diff --git a/.beads/issues.jsonl b/.beads/issues.jsonl new file mode 100644 index 0000000..cbd3cf1 --- /dev/null +++ b/.beads/issues.jsonl @@ -0,0 +1,11 @@ +{"id":"data-a0y","title":"omicsio specification","status":"open","priority":2,"issue_type":"epic","owner":"cameron.ray.smith@gmail.com","created_at":"2026-02-18T17:22:11.753993-05:00","created_by":"Cameron Smith","updated_at":"2026-02-18T17:22:11.753993-05:00"} +{"id":"data-a0y.1","title":"Research lakehouse-native columnar encoding for multimodal single-cell data","description":"Context: omicsio converts AnnData/MuData to columnar formats (vortex, parquet). The primary design question is how to optimally represent multimodal count/expression matrices in lakehouse table formats.\n\nTwo known approaches to evaluate:\n- X-Atlas-Orion list-column format: ~/projects/omicslake-workspace/X-Atlas-Orion\n- pqdata COO format: ~/projects/omicslake-workspace/pqdata\n\nTwo coupled questions:\n1. Lakehouse-native representation compatible with both parquet and vortex, building on X-Atlas-Orion and extending to multimodal data\n2. Rehydration to AnnData/MuData with Arrow as in-memory intermediate for out-of-core DuckDB processing\n\nUse cases to evaluate against:\n- Transcriptomic data with total RNA\n- RNA separated by spliced/unspliced\n- RNA separated by spliced/unspliced/ambiguous\n- Multimodal with ATAC-seq data (10x multiome format)\n- MuData structures with modalities of different dimensions\n\nDownstream consumers whose access patterns constrain the format:\n- pyrovelocity: ~/projects/pyrovelocity-workspace/pyrovelocity\n- stormi: ~/projects/pyrovelocity-workspace/stormi-review/stormi\n- hodosome: ~/projects/hodosome-workspace/hodosome\n\nReference documents in this repo:\n- docs/notes/omicsio/single-cell-data-architecture-for-queries-and-training.md\n- docs/notes/omicsio/single-cell-processing-pipeline-binseq-to-count-matrices.md\n- docs/notes/omicsio/pqdata-vs-xatlas-orion-schema-comparison.md\n\nContext files:\n- packages/omicsio/CLAUDE.md -\u003e ~/projects/sciexp/planning/contexts/omicsio.md\n\nStarting artifact: docs/notes/omicsio/research/columnar-encoding-for-multimodal-single-cell-data.md\n\nFirst round is reference-based research: survey local repos and docs to map the decision space. Output is a framing document capturing what is known, what remains open, and what experiments would resolve the open questions.","status":"open","priority":2,"issue_type":"task","owner":"cameron.ray.smith@gmail.com","created_at":"2026-02-18T17:22:19.109095-05:00","created_by":"Cameron Smith","updated_at":"2026-02-18T17:22:40.340002-05:00","dependencies":[{"issue_id":"data-a0y.1","depends_on_id":"data-a0y","type":"parent-child","created_at":"2026-02-18T17:22:19.10975-05:00","created_by":"Cameron Smith"}]} +{"id":"data-pvg","title":"Bootstrap sciexp data platform","status":"inreview","priority":2,"issue_type":"epic","owner":"cameron.ray.smith@gmail.com","created_at":"2026-02-18T17:21:12.110237-05:00","created_by":"Cameron Smith","updated_at":"2026-02-18T17:31:25.247825-05:00"} +{"id":"data-pvg.1","title":"Migrate predecessor documentation","status":"closed","priority":2,"issue_type":"task","owner":"cameron.ray.smith@gmail.com","created_at":"2026-02-18T17:21:17.166594-05:00","created_by":"Cameron Smith","updated_at":"2026-02-18T17:21:31.333473-05:00","closed_at":"2026-02-18T17:21:31.333473-05:00","close_reason":"Completed in 8 commits (3957269..26b57b3) on dat-bootstrap branch","dependencies":[{"issue_id":"data-pvg.1","depends_on_id":"data-pvg","type":"parent-child","created_at":"2026-02-18T17:21:17.167257-05:00","created_by":"Cameron Smith"}]} +{"id":"data-pvg.2","title":"Create scilake package skeleton","status":"closed","priority":2,"issue_type":"task","owner":"cameron.ray.smith@gmail.com","created_at":"2026-02-18T17:21:18.432294-05:00","created_by":"Cameron Smith","updated_at":"2026-02-18T17:21:35.03494-05:00","closed_at":"2026-02-18T17:21:35.03494-05:00","close_reason":"Completed in commits a00ba60..2966f3b on dat-bootstrap branch","dependencies":[{"issue_id":"data-pvg.2","depends_on_id":"data-pvg","type":"parent-child","created_at":"2026-02-18T17:21:18.432861-05:00","created_by":"Cameron Smith"}]} +{"id":"data-rg1","title":"scilake tool evaluation","status":"open","priority":2,"issue_type":"epic","owner":"cameron.ray.smith@gmail.com","created_at":"2026-02-18T17:22:43.983405-05:00","created_by":"Cameron Smith","updated_at":"2026-02-18T17:22:43.983405-05:00"} +{"id":"data-rg1.1","title":"Survey tool landscape from local repos and documentation","description":"Reference-based first pass across candidate tools for scilake's orchestration and catalog layers.\n\nTools to evaluate: DuckDB, DuckLake, dlt, SQLMesh, Dagster, Flyte.\n\nKey context:\n- Migrated ADRs: docs/notes/scilake/architecture/decisions/ (ingestion, metadata-store, orchestration)\n- Architecture overview: docs/notes/scilake/architecture/overview.md\n- DuckLake source: ~/projects/lakescope-workspace/ducklake/\n- scilake context: packages/scilake/CLAUDE.md -\u003e ~/projects/sciexp/planning/contexts/scilake.md\n\nNear-term constraint: DuckLake does not yet support vortex. Plan is managed directory trees of vortex/parquet files first, DuckLake adoption when vortex support lands.\n\nThe migrated ADRs favor Dagster (orchestration) and DuckLake (metadata). Treat these as hypotheses to validate, not commitments.\n\nConsider Dagster local dev overhead and production scaling. Flyte is an alternative. SQLMesh may be sufficient alone for some workflows. dlt may complement or overlap with other tools.\n\nOutput: assessment of each tool's capabilities, integration points, and constraints. Identification of which combinations to test experimentally and in what order.","status":"open","priority":2,"issue_type":"task","owner":"cameron.ray.smith@gmail.com","created_at":"2026-02-18T17:22:50.000747-05:00","created_by":"Cameron Smith","updated_at":"2026-02-18T17:23:24.409914-05:00","dependencies":[{"issue_id":"data-rg1.1","depends_on_id":"data-rg1","type":"parent-child","created_at":"2026-02-18T17:22:50.001544-05:00","created_by":"Cameron Smith"}]} +{"id":"data-rg1.2","title":"Implement DuckDB directory-tree catalog management in scilake","description":"Foundation layer for scilake. Implement managed directory trees of vortex/parquet files with DuckDB as query engine.\n\nThis is the base that all other tool integrations build on. DuckLake is deferred until it supports vortex; monitor its roadmap.\n\nImplement directly in packages/scilake/ as production code.\n\nHuggingFace Hub integration: upload files and access via DuckDB httpfs extension using hf:// URIs. HF Hub is git-lfs based and supports any file type.\n\nVortex is increasingly preferred over parquet for performance. Support both formats.","status":"open","priority":2,"issue_type":"task","owner":"cameron.ray.smith@gmail.com","created_at":"2026-02-18T17:22:53.78552-05:00","created_by":"Cameron Smith","updated_at":"2026-02-18T17:23:32.845226-05:00","dependencies":[{"issue_id":"data-rg1.2","depends_on_id":"data-rg1","type":"parent-child","created_at":"2026-02-18T17:22:53.786422-05:00","created_by":"Cameron Smith"},{"issue_id":"data-rg1.2","depends_on_id":"data-rg1.1","type":"blocks","created_at":"2026-02-18T17:23:57.178252-05:00","created_by":"Cameron Smith"}]} +{"id":"data-rg1.3","title":"Evaluate dlt integration for data ingestion","description":"Evaluate whether dlt adds value for extract-load into managed directory trees.\n\nConsider dlt standalone and composed with an orchestrator (Dagster/Flyte).\n\nImplement directly in packages/scilake/ as production code. If dlt doesn't prove valuable, remove it.","status":"open","priority":2,"issue_type":"task","owner":"cameron.ray.smith@gmail.com","created_at":"2026-02-18T17:23:00.451788-05:00","created_by":"Cameron Smith","updated_at":"2026-02-18T17:23:38.85833-05:00","dependencies":[{"issue_id":"data-rg1.3","depends_on_id":"data-rg1","type":"parent-child","created_at":"2026-02-18T17:23:00.452758-05:00","created_by":"Cameron Smith"},{"issue_id":"data-rg1.3","depends_on_id":"data-rg1.1","type":"blocks","created_at":"2026-02-18T17:23:57.29699-05:00","created_by":"Cameron Smith"},{"issue_id":"data-rg1.3","depends_on_id":"data-rg1.2","type":"blocks","created_at":"2026-02-18T17:24:02.231573-05:00","created_by":"Cameron Smith"}]} +{"id":"data-rg1.4","title":"Evaluate SQLMesh for transformation layer","description":"Evaluate SQLMesh for incremental transformations over directory-tree data.\n\nKey questions:\n- How does SQLMesh integrate with DuckDB?\n- Is SQLMesh sufficient as a standalone orchestration solution?\n- Or should it be integrated with Dagster/Flyte?\n\nImplement directly in packages/scilake/ as production code.","status":"open","priority":2,"issue_type":"task","owner":"cameron.ray.smith@gmail.com","created_at":"2026-02-18T17:23:04.88131-05:00","created_by":"Cameron Smith","updated_at":"2026-02-18T17:23:44.51313-05:00","dependencies":[{"issue_id":"data-rg1.4","depends_on_id":"data-rg1","type":"parent-child","created_at":"2026-02-18T17:23:04.88191-05:00","created_by":"Cameron Smith"},{"issue_id":"data-rg1.4","depends_on_id":"data-rg1.1","type":"blocks","created_at":"2026-02-18T17:23:57.425759-05:00","created_by":"Cameron Smith"},{"issue_id":"data-rg1.4","depends_on_id":"data-rg1.2","type":"blocks","created_at":"2026-02-18T17:24:02.370434-05:00","created_by":"Cameron Smith"}]} +{"id":"data-rg1.5","title":"Evaluate orchestration layer","description":"Evaluate whether a dedicated orchestrator is needed and which one.\n\nOptions: Dagster, Flyte, SQLMesh-only (if SQLMesh proves sufficient), or no orchestrator.\n\nThis depends on findings from the dlt and SQLMesh evaluations — which tools survived and how they compose determines what orchestration needs remain.\n\nConsider: local dev environment overhead, production scaling, integration with tools from prior evaluations.\n\nImplement directly in packages/scilake/ as production code.","status":"open","priority":2,"issue_type":"task","owner":"cameron.ray.smith@gmail.com","created_at":"2026-02-18T17:23:08.513883-05:00","created_by":"Cameron Smith","updated_at":"2026-02-18T17:23:51.092749-05:00","dependencies":[{"issue_id":"data-rg1.5","depends_on_id":"data-rg1","type":"parent-child","created_at":"2026-02-18T17:23:08.5145-05:00","created_by":"Cameron Smith"},{"issue_id":"data-rg1.5","depends_on_id":"data-rg1.1","type":"blocks","created_at":"2026-02-18T17:23:57.553935-05:00","created_by":"Cameron Smith"},{"issue_id":"data-rg1.5","depends_on_id":"data-rg1.2","type":"blocks","created_at":"2026-02-18T17:24:02.521603-05:00","created_by":"Cameron Smith"},{"issue_id":"data-rg1.5","depends_on_id":"data-rg1.3","type":"blocks","created_at":"2026-02-18T17:24:06.620935-05:00","created_by":"Cameron Smith"},{"issue_id":"data-rg1.5","depends_on_id":"data-rg1.4","type":"blocks","created_at":"2026-02-18T17:24:06.756134-05:00","created_by":"Cameron Smith"}]} diff --git a/.dvc/config b/.dvc/config index 5919cd3..d78a965 100644 --- a/.dvc/config +++ b/.dvc/config @@ -2,7 +2,7 @@ remote = gcs analytics = false ['remote "gcs"'] - url = gs://sciexp/projects/omicsio/cas + url = gs://sciexp/projects/data/cas credentialpath = ../.dvc-sa.json ['remote "drive"'] url = gdrive://1yS1zpTqR4w2WFjXQuV8HypMrQqJvcM-x/dvcstore diff --git a/.gitignore b/.gitignore index f43d970..91031da 100644 --- a/.gitignore +++ b/.gitignore @@ -103,3 +103,4 @@ node_modules/ # bv (beads viewer) local config and caches .bv/ +.wrangler diff --git a/docs/_freeze.dvc b/docs/_freeze.dvc index 59f5966..878197c 100644 --- a/docs/_freeze.dvc +++ b/docs/_freeze.dvc @@ -1,6 +1,6 @@ outs: -- md5: 481f00b14626c9c558a990f58cc704f7.dir - size: 9160 - nfiles: 2 +- md5: d751713988987e9331980363e24189ce.dir + size: 0 + nfiles: 0 hash: md5 path: _freeze diff --git a/docs/_quarto.yml