ## figures/ |
|
|
| [Figure guide](../figures/README.md) indexes all six overview assets. [platform_counts.json](../figures/platform_counts.json) stores the five overview counts. [measured_and_nominal_design_space.csv](../figures/measured_and_nominal_design_space.csv) has one row per context: `observed_compounds`, `bb0`–`bb4` vocabulary sizes, `nominal_independent_combinations`, `observed_to_nominal_fraction`, and `scope`. PNG/SVG pairs visualize those inputs. [The results reference](PILOT_RESULTS_REFERENCE.md) explains the distinct library-union calculation. |
|
|
|
|
| ## Additional pilot objects |
|
|
| | Module | Principal key and unit | Schema/method entry | |
| |---|---|---| |
| | Response-family members/centroids | `context, cluster_id` and explicit `atlas_id`; member order and centroid row are separate | [Response-family atlas](../atlas/original_clusters/README.md) | |
| | Selected case-study evidence | Chemical family/group; objective, policy, start and round; processed summaries | [Case-study data guide](../annex_case_studies/DATA_GUIDE.md) | |
| | Measurement-design evidence | Exact well key or context/control/batch; sample/partition IDs and training size | [Measurement design](../annex_measurement_design/README.md) | |
| | Microscopy examples | Image crop index, channel and public compound ID; RNA linked by compound | [Gallery](../gallery/DATA_GUIDE.md) | |
|
|
| The [annex index](ANNEX_INDEX.md) distinguishes measured profiles, predictions, correspondence analyses and hypotheses. [Data access](ANALYSIS_ACCESS.md) describes the processed-data scope and how to obtain raw measurements or external genetic references. |
|
|