b/docs/_quarto.yml index 534063b..76f4843 100644 --- a/docs/_quarto.yml +++ b/docs/_quarto.yml @@ -1,6 +1,9 @@ project: type: website output-dir: _site + render: + - "**/*.qmd" + - "!notes/" preview: port: 7779 diff --git a/docs/notes/data/datasets-for-dynamical-grn-modeling-urls.md b/docs/notes/data/datasets-for-dynamical-grn-modeling-urls.md new file mode 100644 index 0000000..056d1dd --- /dev/null +++ b/docs/notes/data/datasets-for-dynamical-grn-modeling-urls.md @@ -0,0 +1,429 @@ +# GRN Dataset Download URLs: A Complete Reference Guide + +Gene regulatory network (GRN) modeling benefits enormously from high-quality perturbation, multi-omic, and time-series datasets. This comprehensive guide provides **direct download URLs** for 20+ priority datasets across GEO, EBI, Zenodo, and specialty portals—organized for immediate programmatic access. + +## Time-series + perturbation datasets + +These datasets combine temporal dynamics with genetic or chemical perturbations, providing essential ground truth for causal GRN inference. + +### GSE213069 (RENGE) - Human iPSC TF knockouts + +**Primary accession:** https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE213069 + +| Resource | Direct URL | +|----------|------------| +| Supplementary files | `ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE213nnn/GSE213069/suppl/` | +| Series matrix | `ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE213nnn/GSE213069/matrix/` | +| SOFT format | `ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE213nnn/GSE213069/soft/` | +| GitHub (RENGE code) | https://github.com/masastat/RENGE | + +### ZSCAPE - Zebrafish perturbed embryos (Nature 2023) + +**GEO accession:** GSE202639 | **Portal:** https://cole-trapnell-lab.github.io/zscape/ + +**AWS S3 direct downloads (no authentication required):** +```bash +# Reference atlas (~1.25M cells) +wget http://trapnell-lab-s3-zscape.s3-website-us-west-2.amazonaws.com/zscape_reference_cds.RDS.gz + +# Raw count matrix +wget http://trapnell-lab-s3-zscape.s3-website-us-west-2.amazonaws.com/zscape_reference_raw_counts.mtx.gz + +# Cell metadata +wget http://trapnell-lab-s3-zscape.s3-website-us-west-2.amazonaws.com/zscape_reference_cell_metadata.csv.gz + +# Genetic perturbation atlas (2M cells, 23 perturbations) +wget http://trapnell-lab-s3-zscape.s3-website-us-west-2.amazonaws.com/zscape_perturb_cds.RDS +wget http://trapnell-lab-s3-zscape.s3-website-us-west-2.amazonaws.com/zscape_perturb_full_raw_counts.mtx.gz +wget http://trapnell-lab-s3-zscape.s3-website-us-west-2.amazonaws.com/zscape_perturb_full_cell_metadata.csv.gz + +# Temperature perturbation atlas +wget http://trapnell-lab-s3-zscape.s3-website-us-west-2.amazonaws.com/zscape_temp_cds.rds +``` + +**GitHub:** https://github.com/cole-trapnell-lab/sdg-zfish + +### Mesendoderm multilineage atlas (Scientific Data 2025) + +**Publication DOI:** https://doi.org/10.1038/s41467-025-56533-2 + +This dataset covers days 2-9 of human iPSC differentiation with **WNT/BMP4/VEGF perturbations** across 62,208 cells. Check the companion Scientific Data paper for specific GEO/Zenodo accessions in the Data Availability statement. + +--- + +## Multiome datasets (joint RNA + ATAC) + +Joint profiling of transcription and chromatin accessibility enables direct linking of regulatory elements to target genes. + +### GSE140203 (SHARE-seq mouse skin) + +**Primary accession:** https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE140203 + +| Resource | Direct URL | +|----------|------------| +| RAW data tarball (7.4 GB) | `ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE140nnn/GSE140203/suppl/GSE140203_RAW.tar` | +| Supplementary files | `ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE140nnn/GSE140203/suppl/` | +| SRA Run Selector | https://www.ncbi.nlm.nih.gov/Traces/study/?acc=PRJNA588784 | +| Pipeline (Broad) | https://github.com/broadinstitute/epi-SHARE-seq-pipeline | +| FigR tutorial | https://buenrostrolab.github.io/FigR/articles/FigR_shareseq.html | + +### GSE117089 (sci-CAR) - Dexamethasone time-course + +**Primary accession:** https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE117089 + +```bash +# Direct downloads +wget ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE117nnn/GSE117089/suppl/GSE117089_RAW.tar # 232.2 MB +wget ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE117nnn/GSE117089/suppl/GSE117089_README.txt +``` + +**GitHub:** https://github.com/JunyueC/sci-CAR_analysis +**Human Cell Atlas:** https://explore.data.humancellatlas.org/projects/0792db34-8047-4e62-802c-9177c9cd8e28 + +### Drosophila DEAP v2 (atlas.gs.washington.edu) + +**Portal:** https://atlas.gs.washington.edu/deap_v2/ +**Download resources:** https://shendure-web.gs.washington.edu/content/members/DEAP_website/public/ +**GitHub:** https://github.com/asddf123789/deap_website +**Original sci-ATAC-seq:** GEO GSE116240 + +### GSE194122 (NeurIPS 2021 BMMC) - Multimodal benchmark + +**Primary accession:** https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE194122 + +```bash +# Processed h5ad files (recommended) +wget "ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE194nnn/GSE194122/suppl/GSE194122_openproblems_neurips2021_cite_BMMC_processed.h5ad.gz" # 587 MB +wget "ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE194nnn/GSE194122/suppl/GSE194122_openproblems_neurips2021_multiome_BMMC_processed.h5ad.gz" # 2.7 GB +``` + +**AWS S3 (post-competition fragments):** +```bash +aws s3 sync s3://openproblems-bio/public/post_competition/multiome/ ./multiome_fragments/ +``` + +| Resource | URL | +|----------|-----| +| SRA BioProject | PRJNA799242 | +| Open Problems portal | https://openproblems.bio/events/2021-09_neurips/ | +| Publication | https://proceedings.mlr.press/v176/lance22a.html | + +### NeurIPS 2022 CD34+ Time-Course (Kaggle) - Time-series multiome + +**Primary access:** https://www.kaggle.com/competitions/open-problems-multimodal/data + +```bash +# Requires Kaggle API authentication +pip install kaggle +kaggle competitions download -c open-problems-multimodal +``` + +| Resource | URL | +|----------|-----| +| Competition page | https://www.kaggle.com/competitions/open-problems-multimodal | +| Open Problems portal | https://openproblems.bio/events/2022-08_neurips/ | +| Saturn Cloud notebooks | https://github.com/openproblems-bio/neurips_2022_saturn_notebooks | + +**Note:** The 2022 time-course data (~300K CD34+ cells, days 2/3/4/7/10) does not have a separate GEO accession—it is primarily distributed through Kaggle. Contains both CITE-seq and 10x Multiome from 4 donors. + +--- + +## Triple modality datasets + +These rare datasets capture RNA, chromatin accessibility, and DNA methylation simultaneously—critical for understanding epigenetic regulation. + +### GSE121708 (scNMT-seq gastrulation) + +**Primary accession:** https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE121708 + +| Resource | Direct URL | +|----------|------------| +| **EBI FTP (recommended)** | `ftp://ftp.ebi.ac.uk/pub/databases/scnmt_gastrulation` | +| GEO supplementary | `ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE121nnn/GSE121708/suppl/` | +| GitHub (main) | https://github.com/rargelaguet/scnmt_gastrulation | +| GitHub (EB analysis) | https://github.com/rargelaguet/scnmt_eb | + +**Bioconductor R access:** +```R +# SingleCellMultiModal package +BiocManager::install("SingleCellMultiModal") +library(SingleCellMultiModal) +scNMT("mouse_gastrulation", version = "2.0.0") +``` + +**Modalities:** RNA expression, CpG methylation, GpC accessibility (NOME-seq) + +### Paired-Tag mouse brain - scRNA + 5 histone marks + +**Portal:** http://catlas.org/pairedTag +**Allen Brain Map:** https://knowledge.brain-map.org/data/717LLMGF4TOCC860LH5 +**GitHub:** https://github.com/cxzhu/Paired-Tag +**NEMO Archive:** https://nemoanalytics.org (RRID:SCR_016152) + +Contains **64,849 nuclei** with H3K4me1, H3K4me3, H3K27ac, H3K27me3, H3K9me3 profiles from mouse frontal cortex and hippocampus. + +--- + +## Population genetics eQTL datasets + +Large-scale population studies link genetic variants to gene expression, providing statistical ground truth for regulatory relationships. + +### GSE196830 (OneK1K) - 982 donors, 1.27M PBMCs + +**Primary accession:** https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE196830 + +**AWS S3 eQTL summary statistics (public bucket):** +```bash +# All cell types +wget https://onek1k.s3.ap-southeast-2.amazonaws.com/eqtl/eqtl_table.tsv.gz +wget https://onek1k.s3.ap-southeast-2.amazonaws.com/esnp/esnp_table.tsv.gz + +# Cell-type specific (examples) +wget https://onek1k.s3.ap-southeast-2.amazonaws.com/eqtl/cd4nc_eqtl_table.tsv.gz +wget https://onek1k.s3.ap-southeast-2.amazonaws.com/eqtl/cd8nc_eqtl_table.tsv.gz +wget https://onek1k.s3.ap-southeast-2.amazonaws.com/eqtl/nk_eqtl_table.tsv.gz +wget https://onek1k.s3.ap-southeast-2.amazonaws.com/eqtl/monoc_eqtl_table.tsv.gz +``` + +**CZ CELLxGENE:** https://cellxgene.cziscience.com/collections/dde06e0f-ab3b-46be-96a2-a8082383c4a1 +**Zenodo (analysis code):** https://zenodo.org/records/5084396 +**Zenodo (SAIGE-QTL):** https://zenodo.org/records/10811106 +**GitHub:** https://github.com/powellgenomicslab/onek1k_phase1 + +**Python API access:** +```python +import cellxgene_census +census = cellxgene_census.open_soma() +``` + +### HipSci DA neurons (Jerber et al.) - 215 donors + +**ENA accession:** ERP121676 (open access) | **EGA:** EGAS00001002885 (managed) + +**Zenodo (DOI: 10.5281/zenodo.4333872):** +```bash +# Normalized counts by timepoint +wget "https://zenodo.org/records/4333872/files/D11.h5?download=1" -O D11.h5 +wget "https://zenodo.org/records/4333872/files/D30.h5?download=1" -O D30.h5 +wget "https://zenodo.org/records/4333872/files/D52.h5?download=1" -O D52.h5 + +# eQTL summary statistics +wget "https://zenodo.org/records/4333872/files/eqtl_summary_stats.tar.gz?download=1" + +# All files (39.2 GB) +wget "https://zenodo.org/api/records/4333872/files-archive" -O zenodo_4333872.zip +``` + +**GitHub:** https://github.com/single-cell-genetics/singlecell_neuroseq_paper/ + +### HipSci endoderm differentiation (Cuomo et al.) + +**ENA:** ERP016000 | **EGA:** EGAS00001002278 + +**Zenodo (DOI: 10.5281/zenodo.3625024):** +```bash +wget "https://zenodo.org/records/3625024/files/raw_counts.csv.zip?download=1" +wget "https://zenodo.org/records/3625024/files/log_normalised_counts.csv.zip?download=1" +wget "https://zenodo.org/records/3625024/files/cell_metadata_cols.tsv?download=1" +``` + +**HipSci FTP:** `ftp://ftp.hipsci.ebi.ac.uk/vol1/ftp` +**GitHub:** https://github.com/single-cell-genetics/singlecell_endodiff_paper + +--- + +## Genome-scale perturbation screens + +These massive CRISPR screens provide systematic perturbation data across thousands of genes. + +### Replogle Perturb-seq (gwps.wi.mit.edu) - 9,866 genes + +**Portal:** https://gwps.wi.mit.edu +**SRA BioProject:** PRJNA831566 + +**Figshare+ (159.71 GB total):** +```bash +# Download all files +wget "https://plus.figshare.com/ndownloader/articles/20029387/versions/1" -O replogle_all_files.zip +``` + +**scPerturb Zenodo (pre-harmonized h5ad):** +```bash +wget "https://zenodo.org/records/13350497/files/ReplogleWeissman2022_K562_essential.h5ad?download=1" # 1.5 GB +wget "https://zenodo.org/records/13350497/files/ReplogleWeissman2022_K562_gwps.h5ad?download=1" # 8.8 GB +wget "https://zenodo.org/records/13350497/files/ReplogleWeissman2022_rpe1.h5ad?download=1" # 1.2 GB +``` + +**GitHub:** https://github.com/josephreplogle/guide_calling + +### ERP165335 (HipSci CRISPRi) - 7,226 genes, 34 iPSC lines + +**ENA:** https://www.ebi.ac.uk/ena/browser/view/PRJEB81502 + +| Resource | URL | +|----------|-----| +| LFC tables | https://doi.org/10.6084/m9.figshare.26819743 | +| Count data | https://doi.org/10.6084/m9.figshare.27989294 | +| GitHub | https://github.com/claudiafeng123/crispri_scrnaseq_hipsci | +| Project page | https://www.sanger.ac.uk/tool/crispri-scrna-seq-hipsci/ | + +### Yeast genome-scale Perturb-seq (Nature Communications 2025) + +**Publication:** https://doi.org/10.1038/s41467-025-57600-4 + +Contains **4,162 mutant strains** from the RNA-barcoded YKOC with ~1 million cells profiled under control and stress (0.4M NaCl) conditions. The RNA-barcoded YKOC library is available upon request from the authors. Check the Data Availability section of the paper for specific GEO accession. + +### GSE139944 (sci-Plex) - 188 drugs, 650K cells + +**Primary accession:** https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE139944 + +```bash +# Processed data from GEO (9.2 GB) +wget ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE139nnn/GSE139944/suppl/GSE139944_RAW.tar + +# theislab h5ad version +wget https://ndownloader.figshare.com/files/33979517 -O sciplex_raw.h5ad +``` + +**GitHub:** https://github.com/cole-trapnell-lab/sci-plex + +--- + +## Lineage ground truth datasets + +Datasets with lineage tracing provide explicit cell fate relationships for validating trajectory inference. + +### Weinreb LARRY hematopoiesis + +**GitHub:** https://github.com/AllonKleinLab/paper-data +**GEO:** GSE140802 + +```bash +# In vitro data +wget "https://raw.githubusercontent.com/AllonKleinLab/paper-data/master/Lineage_tracing_on_transcriptional_landscapes_links_state_to_fate_during_differentiation/in_vitro/stateFate_inVitro_normed_counts.mtx.gz" +wget "https://raw.githubusercontent.com/AllonKleinLab/paper-data/master/Lineage_tracing_on_transcriptional_landscapes_links_state_to_fate_during_differentiation/in_vitro/stateFate_inVitro_cell_metadata.txt.gz" +wget "https://raw.githubusercontent.com/AllonKleinLab/paper-data/master/Lineage_tracing_on_transcriptional_landscapes_links_state_to_fate_during_differentiation/in_vitro/stateFate_inVitro_clone_matrix.mtx.gz" + +# In vivo data +wget "https://raw.githubusercontent.com/AllonKleinLab/paper-data/master/Lineage_tracing_on_transcriptional_landscapes_links_state_to_fate_during_differentiation/in_vivo/stateFate_inVivo_normed_counts.mtx.gz" +``` + +**scPerturb version:** +```bash +wget "https://zenodo.org/records/13350497/files/WeinrebKlein2020.h5ad?download=1" # 228.5 MB +``` + +### C. elegans embryogenesis (Zenodo) + +**DOI:** 10.5281/zenodo.15236812 | **GEO:** GSE126954 + +```bash +wget "https://zenodo.org/records/15236812/files/celegans_global_adata.h5ad?download=1" # 710.7 MB +``` + +**VisCello tool:** https://github.com/qinzhu/VisCello.celegans + +--- + +## Dense temporal datasets + +High temporal resolution captures regulatory dynamics during rapid developmental transitions. + +### E-MTAB-6967 (Pijuan-Sala mouse gastrulation) + +**ArrayExpress:** https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-6967 + +| Resource | URL | +|----------|-----| +| ArrayExpress FTP | `ftp://ftp.ebi.ac.uk/pub/databases/arrayexpress/data/experiment/MTAB/E-MTAB-6967/` | +| BioStudies FTP | `ftp://ftp.ebi.ac.uk/biostudies/fire/E-MTAB-/967/E-MTAB-6967/Files/` | +| GitHub (Marioni Lab) | https://github.com/MarioniLab/EmbryoTimecourse2018 | +| Interactive browser | https://marionilab.cruk.cam.ac.uk/MouseGastrulation2018/ | + +**Bioconductor:** +```R +BiocManager::install("MouseGastrulationData") +library(MouseGastrulationData) +``` + +**116,312 cells** from E6.5-E8.5 mouse embryos (9 timepoints). + +### iPSC cardiac differentiation (eLife 2023) + +**Publication:** https://doi.org/10.7554/eLife.80075 + +12 consecutive days of cardiac differentiation across 2 iPSC lines (WTC-11 and SCVI-111), with 27,595 cells. Check the Figure 4—source data and Data Availability section for GEO accessions. + +--- + +## Mouse Mutant Cell Atlas (MMCA) + +**Portal:** https://atlas.gs.washington.edu/mmca_v2/ +**Download page:** https://atlas.gs.washington.edu/mmca_v2/public/download.html +**GitHub:** https://github.com/shendurelab/MMCA/ + +Contains **1.67 million nuclei** from 103 E13.5 embryos across 22 mutant strains + 4 wild-type backgrounds. + +--- + +## scPerturb aggregation resource + +**Portal:** https://scperturb.org +**Zenodo DOI:** 10.5281/zenodo.13350497 (43.0 GB total, 53 h5ad files) + +```bash +# Download entire collection +wget "https://zenodo.org/api/records/13350497/files-archive" -O scperturb_all.zip + +# Individual datasets (examples) +wget "https://zenodo.org/records/13350497/files/NormanWeissman2019_filtered.h5ad?download=1" # 698.7 MB +wget "https://zenodo.org/records/13350497/files/SrivatsanTrapnell2020_sciplex3.h5ad?download=1" # 2.5 GB +wget "https://zenodo.org/records/13350497/files/JoungZhang2023_atlas.h5ad?download=1" # 5.8 GB +``` + +**scPerturb ATAC data (Zenodo 7058382):** +```bash +wget "https://zenodo.org/api/records/7058382/files-archive" -O scperturb_atac.zip # 6.9 GB +``` + +**GitHub:** https://github.com/sanderlab/scPerturb + +--- + +## Programmatic access patterns + +### GEO FTP structure +```bash +# General pattern +ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSEnnn/GSExxxxx/suppl/ +ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSEnnn/GSExxxxx/matrix/ + +# SRA Toolkit for raw reads +prefetch SRRxxxxxxx +fasterq-dump SRRxxxxxxx +``` + +### EBI FTP patterns +```bash +# ArrayExpress +ftp://ftp.ebi.ac.uk/pub/databases/arrayexpress/data/experiment/MTAB/E-MTAB-XXXX/ + +# ENA fastq +ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERRxxx/ERRxxxxxx/ +``` + +### Zenodo direct download +```bash +# Pattern: https://zenodo.org/records/{record_id}/files/{filename}?download=1 +# Archive: https://zenodo.org/api/records/{record_id}/files-archive +``` + +### Cloud resources + +**NCBI SRA on AWS:** Data accessible via SRA Toolkit with AWS backend +**CellxGene Census (AWS S3 us-west-2):** Python/R API access via `cellxgene-census` package +**OneK1K eQTL (AWS S3 ap-southeast-2):** Direct public bucket URLs listed above + +## Conclusion + +This reference consolidates **direct download URLs** for GRN-relevant datasets across major archives. Key takeaways: **Zenodo** provides the most reliable direct h5ad downloads for processed data; **scPerturb** serves as an excellent aggregator for harmonized perturbation datasets; **specialty portals** (ZSCAPE, MMCA, gwps.wi.mit.edu) often host processed data on AWS S3 with no authentication required. For raw sequencing data, GEO and ENA FTP servers remain the primary sources, accessible via standard `wget`/`curl` commands or dedicated tools like SRA Toolkit. \ No newline at end of file diff --git a/docs/notes/data/datasets-for-dynamical-grn-modeling.md b/docs/notes/data/datasets-for-dynamical-grn-modeling.md new file mode 100644 index 0000000..bd397f0 --- /dev/null +++ b/docs/notes/data/datasets-for-dynamical-grn-modeling.md @@ -0,0 +1,442 @@ +# Publicly available datasets for dynamical GRN modeling + +Single-cell datasets with perturbations, time-series data, and multimodal profiling are increasingly available for gene regulatory network inference. **Over 80 datasets** across GEO, Human Cell Atlas, ENCODE, and specialized portals meet the criteria for dynamical systems modeling analogous to GSE213069 (RENGE). The most valuable resources combine temporal sampling with either genetic perturbations or chromatin accessibility—enabling causal regulatory inference rather than correlational analysis alone. + +--- + +## Perturbation datasets: the gold standard for causal GRN inference + +Time-series perturbation experiments provide the strongest foundation for dynamical GRN modeling because they capture both regulatory topology and kinetics. + +### GSE213069 — RENGE time-series scCRISPR (reference dataset) +- **Organism/cells**: Human iPSCs (OILG-3 line) +- **Modalities**: scRNA-seq with CRISPR-Cas9 knockouts +- **Perturbations**: 23 transcription factors (POU5F1, SOX2, NANOG, PRDM14) +- **Time-series**: Days 2, 3, 4, 5 post-transduction (4 timepoints) +- **Scale**: ~5,000 cells/sample, ~100 cells/gRNA +- **Genetic variants**: No population-level variants +- **Publication**: Ishikawa et al., Communications Biology 2023 +- **GRN suitability**: Purpose-built for time-resolved GRN inference; directly benchmarks RENGE method + +### GSE90063 — Original Perturb-seq with temporal sampling +- **Organism/cells**: Mouse BMDCs; Human K562 +- **Modalities**: scRNA-seq with CRISPR knockouts +- **Perturbations**: 10–24 TFs per experiment +- **Time-series**: 0h, 3h post-LPS stimulation (BMDCs); 7d, 14d (K562) +- **Scale**: ~200,000 cells total +- **Genetic variants**: No +- **Publication**: Dixit et al., Cell 2016 +- **GRN suitability**: Foundational dataset for Perturb-seq; immune response dynamics with defined perturbations + +### Genome-scale Perturb-seq (Replogle et al.) +- **Accession**: Figshare/gwps.wi.mit.edu; GEO GSE168620 +- **Organism/cells**: Human K562, RPE1 +- **Modalities**: scRNA-seq with CRISPRi (dCas9-KRAB) +- **Perturbations**: **9,866 expressed genes** (genome-scale) +- **Time-series**: Single timepoint (day 6–8) +- **Scale**: **>2.5 million cells** +- **Genetic variants**: Cell line background +- **Publication**: Replogle et al., Cell 2022 +- **GRN suitability**: Comprehensive TF-target relationships; enables systematic network reconstruction at genome scale + +### GSE139944 — sci-Plex chemical transcriptomics +- **Organism/cells**: Human A549, K562, MCF7 cancer lines +- **Modalities**: scRNA-seq (sci-RNA-seq3) +- **Perturbations**: **188 small molecules** across ~5,000 conditions +- **Time-series**: 24h and 48h post-treatment +- **Scale**: ~650,000 cells +- **Genetic variants**: Cell line characterization +- **Publication**: Srivatsan et al., Science 2020 +- **GRN suitability**: Drug-gene regulatory relationships; dose-response modeling + +### GSE120861 — Gasperini enhancer CRISPRi screen +- **Organism/cells**: Human K562 +- **Modalities**: scRNA-seq with CRISPRi targeting enhancers +- **Perturbations**: **5,779 candidate enhancers**; 78,562 enhancer-gene pairs tested +- **Scale**: ~207,324 cells (high MOI) +- **Time-series**: No +- **Genetic variants**: No +- **Publication**: Gasperini et al., Cell 2019 +- **GRN suitability**: Direct enhancer-gene causal links; regulatory element validation + +### scPerturb harmonized database +- **URL**: https://scperturb.org +- **Content**: **44 harmonized perturbation datasets**, >2.5M cells +- **Modalities**: 32 CRISPR datasets, 9 drug datasets, 3 scATAC-seq +- **Publication**: Peidli et al., Nature Methods 2024 +- **GRN suitability**: Standardized format enables cross-dataset GRN comparison; unified preprocessing + +--- + +## Multiome datasets: chromatin accessibility enables regulatory inference + +Joint scRNA-seq + scATAC-seq profiling reveals which regulatory elements control gene expression in the same cells—critical for mechanistic GRN models. + +### GSE140203 — SHARE-seq mouse skin (hair follicle differentiation) +- **Organism/cells**: Mouse skin (keratinocytes, dermal papilla, melanocytes) +- **Modalities**: **scRNA-seq + scATAC-seq** (same cell) +- **Time-series**: Yes—hair follicle differentiation trajectory +- **Scale**: 34,774 joint profiles; 84,426 total cells +- **Genetic variants**: No +- **Publication**: Ma et al., Cell 2020 +- **GRN suitability**: **Outstanding**—introduces "chromatin potential" showing accessibility precedes expression; 63,110 peak-gene associations; Domains of Regulatory Chromatin (DORCs) overlap super-enhancers + +### GSE117089 — sci-CAR with dexamethasone time-course +- **Organism/cells**: Mouse kidney; Human A549 +- **Modalities**: **scRNA-seq + scATAC-seq** (combinatorial indexing) +- **Time-series**: **Yes**—A549 treated with dexamethasone at 0h, 1h, 3h +- **Scale**: ~16,000 cells with both modalities +- **Genetic variants**: No +- **Publication**: Cao et al., Science 2018 +- **GRN suitability**: **Excellent**—time-resolved chromatin-expression coupling; 2,613 DE genes linked to 4,763 differentially accessible sites + +### SNARE-seq mouse brain +- **Organism/cells**: Mouse neonatal and adult cerebral cortex +- **Modalities**: **scRNA-seq + scATAC-seq** (droplet-based) +- **Time-series**: Developmental pseudotime (neurogenesis) +- **Scale**: ~15,000 nuclei +- **Genetic variants**: No +- **Publication**: Chen et al., Nature Biotechnology 2019 +- **GRN suitability**: Cell-type-specific chromatin landscapes during neuronal differentiation + +### GSE109262/GSE121708 — scNMT-seq (triple modality) +- **Organism/cells**: Mouse ESCs; Mouse gastrulation +- **Modalities**: **scRNA-seq + chromatin accessibility + DNA methylation** +- **Time-series**: ESC differentiation; gastrulation stages +- **Scale**: ~150 cells (ESC); larger for gastrulation +- **Genetic variants**: No +- **Publication**: Clark et al., Nature Communications 2018; Argelaguet et al., Nature 2019 +- **GRN suitability**: **Exceptional**—three regulatory layers in same cells; reveals methylation-accessibility-transcription coupling dynamics + +### Paired-Tag mouse brain (5 histone marks) +- **Organism/cells**: Mouse frontal cortex and hippocampus +- **Modalities**: **scRNA-seq + H3K4me1/H3K4me3/H3K27ac/H3K27me3/H3K9me3** +- **Time-series**: Multiple developmental stages +- **Scale**: ~70,000 nuclei (64,849 QC-passed) +- **Genetic variants**: No +- **Publication**: Zhu et al., Nature Methods 2021 +- **GRN suitability**: Five histone marks reveal active vs. repressive states; comprehensive chromatin state + gene regulation + +### GSE207308 — SHARE-seq human bone marrow +- **Organism/cells**: Human BMMC +- **Modalities**: scRNA-seq + scATAC-seq +- **Time-series**: Differentiation trajectories inferred +- **Scale**: 78,520 cells; 51,862 genes; 173,026 chromatin regions +- **Genetic variants**: No +- **GRN suitability**: Large-scale human multiome; comprehensive enhancer-gene coverage + +### GSE194122 — NeurIPS 2021 BMMC multimodal atlas +- **Organism/cells**: Human bone marrow mononuclear cells (BMMCs) +- **Modalities**: **CITE-seq** (RNA + 134 proteins); **10x Multiome** (RNA + ATAC, joint) +- **Time-series**: No (cross-sectional) +- **Scale**: ~90,000 CITE-seq cells; ~69,249 Multiome cells +- **Donors**: 12 healthy donors across 4 sites +- **Genetic variants**: Limited (12 donors) +- **Publication**: Luecken et al., NeurIPS 2021 +- **GRN suitability**: Benchmarking standard for multimodal integration; joint RNA+ATAC enables enhancer-gene linkage; nested batch design tests robustness + +### NeurIPS 2022 CD34+ HSPC time-course (Kaggle) +- **Organism/cells**: Human CD34+ hematopoietic stem and progenitor cells +- **Modalities**: **CITE-seq** (RNA + protein); **10x Multiome** (RNA + ATAC) +- **Time-series**: **Days 2, 3, 4, 7, 10** (5 timepoints over 10-day differentiation) +- **Scale**: **~300,000 cells** +- **Donors**: 4 (IDs: 13176, 27678, 31800, 32606) +- **Cell types**: HSC, EryP, BP, NeuP, MkP, MasP, MoP +- **Genetic variants**: Limited (4 donors) +- **Publication**: Cellarity/Open Problems, NeurIPS 2022 +- **GRN suitability**: **Excellent**—rare time-series multiome; captures HSPC differentiation dynamics; test set from unseen timepoint enables temporal extrapolation validation + +### 10x Genomics Multiome datasets +- **PBMC reference**: ~10,000 cells, human immune types +- **Mouse brain aging (GSE294772)**: 2, 9, 18 months across 8 brain regions +- **Alzheimer's disease mouse model**: Disease progression time-course +- **Access**: 10x Genomics website and GEO +- **GRN suitability**: Commercial platform with high data quality; aging dataset provides **3-timepoint longitudinal data** + +--- + +## Time-series developmental and differentiation datasets + +Dense temporal sampling during biological processes enables dynamical systems approaches without requiring perturbations. + +### E-MTAB-6967 — Pijuan-Sala mouse gastrulation atlas +- **Organism/cells**: Mouse E6.5–E8.5 (extended to E9.5) +- **Modalities**: scRNA-seq; Tal1−/− chimeras for perturbation +- **Time-series**: **9 timepoints at 6-hour intervals** (extended: 13 timepoints) +- **Scale**: 116,312 cells (original); **430,339 cells** (extended) +- **Genetic variants**: Tal1 knockout chimeras included +- **Publication**: Pijuan-Sala et al., Nature 2019 +- **GRN suitability**: **Gold standard** for mouse development; Bioconductor package (MouseGastrulationData); highest temporal resolution available + +### Weinreb hematopoiesis with LARRY lineage tracing +- **Accession**: github.com/AllonKleinLab/paper-data +- **Organism/cells**: Mouse bone marrow HSPCs +- **Modalities**: scRNA-seq + clonal lineage barcodes +- **Time-series**: Days 2, 4, 6 in vitro + in vivo transplantation +- **Scale**: ~130,000 cells; 49,297 with barcodes; 5,864 unique clones +- **Genetic variants**: Clonal identity provides "natural perturbations" +- **Publication**: Weinreb et al., Science 2020 +- **GRN suitability**: **Benchmark standard** for fate prediction; ground-truth lineage enables validation; used for PRESCIENT dynamical modeling + +### GSE75748 — Human ESC to definitive endoderm +- **Organism/cells**: Human H1 ESC +- **Modalities**: scRNA-seq + matched bulk RNA-seq +- **Time-series**: **0h, 12h, 24h, 36h, 72h, 96h** (6 densely sampled points) +- **Scale**: 1,776 cells +- **Genetic variants**: No +- **Publication**: Chu et al., Genome Biology 2016 +- **GRN suitability**: Classic benchmark; includes Wave-Crest trajectory; regulators validated by CRISPR/Cas9 + +### iPSC cardiac differentiation (12 timepoints) +- **Accession**: eLife 2023 publication data +- **Organism/cells**: Human iPSC (WTC, SCVI-111 lines) +- **Modalities**: scRNA-seq + TBX5/MYL2 lineage reporters +- **Time-series**: **12 consecutive days** during cardiac differentiation +- **Scale**: 27,595 cells +- **Genetic variants**: Two iPSC lines +- **Publication**: eLife 2023 +- **GRN suitability**: Densest temporal sampling for cardiac differentiation; lineage tracing validates fate predictions + +### Mesendoderm multilineage atlas with perturbations +- **Accession**: Scientific Data 2025 +- **Organism/cells**: Human iPSC (WTC CRISPRi GCaMP) +- **Modalities**: scRNA-seq +- **Time-series**: Days 2–9 (8 consecutive days) +- **Perturbations**: **WNT, BMP4, VEGF modulators** +- **Scale**: Multiple conditions +- **GRN suitability**: **Excellent**—combines time-series with signaling perturbations; ideal for causal GRN inference + +### Human reprogramming with paired scATAC-seq +- **Accession**: Science Advances 2020 +- **Organism/cells**: Human BJ fibroblasts → iPSC +- **Modalities**: **scRNA-seq + scATAC-seq** at matched timepoints +- **Time-series**: Day 0, 2, 8, 16+, H1 ESC reference +- **Scale**: Fluidigm + 10X libraries +- **Genetic variants**: No +- **Publication**: Science Advances 2020 +- **GRN suitability**: Multi-omic time-series during reprogramming; identifies TF regulatory phasing + +### Mouse organogenesis (MOCA) +- **Organism/cells**: Mouse E9.5–E13.5 +- **Modalities**: scRNA-seq (sci-RNA-seq3) +- **Time-series**: E9.5, E10.5, E11.5, E12.5, E13.5 (5 stages) +- **Scale**: **~2 million cells** +- **Genetic variants**: No +- **Publication**: Cao et al., Nature 2019 +- **GRN suitability**: Massive scale covers critical organogenesis window; 38 major cell types + +--- + +## Datasets with genetic variant information + +Natural genetic variation or defined mutations enable causal inference by linking genotype to expression. + +### OneK1K — Population-scale single-cell eQTLs +- **Accession**: GSE196830; onek1k.org +- **Organism/cells**: Human PBMCs from **982 donors** +- **Modalities**: scRNA-seq +- **Time-series**: Dynamic eQTLs during B-cell naïve→memory transition +- **Scale**: **1.27 million cells**; 14 immune cell types +- **Genetic variants**: **26,597 independent cis-eQTLs**; 990 trans-eQTLs; full genotyping +- **Publication**: Yazar et al., Science 2022 +- **GRN suitability**: **Gold standard for sc-eQTL**; causal SNP→gene effects enable directional GRN edges; 38.4% of GWAS colocalizations are cell-type specific + +### HipSci dopaminergic neuron differentiation +- **Accession**: Nature Genetics 2021 +- **Organism/cells**: Human iPSCs from **215 donors** → DA neurons +- **Modalities**: scRNA-seq +- **Time-series**: Day 11 (progenitors), Day 30 (young neurons), Day 52 (mature) + rotenone stress +- **Scale**: **>1 million cells** +- **Genetic variants**: Full genotyping; stage-specific eQTLs; **1,284 eQTLs colocalize with neurological GWAS** +- **Perturbations**: Rotenone oxidative stress +- **Publication**: Jerber et al., Nature Genetics 2021 +- **GRN suitability**: **Outstanding**—time-series + population-scale genetics + stress perturbation; disease-relevant context + +### HipSci endoderm differentiation +- **Accession**: ArrayExpress, hipsci.org +- **Organism/cells**: Human iPSCs from **125 donors** → definitive endoderm +- **Modalities**: scRNA-seq +- **Time-series**: Day 0, Day 1, Day 3 +- **Scale**: ~37,000 cells +- **Genetic variants**: Full genotyping; 1,833 eGenes at iPSC stage; dynamic eQTLs +- **Publication**: Cuomo et al., Nature Communications 2020 +- **GRN suitability**: Dynamic eQTLs reveal temporal gene regulation; context-specific regulatory effects + +### HipSci CRISPRi screen with genetic backgrounds +- **Accession**: ERP165335; Sanger preprint 2024 +- **Organism/cells**: Human iPSCs from **26 donors** +- **Modalities**: scRNA-seq + CRISPRi +- **Perturbations**: **7,226 genes targeted** +- **Genetic variants**: Full genotyping enables genetic interaction studies +- **GRN suitability**: **Exceptional**—perturbation × genetic background = powerful causal inference; regulatory networks directly testable + +### CCLE pan-cancer single-cell collection +- **Accession**: DepMap Portal; Single Cell Portal SCP542 +- **Organism/cells**: Human, **198 cancer cell lines** from 22 tumor types +- **Modalities**: scRNA-seq +- **Time-series**: No +- **Scale**: ~53,500 cells +- **Genetic variants**: Full CCLE characterization (WES, WGS for 329 lines, CNV, mutations) +- **Publication**: Kinker et al., Nature Genetics 2020 +- **GRN suitability**: CNV, epigenetic variation, and ecDNA linked to expression heterogeneity; 12 recurrent programs identified + +### Asian Immune Diversity Atlas (AIDA) +- **Organism/cells**: Human PBMCs from **474 Asian donors** +- **Modalities**: scRNA-seq +- **Time-series**: 107 dynamic sQTLs identified +- **Scale**: ~1 million cells +- **Genetic variants**: **11,577 cis-sQTLs** (splicing); 607 trans-sGenes; ancestry-specific variants +- **Publication**: Nature Genetics 2024 +- **GRN suitability**: Splicing QTLs add post-transcriptional regulatory layer + +--- + +## Model organism datasets with comprehensive genetic characterization + +Model organisms offer complete genetic libraries and well-annotated genomes enabling systematic perturbation studies. + +### Yeast genome-scale Perturb-seq atlas +- **Organism/cells**: S. cerevisiae, log-phase +- **Modalities**: scRNA-seq with RNA-traceable clone barcodes +- **Perturbations**: **4,162 mutant strains** (82% of YKOC); ~3,500 mutants profiled +- **Time-series**: Control vs. osmotic stress (0.4M NaCl, 15 min) +- **Scale**: ~1,061,865 cells +- **Genetic variants**: Genome-scale deletion library +- **Publication**: Sameith et al., Nature Communications 2025 +- **GRN suitability**: **Outstanding**—systematic genotype-to-transcriptome mapping; causal inference for GRN construction + +### GSE136049 — CeNGEN (C. elegans neuronal atlas) +- **Organism/cells**: C. elegans nervous system +- **Modalities**: scRNA-seq + bulk RNA-seq (160 samples) + alternative splicing +- **Time-series**: L1→L4→Adult developmental stages +- **Scale**: 100,955 cells; 70,296 neurons; **128 neuronal classes** +- **Genetic variants**: Complete nervous system annotation; WormBase integration +- **Publication**: Taylor et al., Cell 2021 +- **GRN suitability**: Complete gene expression map of entire nervous system; links to known connectome; TF motif analysis (FIRE) + +### C. elegans embryogenesis lineage atlas +- **Accession**: Zenodo 10.5281/zenodo.15236812 +- **Organism/cells**: C. elegans embryo +- **Modalities**: scRNA-seq with lineage resolution +- **Time-series**: Fertilization through 500 minutes; **502 terminal cell types** +- **Scale**: 86,024 cells; 1,068 of 1,228 lineage branches covered +- **Genetic variants**: **Invariant cell lineage fully mapped** +- **Publication**: Packer et al., Science 2019 +- **GRN suitability**: **Gold standard** for lineage-coupled transcriptomics; multilineage priming at branch points; exact cell position known + +### Drosophila embryogenesis continuum (multiome) +- **Accession**: atlas.gs.washington.edu/deap_v2 +- **Organism/cells**: Drosophila embryo +- **Modalities**: **scRNA-seq + scATAC-seq** (matched samples) +- **Time-series**: **11 overlapping time windows** (0–20 hours); 2-hour resolution early +- **Scale**: ~1.5 million nuclei (~500K scRNA, ~1M scATAC) +- **Genetic variants**: Well-annotated dm6 genome +- **Publication**: Calderon et al., Science 2022 +- **GRN suitability**: **Excellent**—multi-omics enables direct enhancer-gene linkage; computed gene activity scores; TF motif activity + +### Zebrafish perturbed embryos atlas +- **Organism/cells**: Zebrafish embryos +- **Modalities**: scRNA-seq (sci-RNA-seq3) +- **Time-series**: **19 timepoints** (18 hpf–96 hpf); 2-hour resolution until 48 hpf +- **Scale**: ~3.2 million cells; 1,812 embryos +- **Genetic variants**: **23 defined genetic knockouts/perturbations** +- **Publication**: Saunders et al., Nature 2023 +- **GRN suitability**: **Excellent**—purpose-built for perturbation effects; individual embryo barcoding; cell type composition changes quantified + +### Zebrahub multimodal atlas +- **URL**: zebrahub.sf.czbiohub.org +- **Organism/cells**: Zebrafish (10 hpf–10 dpf) +- **Modalities**: scRNA-seq + epigenomics + **live light-sheet imaging** (lineage) +- **Time-series**: 10 developmental stages +- **Scale**: 120,000+ cells across 40 single-embryo transcriptomes +- **Genetic variants**: Individual embryo resolution +- **Publication**: CZ Biohub, Cell 2024 +- **GRN suitability**: Image-based lineage trees combined with transcriptomes; NMP pluripotency analysis + +### Mouse Mutant Cell Atlas (MMCA) +- **Organism/cells**: Mouse E13.5 whole embryos +- **Modalities**: scRNA-seq (sci-RNA-seq3) +- **Perturbations**: **22 defined mutations** (knockouts, knock-ins, enhancer deletions) + 4 WT strains +- **Scale**: ~1.6 million nuclei; 103 embryos +- **Genetic variants**: Defined genetic perturbations +- **Publication**: Cao et al., Nature 2023 +- **GRN suitability**: Systematic developmental disorder phenotyping; enhancer perturbations included + +### Diversity Outbred mouse scRNA-seq +- **Organism/cells**: Mouse bone marrow stromal cells +- **Modalities**: scRNA-seq with genetic variant integration +- **Scale**: 80 DO mice profiled +- **Genetic variants**: **>6 million sequence variants** (comparable to human GWAS); GigaMUGA genotyping +- **Publication**: Calabrese et al., JBMR/eLife 2023–2024 +- **GRN suitability**: Population-level scRNA-seq enables genetic effect estimation; BMD GWAS integration + +--- + +## ENCODE and regulatory reference datasets + +Gold-standard regulatory annotations complement scRNA-seq for mechanistic GRN modeling. + +### ENCODE single-cell resources +- **Portal**: encodeproject.org/single-cell +- **Data types**: snATAC-seq, scRNA-seq +- **Organism**: Human and mouse tissues +- **GRN application**: Cell-type-specific regulatory elements; gold-standard TF binding from ChIP-seq + +### Roadmap Epigenomics reference +- **URL**: egg2.wustl.edu/roadmap +- **Data types**: 15/25-state ChromHMM; 6 histone marks; DNase; methylation; RNA +- **Scale**: **127 reference epigenomes** across primary cells/tissues +- **Publication**: Nature 2015 +- **GRN application**: 2.3 million promoter/enhancer regions; cell-type-specific regulatory modules + +### 4D Nucleome Portal +- **URL**: data.4dnucleome.org +- **Data types**: Hi-C, sci-Hi-C, Micro-C, SPRITE, GAM +- **Scale**: >1,800 experiment sets; >19,000 single cells (sci-Hi-C) +- **GRN application**: Enhancer-promoter contacts; long-range chromatin interactions; TAD context + +### FANTOM5 enhancer atlas +- **URL**: fantom.gsc.riken.jp/5 +- **Data types**: CAGE for TSS/enhancer activity +- **Scale**: ~63,000 human enhancers; ~44,000 mouse enhancers +- **GRN application**: Active enhancer identification via eRNAs; promoter activity maps + +### JASPAR 2024 and HOCOMOCO v12 +- **URLs**: jaspar.elixir.no; hocomoco.autosome.org +- **Content**: 1,920+ TF binding profiles (JASPAR); 949 human + 720 mouse TF models (HOCOMOCO) +- **GRN application**: Essential for TF binding site prediction from ATAC-seq peaks + +--- + +## Benchmark datasets for GRN method validation + +### BEELINE benchmark +- **Zenodo**: doi.org/10.5281/zenodo.3378975 +- **GitHub**: github.com/Murali-group/Beeline +- **Content**: 6 synthetic networks; 4 Boolean models; 5 experimental scRNA-seq datasets (hHEP, hESC, mESC, mDC, mHSC) +- **Ground truth**: Cell-type-specific ChIP-seq networks +- **Publication**: Nature Methods 2020 + +### Dictys GRN pipeline datasets +- **Zenodo**: zenodo.org/records/6904917 +- **Content**: Human blood (separate scRNA+scATAC); Mouse skin (joint multiome); 10x Multiome examples + +--- + +## Summary: recommended priority datasets by use case + +| Use case | Top recommended datasets | +|----------|-------------------------| +| **Time-series + perturbation** | GSE213069 (RENGE), Mesendoderm atlas, Zebrafish perturbed embryos | +| **Multiome (scRNA+scATAC)** | SHARE-seq skin (GSE140203), sci-CAR (GSE117089), Drosophila embryo atlas, NeurIPS 2022 CD34+ time-course | +| **Triple modality** | scNMT-seq gastrulation (GSE121708), Paired-Tag brain | +| **Population-scale genetics** | OneK1K, HipSci DA neurons, HipSci endoderm | +| **Genome-scale perturbation** | Replogle Perturb-seq, HipSci CRISPRi, Yeast Perturb-seq atlas | +| **Lineage ground truth** | Weinreb hematopoiesis (LARRY), C. elegans embryo atlas | +| **Dense temporal sampling** | Pijuan-Sala gastrulation (E-MTAB-6967), iPSC cardiac (12 days), GSE75748 endoderm | +| **Model organism + genetics** | Yeast genome-scale Perturb-seq, Zebrafish perturbed embryos, Mouse MMCA | +| **Drug perturbations** | sci-Plex (GSE139944), MIX-Seq (DepMap) | +| **Regulatory element validation** | Gasperini enhancer CRISPRi (GSE120861), ENCODE ChIP-seq | +| **Method benchmarking** | BEELINE, Dictys datasets, NeurIPS 2021 BMMC (GSE194122) | + +The **scPerturb database** (scperturb.org) provides unified access to 44 harmonized perturbation datasets. For multiome integration, the **scMMO-atlas** database aggregates 70 high-quality multiome datasets with 3.2 million cells. CellxGene Census (cellxgene.cziscience.com) enables programmatic filtering across >93 million cells to identify additional developmental/differentiation datasets with the Python or R APIs. \ No newline at end of file diff --git a/docs/notes/omicsio/research/columnar-encoding-for-multimodal-single-cell-data.md b/docs/notes/omicsio/research/columnar-encoding-for-multimodal-single-cell-data.md new file mode 100644 index 0000000..cb2dfa1 --- /dev/null +++ b/docs/notes/omicsio/research/columnar-encoding-for-multimodal-single-cell-data.md @@ -0,0 +1,200 @@ +--- +title: Columnar encoding for multimodal single-cell data +status: under investigation +beads: data-a0y.1 +--- + +## Context + +omicsio converts AnnData and MuData objects from the scverse ecosystem into columnar table formats (parquet and vortex) for lakehouse storage and analytical querying via DuckDB. +The representation chosen for count and expression matrices is the central design decision of the library because it determines query performance, storage efficiency, downstream consumer compatibility, and the fidelity with which data can be reconstructed into its original in-memory form. + +Single-cell expression matrices are large, sparse, and structurally varied. +A typical RNA dataset contains 10k-1M cells with expression values across 20k-60k genes at 5-10% density. +Multimodal experiments add further complexity: splice-state quantification produces parallel count matrices for spliced, unspliced, and ambiguous transcripts; paired assays like 10x Multiome produce RNA and ATAC-seq data with entirely different feature spaces measured from the same cells. +MuData structures formalize this by organizing modalities with independent variable (feature) dimensions under a shared observation (cell) axis. + +Two existing implementations provide concrete starting points for this design space. +X-Atlas-Orion uses a denormalized list-column format optimized for DuckDB analytical queries and GPU batch streaming. +pqdata uses a directory-tree format mirroring the AnnData object graph, with sparse matrices decomposed to COO triplet columns. +Each approach makes different tradeoffs, and neither directly addresses multimodal data at the schema level. + +This document frames the two coupled questions that omicsio must resolve, surveys what is known from existing implementations and prior analysis, identifies the use cases against which any solution must be evaluated, and catalogs the open questions that remain. + +## Two coupled questions + +The first question concerns representation: how should multimodal count matrices be encoded in columnar table formats? +The encoding must work in both parquet and vortex, accommodate modalities with different feature dimensions (RNA genes vs ATAC peaks), and support efficient analytical queries without requiring reconstruction into dense matrices. +The X-Atlas-Orion list-column approach provides a strong foundation for single-modality RNA data, but extending it to splice-state layers and cross-modality structures introduces schema design choices that have not been explored in any existing implementation. + +The second question concerns rehydration: how should the lakehouse-native format support round-trip conversion back to AnnData and MuData when downstream tools require scverse objects? +The ideal data path for out-of-core analytics avoids materializing full AnnData objects entirely, flowing instead from columnar storage through Arrow record batches to dense or sparse arrays consumed directly by DuckDB, JAX, or PyTorch. +However, many tools in the scverse ecosystem accept only AnnData inputs, making reconstruction a practical necessity. +The rehydration path should be a compatibility layer rather than the primary access pattern, and its design should not constrain the columnar encoding choices. + +These questions are coupled because the columnar encoding determines what information is preserved and how efficiently it can be reassembled into scverse objects, while the rehydration requirements constrain which encodings are viable. + +## Known approaches + +### X-Atlas-Orion: list-column format + +X-Atlas-Orion encodes single-cell expression data as a denormalized cell-centric table where each row represents one cell. +Expression values are stored in two parallel list columns: `gene_token_id` (integer identifiers for genes with non-zero expression) and `gene_expression` (corresponding expression values). +A separate `gene_metadata.parquet` table maps integer token IDs to Ensembl IDs and gene names. +Cell-level metadata columns (barcode, sample, QC metrics, perturbation labels) sit alongside the list columns in the same row. + +This schema was designed for the Xaira Therapeutics Perturb-seq atlas (8M cells across two cell lines) and published as a HuggingFace dataset. +The original implementation uses `BIGINT[]` for token IDs and `DOUBLE[]` for expression values, though prior analysis in this repository recommends narrowing to `INT32[]` and `FLOAT32[]` respectively. + +The list-column format enables several query patterns directly in DuckDB. +Gene presence can be checked with `list_contains(gene_token_id, target_id)`, and expression values extracted with `list_position` for indexed lookup. +The X-Atlas-Orion repository includes a Wnt pathway query demonstrating multi-gene co-expression filtering with a `get_gene_expr` macro that extracts individual gene values from the parallel lists. +The tutorial code shows reconstruction back to AnnData by iterating rows, mapping token IDs to column indices, and assembling a CSR sparse matrix. + +The structural relationship between Arrow ListArray offsets and CSR indptr arrays is the key property enabling efficient conversion between the list-column format and sparse matrix representations. +Within a single non-chunked ListArray, the offsets array is structurally identical to CSR indptr, and the values array maps directly to CSR data and indices arrays. + +The format does not currently address multimodal data, splice-state layers, or any AnnData component beyond X and obs-level metadata. + +### pqdata: COO directory format + +pqdata serializes the full AnnData and MuData object graph as a directory tree of parquet files. +The directory structure mirrors the AnnData hierarchy: `X.parquet` for the expression matrix, `obs.parquet` and `var.parquet` for metadata dataframes, subdirectories for `obsm/`, `varm/`, `obsp/`, `varp/`, `layers/`, and `uns/`. +For MuData, a `mod/` subdirectory contains one nested AnnData directory per modality, with modality ordering preserved in a `pqdata.json` sidecar file. + +Sparse matrices are decomposed to COO format with three columns (`row`, `col`, `data`) in a single parquet file. +Matrix shape and original sparse class (CSR, CSC, COO) are stored as JSON in the Arrow schema metadata, enabling reconstruction to the original scipy sparse type on read. +Dense matrices are stored with columns named after variable names (gene names as parquet column names). + +pqdata provides a `Group` and `GroupAccessor` class hierarchy that presents an HDF5/Zarr-like interface over the directory tree, supporting `fsspec` for remote filesystem access. +The read path detects COO format by checking for `row`, `col`, `data` column names and reconstructs sparse matrices using `scipy.sparse.coo_matrix`. +MuData is reconstructed via `MuData._init_from_dict_()` from the nested directory structure. + +The COO format preserves lossless round-trip fidelity for all AnnData and MuData components including layers, pairwise matrices, unstructured metadata, and raw data. +However, the COO decomposition is not optimized for analytical queries: querying expression values for specific genes requires scanning the full COO table and filtering on the `col` column, with no columnar projection benefit. + +### Comparative assessment from prior analysis + +The architecture document in this repository (`single-cell-data-architecture-for-queries-and-training.md`) concludes that the X-Atlas-Orion list-column pattern is the superior foundation for queryable expression data across three constraints: array query efficiency, network-scalable queries, and storage efficiency. +pqdata retains advantages for lossless AnnData round-tripping and unstructured metadata components (uns, obsp/varp) that do not benefit from columnar query patterns. + +The recommended refinements over raw X-Atlas-Orion include narrower integer and float types, sorted token ID lists as a schema invariant for improved compression, biologically meaningful partitioning, inline fixed-size list columns for embeddings, and separate tables per modality with a shared cell ID key for multimodal data. +The architecture document provides a DDL sketch with `rna_cells`, `rna_layers_spliced`, `rna_layers_unspliced`, `rna_layers_ambiguous`, `atac_cells`, and `gene_cell_index` tables. + +## Use cases to evaluate against + +Any encoding scheme must support the following data configurations, which represent the range of structures omicsio will encounter in practice. + +Transcriptomic data with total RNA is the simplest case: a single expression matrix (X) with one count per cell-gene pair. +This is what X-Atlas-Orion directly addresses and what most scverse tutorials assume. + +RNA separated by spliced and unspliced counts appears in RNA velocity workflows. +scvelo and pyrovelocity expect `adata.layers["spliced"]` and `adata.layers["unspliced"]` as sparse matrices with the same shape as X. +The spliced and unspliced matrices share the same feature space (genes) but have different sparsity patterns and different values. + +RNA separated by spliced, unspliced, and ambiguous counts is the three-layer variant produced by STARsolo, kallisto-bustools, and the binseq pipeline described in the companion processing document. +The ambiguous layer captures reads that cannot be definitively assigned to a splice state. + +Multimodal data with ATAC-seq from 10x Multiome format pairs RNA expression with chromatin accessibility measurements from the same cells. +The RNA and ATAC modalities have different feature dimensions (genes vs peaks), different sparsity characteristics, and different value semantics (UMI counts vs binary accessibility or fragment counts). +In AnnData terms, these are stored as separate AnnData objects within a MuData container, sharing the same observation axis but with independent variable axes. + +MuData structures with modalities of different feature dimensions generalize the multiome case to arbitrary combinations of assays. +CITE-seq pairs RNA with protein measurements (typically 100-300 surface proteins). +Spatial transcriptomics may pair gene expression with spatial coordinates and morphological features. +The key structural property is that modalities share cells but have independently sized feature spaces. + +## Downstream consumer constraints + +Three downstream projects in the ecosystem consume single-cell data, and their format expectations constrain what omicsio must support. + +pyrovelocity implements probabilistic RNA velocity models using PyTorch and Pyro. +It loads data exclusively as AnnData objects from h5ad files, using `scanpy.read()` with sparse matrix support. +The preprocessing pipeline accesses `adata.layers["spliced"]` and `adata.layers["unspliced"]` (and copies to `raw_spliced` / `raw_unspliced`), computes library sizes from these layers, and expects scipy sparse matrices. +There is no support for parquet, vortex, or DuckDB-based data loading. +pyrovelocity requires full AnnData rehydration for any data delivered via omicsio. + +stormi implements stochastic differential equation models for gene regulatory network inference using JAX and NumPyro. +It operates on AnnData objects for preprocessing, expecting separate RNA and ATAC AnnData objects as inputs. +The preprocessing pipeline converts expression matrices to CSR format (`csr_matrix(adata.X)`), performs metacell aggregation, and produces dense numpy arrays as model inputs. +The training loop consumes dense numpy arrays sliced by a cell minibatch loader, not AnnData objects directly. +stormi's data path is AnnData for preprocessing, then dense arrays for training, suggesting that a DuckDB-to-Arrow-to-dense path could bypass AnnData for the training phase if the preprocessing steps were adapted. + +hodosome is a placeholder project for stochastic dynamical modeling of gene regulatory networks. +It depends on pyrovelocity and stormi for modeling algorithms and outputs DuckDB data for visualization. +No data loading code exists yet, so its format constraints are inherited from its dependencies and from the DuckDB output requirement. +hodosome represents an opportunity to design data loading against the lakehouse-native format from the start rather than inheriting AnnData assumptions. + +Across all three consumers, the common pattern is that preprocessing requires AnnData with layers (particularly spliced/unspliced for velocity), while training consumes dense or sparse numpy/JAX arrays. +The rehydration requirement is driven primarily by preprocessing compatibility with scverse tools (scanpy, scvelo, scvi-tools) rather than by the modeling frameworks themselves. + +## Open questions + +Several questions remain unresolved and would benefit from experimental validation or further analysis. + +How should splice-state layers relate to the primary expression table? +The DDL sketch in the architecture document uses separate tables per layer (`rna_layers_spliced`, `rna_layers_unspliced`, `rna_layers_ambiguous`), each with their own `cell_id`, `gene_token_id`, and `gene_expression` columns. +An alternative is to add additional list columns to the primary cell table (`spliced_token_id`, `spliced_expression`, `unspliced_token_id`, `unspliced_expression`), keeping all per-cell data in a single row. +The separate-table approach avoids wide rows and allows layers to be queried independently, but requires joins for velocity workflows that need spliced and unspliced counts together. +The inline approach keeps related data co-located for joint queries but produces wider rows with more list columns. +The tradeoff depends on typical query patterns and on how DuckDB handles joins versus wide list-column rows at scale. + +How should multimodal data be organized across tables? +The architecture document recommends separate tables per modality with a shared `cell_id` key. +This aligns with the MuData model where modalities have independent feature spaces. +However, it raises questions about how cross-modality queries perform, how metadata shared across modalities (cell-level annotations) should be stored (duplicated per table or in a shared obs table), and whether the DuckLake catalog should represent each modality as a separate table or group them under a dataset-level namespace. + +What is the performance difference between COO and list-column formats for reconstruction? +The list-column format maps efficiently to CSR via Arrow ListArray offsets, but reconstruction requires a token-to-column-index mapping step. +COO format maps directly to `scipy.sparse.coo_matrix` but then requires conversion to CSR for most downstream operations. +Benchmarking both reconstruction paths at realistic scale (100k-1M cells, 20k-60k genes) would quantify whether the format choice materially affects rehydration performance. + +How does vortex encoding interact with list-column schemas? +Sorted integer token ID lists should trigger FastLanes delta encoding in vortex, and float expression lists should get ALP encoding. +However, the interaction between vortex's encoding selection and nested list types has not been empirically validated. +Benchmarks comparing vortex vs parquet storage size and query latency for list-column expression data would inform whether vortex's theoretical advantages materialize for this specific data shape. + +Can rehydration be lazy rather than eager? +Rather than reconstructing a full AnnData object in memory, a lazy proxy could back AnnData's `.X`, `.layers`, `.obs`, and `.var` properties with DuckDB queries that materialize data on access. +This would enable scverse tool compatibility without full materialization, potentially supporting out-of-core workflows where the full dataset exceeds host RAM. +The feasibility depends on which AnnData operations downstream tools actually invoke and whether the anndata library's internal assumptions permit lazy backing stores. + +What token ID assignment strategy should omicsio use? +X-Atlas-Orion assigns integer token IDs derived from its specific gene reference (GRCh38 2024-A). +For a general-purpose tool, token IDs could be assigned per-dataset (simple sequential), per-organism-reference (stable across datasets using the same reference), or via a global registry. +The choice affects whether token IDs are meaningful across datasets and whether the gene metadata table needs versioning. + +How should embeddings and dimensionality reductions be handled? +The architecture document recommends inline fixed-size list columns for embeddings (e.g., `umap FLOAT[2]`, `pca FLOAT[50]`). +This works for standardized reductions but becomes unwieldy if datasets carry many different embeddings or if embedding dimensions vary. +An alternative is a separate `obsm` table with embedding name as a key column. + +## References + +### Source implementations + +- X-Atlas-Orion dataset and schema: `~/projects/omicslake-workspace/X-Atlas-Orion/` +- X-Atlas-Orion query examples: `~/projects/omicslake-workspace/X-Atlas-Orion/queries/wnt-pathway-query.sql` +- X-Atlas-Orion AnnData reconstruction tutorial: `~/projects/omicslake-workspace/X-Atlas-Orion/tutorials/filter_convert_to_anndata.py` +- pqdata library: `~/projects/omicslake-workspace/pqdata/` +- pqdata COO write implementation: `~/projects/omicslake-workspace/pqdata/src/pqdata/io/write.py` +- pqdata COO read implementation: `~/projects/omicslake-workspace/pqdata/src/pqdata/io/read.py` + +### Design documents in this repository + +- Single-cell data architecture for queries and training: `docs/notes/omicsio/single-cell-data-architecture-for-queries-and-training.md` +- Single-cell processing pipeline (binseq to count matrices): `docs/notes/omicsio/single-cell-processing-pipeline-binseq-to-count-matrices.md` + +### Context documents + +- omicsio architecture context: `~/projects/sciexp/planning/contexts/omicsio.md` +- sciexp data platform context: `CLAUDE.md` (repository root) + +### Downstream consumers + +- pyrovelocity data loading: `~/projects/pyrovelocity-workspace/pyrovelocity/src/pyrovelocity/io/datasets.py` +- pyrovelocity preprocessing (splice-state layer access): `~/projects/pyrovelocity-workspace/pyrovelocity/src/pyrovelocity/tasks/preprocess.py` +- stormi preprocessing pipeline: `~/projects/pyrovelocity-workspace/stormi-review/stormi/packages/stormi/src/stormi/preprocessing/_pipeline.py` +- stormi multimodal embedding: `~/projects/pyrovelocity-workspace/stormi-review/stormi/packages/stormi/src/stormi/preprocessing/_embedding.py` +- stormi training data loader: `~/projects/pyrovelocity-workspace/stormi-review/stormi/packages/stormi/src/stormi/train.py` +- hodosome (placeholder): `~/projects/hodosome-workspace/hodosome/` diff --git a/docs/notes/omicsio/single-cell-data-architecture-for-queries-and-training.md b/docs/notes/omicsio/single-cell-data-architecture-for-queries-and-training.md new file mode 100644 index 0000000..c259e0a --- /dev/null +++ b/docs/notes/omicsio/single-cell-data-architecture-for-queries-and-training.md @@ -0,0 +1,242 @@ +--- +title: Single-cell data architecture for network queries and JAX GPU training +--- + +## Context + +This document synthesizes conclusions from comparative schema analysis (pqdata vs X-Atlas-Orion list-column format), JAX GPU data loading landscape research (Grain, ArrayRecord, DLPack, DuckDB streaming), and architectural design for the omicsio pipeline. +The analysis covers two distinct data regimes: full-transcriptome (60k genes, low density) and focused-gene (2k genes, moderate density). + +The upstream processing pipeline (binseq through splice-state count matrices) is documented in the companion document `single-cell-processing-pipeline-binseq-to-count-matrices.md`. +This document begins at the Arrow in-memory boundary where that pipeline ends. + +Source conversation transcripts are archived alongside this document: +- `pqdata-vs-xatlas-orion-schema-comparison.md` +- `jax-gpu-data-loading-parquet-vortex-sparse-schema-analysis.md` +- `binseq-minimap2-splice-pipeline-design.md` (pending archival) + +## Schema comparison: pqdata vs X-Atlas-Orion + +pqdata mirrors the AnnData object graph as a directory tree of parquet files (X.parquet stores expression as COO triplets or dense gene-named columns, with separate obs.parquet, var.parquet, and subdirectories for obsm, layers, obsp, uns). +X-Atlas-Orion uses a denormalized cell-centric table where each row carries inline parallel list columns `gene_token_id BIGINT[]` and `gene_expression DOUBLE[]` encoding only non-zero entries, with a separate `gene_metadata.parquet` lookup table mapping integer tokens to ensembl IDs and gene names. + +The X-Atlas-Orion list-column pattern is the superior foundation for queryable expression data across three constraints: array query efficiency, network-scalable queries, and storage efficiency. +pqdata retains advantages for lossless AnnData round-tripping and unstructured metadata components (uns, obsp/varp) that do not benefit from columnar query patterns. + +### Recommended refinements over raw X-Atlas-Orion + +The following refinements improve on X-Atlas-Orion's original schema: + +- Use INT32 rather than BIGINT for token IDs (40k genes fit in int32, halves storage and improves compression via narrower bitpacking). +- Use FLOAT32 rather than DOUBLE for expression values (pre-normalization counts can be INT32 or INT16). +- Guarantee sorted token ID lists as a schema invariant (enables binary search semantics and dramatically improves delta-encoding compression). +- Partition by biologically meaningful units (dataset, tissue, organism) rather than arbitrary batches, enabling partition pruning for cross-study queries. +- Store embeddings as fixed-size list columns inline (e.g., `umap FLOAT[2]`, `pca FLOAT[50]`). +- Use separate tables per modality with a shared cell_id key for multimodal (MuData-like) data, preserving modality-specific feature spaces while enabling cross-modality joins. +- Target vortex as the storage format where supported (sorted INT32 token ID lists trigger FastLanes delta compression automatically, expression FLOAT32 lists get ALP compression, and random access is approximately 100x faster than parquet). + +### Corrections to prior analysis + +The claim that Arrow ListArray offsets are "structurally identical to CSR indptr" is correct within a single non-chunked ListArray but requires care in three scenarios. + +First, chunked arrays: DuckDB `fetch_record_batch()` returns individual RecordBatch objects with contiguous offsets starting from 0, avoiding the problem. +Using `fetchall()` or `arrow()` on large results may return a ChunkedArray where each chunk has per-chunk offsets — concatenating naively produces invalid indptr. +The fix is to iterate chunks and accumulate offsets. + +Second, null handling: a null list entry produces `offsets[i] == offsets[i+1]` (empty range), identical to a zero-length list. +The validity bitmap distinguishes them but `.offsets` does not carry it. +For CSR construction this is acceptable when nulls mean "no expressed genes." + +Third, dtype alignment: Arrow ListArray offsets are int32 by default, matching SciPy `csr_matrix` expectations when gene counts and batch sizes fit in int32 (as they do for our use case). + +The `list_contains` + `list_position` query pattern performs O(k) linear scans per cell (where k is the number of expressed genes, typically 2k-8k). +DuckDB does not exploit sort order for `list_contains` even on sorted lists. +At 100M cells with multiple gene lookups per row, this produces trillions of comparisons. +Mitigations include pre-filtering with metadata columns, materializing boolean gene-set membership columns at ingestion, and maintaining an inverted index table (`gene_token_id INT32, cell_rowids LIST`) for gene-centric queries at scale. + +## Schema DDL + +```sql +CREATE TABLE gene_metadata ( + gene_token_id INT32 PRIMARY KEY, + ensembl_id VARCHAR NOT NULL, + gene_name VARCHAR NOT NULL, + chromosome VARCHAR, + gene_type VARCHAR +); + +CREATE TABLE rna_cells ( + cell_id VARCHAR NOT NULL, + gene_token_id LIST(INT32) NOT NULL, -- sorted ascending invariant + gene_expression LIST(FLOAT32) NOT NULL, -- parallel to gene_token_id + cell_type VARCHAR, + tissue VARCHAR, + organism VARCHAR, + dataset VARCHAR NOT NULL, + batch VARCHAR, + umap FLOAT[2], + n_genes INT32, + total_counts FLOAT, + CHECK (len(gene_token_id) = len(gene_expression)) +); + +CREATE TABLE rna_layers_spliced ( + cell_id VARCHAR NOT NULL, + gene_token_id LIST(INT32) NOT NULL, + gene_expression LIST(FLOAT32) NOT NULL +); + +CREATE TABLE rna_layers_unspliced ( + cell_id VARCHAR NOT NULL, + gene_token_id LIST(INT32) NOT NULL, + gene_expression LIST(FLOAT32) NOT NULL +); + +CREATE TABLE rna_layers_ambiguous ( + cell_id VARCHAR NOT NULL, + gene_token_id LIST(INT32) NOT NULL, + gene_expression LIST(FLOAT32) NOT NULL +); + +CREATE TABLE atac_cells ( + cell_id VARCHAR NOT NULL, + peak_token_id LIST(INT32) NOT NULL, + peak_accessibility LIST(FLOAT32) NOT NULL, + dataset VARCHAR NOT NULL +); + +CREATE TABLE gene_cell_index ( + gene_token_id INT32 NOT NULL, + dataset VARCHAR NOT NULL, + cell_rowids LIST(INT64) NOT NULL +); +``` + +## Network query architecture + +DuckDB's httpfs extension provides first-class support for S3-compatible storage and HuggingFace paths (`hf://datasets///` with glob support, branch references via `@` syntax, and authentication via DuckDB Secrets Manager). +The vortex-duckdb extension supports S3 remote storage via the Rust `object_store` crate with range requests, retaining vortex's random access and compression advantages over the network. + +The gene metadata table is small (~60k rows, ~2 MB) and should be cached locally on first access rather than embedded in each partition. + +### Progressive refinement query pattern + +The pipeline minimizes bytes transferred at each stage. + +Stage 1 (metadata scan, ~KB transferred): query scalar metadata columns to identify cells of interest via column projection. + +Stage 2 (cell filtering with gene presence, ~MB transferred): check gene presence using `list_contains` on the `gene_token_id` column without extracting expression values. + +Stage 3 (expression extraction for gene subset, ~10s of MB transferred): extract expression values for a small gene panel using parallel list indexing. + +Stage 4 (batch materialization, ~100s of MB transferred): stream full expression vectors using `fetch_record_batch()` for training data preparation. + +### DuckLake catalog role + +DuckLake provides snapshot-based versioning, schema evolution tracking, and file-level metadata per table. +Partitioning is achieved through table organization and file layout rather than explicit partition management APIs. +Create one DuckLake table per modality with files partitioned by dataset, tissue, and organism. + +## JAX GPU data loading + +### The two data regimes + +The full-transcriptome regime (60k genes, 5% density, large cell counts) and focused-gene regime (2k genes, 33% density) have different optimal architectures because the memory profiles differ by orders of magnitude. + +For full-transcriptome: dense full dataset at 1M cells = 240 GB (exceeds host RAM for large cell counts). +Batch streaming from DuckDB with Arrow ListArray zero-copy extraction and numba parallel scatter to dense is the recommended path. +Each batch of 1024 cells produces a 245 MB dense array. + +For focused-gene: dense full dataset at 8M cells × 2k genes = 64 GB (fits in host RAM). +Pre-loading the entire dataset as a dense NumPy array, shuffling with `np.random.permutation`, and streaming batches via `jax.device_put` with sharding is simpler and equally performant. +At 33% density with only 2k genes, dense parquet with gene columns is a competitive storage format — column projection works directly, no scatter reconstruction needed, and parquet's RLE encoding handles zeros well. + +### Arrow ListArray to dense reconstruction + +The core conversion path from list-column format to dense arrays: + +``` +DuckDB fetch_record_batch(batch_size) + → Arrow RecordBatch (ListArray columns) + → .offsets.to_numpy() + .values.to_numpy() (zero-copy views) + → numba parallel scatter to dense np.ndarray + → jax.device_put() with sharding +``` + +The numba scatter function operates in parallel across rows using `numba.prange` and produces a `(batch_size, num_genes)` float32 array directly. +For gene-subset queries, a variant scatter function accepts a `gene_to_col` mapping array and produces a `(batch_size, panel_size)` dense matrix, filtering during scatter rather than as a separate step. + +### Grain and ArrayRecord + +Grain's Parquet support (`grain.experimental.ParquetIterDataset`) is sequential-access only with no RandomAccessDataSource implementation, no global shuffle capability, and degraded LIST column handling (PyArrow `to_numpy()` on LIST columns produces Python object arrays). +These limitations are architectural rather than implementation gaps. + +For full Grain feature utilization (DataLoader, global shuffling, deterministic replay, Orbax checkpointing), converting to ArrayRecord is required. +ArrayRecord provides O(1) index-based access via a footer index, batch read performance of 310-490K QPS, and is the format recommended by the JAX ecosystem for production training. + +### Dual format strategy + +Maintain both vortex (with list-column schema) and ArrayRecord in S3 storage. +These formats serve non-overlapping access patterns: vortex is columnar and query-optimized for analytics and exploration; ArrayRecord is record-oriented with O(1) random access for ML training with global shuffle, deterministic replay, and checkpoint/resume. + +Store only the count matrix in ArrayRecord, not metadata, embeddings, or gene annotations (those belong exclusively in vortex, accessed via DuckDB). +For the 2k-gene regime, store pre-scattered dense rows (8 KB per record). +For the 60k-gene regime, store sparse list-column data and scatter at training time. + +The materialization pipeline runs once per dataset ingestion as a post-processing step after vortex/parquet files are written, decoupled from the primary ingestion pipeline. + +### When ArrayRecord is unnecessary + +For datasets that fit in host RAM (the 8M × 2k = 64 GB case), pre-loading from vortex via DuckDB, shuffling in host memory, and streaming batches to GPU is simpler and performs well without Grain. + +The crossover where ArrayRecord becomes worth maintaining is approximately when the dataset exceeds host RAM, training runs are long enough that preemption recovery matters, multi-host distributed training requires coordinated sharding, or reproducibility requirements demand bit-exact replay. + +## Multi-GPU training architecture + +### Equinox and JAX sharding + +The primary training framework is JAX with Equinox (functional semantics) and optax for optimization, implementing amortized simulation-based Bayesian inference. +NumPyro serves as a comparison framework for smaller models. + +For 2× A100 40GB: data-parallel training with batch sharding across devices. +Batch of 8192 cells (4096 per device, 32 MB each) leaves ~39 GB per device for model and optimizer state. +Host-to-device transfer of 64 MB total batch takes ~2.5 ms at PCIe 4.0. + +For 8× H100 80GB: the full 64 GB dataset can be sharded across devices (8 GB each), eliminating host-to-device transfer during training. +Use `jax.device_put(data, NamedSharding(mesh, PartitionSpec("data")))` at initialization. +With 65536-cell batches (8192 per device), training completes ~122 steps per epoch. + +### Memory budget + +| Configuration | Dense batch (4096 cells × 2k) | Full dataset (8M × 2k) | +| --- | --- | --- | +| Per device | 32 MB | 8 GB (sharded across 8 H100s) | +| Host RAM | 64 GB (pre-loaded) | 64 GB | +| Double-buffered prefetch | 64 MB CPU + 32 MB GPU | N/A (on-GPU) | + +For 60k-gene batches: 1024 cells = 245 MB, 4096 cells = 983 MB per device. + +## Ingestion pipeline output formats + +The Rust-based processing pipeline is documented in `single-cell-processing-pipeline-binseq-to-count-matrices.md`. +In summary: SRA archives and FASTQ files are converted to binseq CBQ format, aligned with minimap2-rs in splice-aware mode, classified into splice states (spliced/unspliced/ambiguous) via CIGAR-based annotation overlap ported from precellar, and deduplicated at the UMI level. +The pipeline produces Arrow `RecordBatch` values in the list-column schema, which are then written to three output formats: + +- Vortex files in pqdata directory layout: primary queryable format for analytics and exploration, with separate layer files for each splice state. +- Parquet files via duckdb-rs: compatibility format for tools without vortex support, benefiting from DuckDB's aggressive dictionary/RLE/zstd encoding. +- ArrayRecord with dense or sparse rows: training-optimized format, materialized as a separate post-processing step. + +Arrow IPC serves as an additional in-process hand-off mechanism for downstream Rust or Python consumers without file format overhead. + +For the 2k-gene regime, dense parquet with gene columns is an additional viable storage option since 2k columns is within parquet's comfortable range and column projection provides direct gene-level access without list functions. + +## Summary of recommended architectures + +For full-transcriptome data (60k genes, low density): vortex list-column schema, DuckDB streaming with Arrow ListArray extraction and numba scatter, batch streaming to GPU. +ArrayRecord for production training. + +For focused-gene data (2k genes, moderate density): dense parquet with gene columns or vortex list-column (either works), pre-load to host RAM, shard across GPUs with JAX. +ArrayRecord when dataset exceeds host RAM or training requires deterministic replay. + +The DuckLake catalog and gene_metadata table are shared infrastructure across both regimes. +The ingestion pipeline supports both output schemas from the same source data, selecting the appropriate format based on downstream use case. diff --git a/docs/notes/omicsio/single-cell-processing-pipeline-binseq-to-count-matrices.md b/docs/notes/omicsio/single-cell-processing-pipeline-binseq-to-count-matrices.md new file mode 100644 index 0000000..292b0e8 --- /dev/null +++ b/docs/notes/omicsio/single-cell-processing-pipeline-binseq-to-count-matrices.md @@ -0,0 +1,166 @@ +--- +title: "Single-cell processing pipeline: binseq to splice-state count matrices" +--- + +## Context + +This document describes the upstream processing pipeline that produces the count matrices consumed by the query and training architecture documented in `single-cell-data-architecture-for-queries-and-training.md`. +That companion document covers everything downstream of the Arrow in-memory representation: schema design, network query patterns, JAX GPU data loading, and multi-GPU training architecture. +This document covers everything upstream: how raw sequencing data (SRA archives or FASTQ files) becomes splice-state-annotated gene-by-cell count matrices in vortex and parquet formats, with no FASTQ text or HDF5/H5AD intermediaries in the processing path. + +Source conversation transcript is archived alongside this document: +- `binseq-minimap2-splice-pipeline-design.md` (pending archival) + +## Design principles + +The pipeline is built on three constraints that shape every technology choice. + +First, no FASTQ text format in the processing pipeline. +FASTQ is an ASCII-encoded, line-oriented format that requires gzip decompression and text parsing before any useful work can happen. +The binseq CBQ format replaces this with 2-bit nucleotide encoding (32 bases per 64-bit word), memory-mapped zero-copy access, and block-based parallel processing. +The only point where ASCII nucleotide encoding appears is at the minimap2 C FFI boundary, where a trivial bit-unpack produces the byte sequences the aligner expects. +This is orders of magnitude cheaper than FASTQ decompression and parsing. + +Second, no HDF5 or H5AD anywhere. +The traditional single-cell output format (AnnData stored as H5AD) is replaced by vortex files and DuckDB-written parquet files organized in the pqdata directory layout convention. +Both output formats are produced from a shared Arrow in-memory representation, which is also the handoff point to the downstream query and training architecture. + +Third, genome-based splice state quantification rather than splici pseudo-alignment. +The pipeline aligns reads to the genome with a splice-aware aligner (minimap2 in splice:sr mode), then classifies each alignment by overlapping its CIGAR-derived segments with GTF exon annotations. +This approach preserves actual splice junction evidence from the aligner (CIGAR N operators marking introns) rather than inferring splice state from which reference sequences a read maps to. +The trade-off is that genome alignment is slower than pseudo-alignment, but the splice junction evidence is more informative, and the pipeline avoids the need to construct and maintain splici reference indices. + +## Input sources + +Raw sequencing data enters the pipeline through two paths, both producing binseq CBQ files as the common internal representation. + +SRA archives are converted using xsra's `recode` subcommand, which reads the NCBI SRA binary format via the ncbi-vdb C library and writes directly to binseq without an intermediate FASTQ stage. +xsra supports streaming from NCBI, GCP, and AWS URLs without local prefetch, and its multi-threaded extraction divides spots across threads with per-thread local buffering. + +FASTQ files are converted using bqtools, which reads compressed FASTQ and writes binseq. +This path exists because many datasets are distributed as FASTQ rather than SRA, and some sequencing facilities deliver FASTQ directly. + +Both tools produce CBQ (columnar binary sequence) files, the recommended binseq variant. +CBQ uses columnar storage within blocks for better compression, native N-base support via Elias-Fano encoded position tracking, and 2-bit nucleotide encoding. +Block-based organization enables parallel processing via the `ParallelProcessor` trait without inter-thread synchronization. +Quality scores are stored as an optional column, preserved for workflows that need per-base quality (barcode correction, SNV calling) and omitted otherwise. + +## Processing pipeline + +The pipeline has six stages between binseq input and Arrow output. +Each stage operates on the output of the previous one, with the binseq memory-mapped representation persisting through the early stages and Arrow arrays accumulating in the later stages. + +### Barcode and UMI extraction + +Barcode and UMI segments are extracted from binseq records according to a seqspec YAML configuration that describes the read structure for a given sequencing platform. +seqspec defines which byte ranges within a read correspond to cell barcodes, UMIs, and insert sequences, abstracting over platform-specific layouts (10x Genomics, sci-RNA-seq, SHARE-seq, etc.). + +The extraction produces three components per read: the barcode sequence, the UMI sequence, and the insert sequence(s) for alignment. +With binseq's paired-end support (primary and extended sequences in a single record), read1 and read2 are accessed without multi-file coordination. + +### Barcode correction + +Cell barcodes are corrected against a whitelist using probabilistic error correction with per-base quality scores, allowing up to 2 mismatches. +When no whitelist is provided, an automatic cell-calling algorithm (OrdMag or similar) identifies genuine barcodes from the frequency distribution. +This logic is well-established in precellar's `barcode.rs` and ports directly. + +### Alignment + +Insert sequences are aligned to the genome using minimap2-rs with `Preset::SpliceSr` (splice-aware short read mode). +minimap2-rs is a Rust FFI wrapper around minimap2 v2.30 that accepts raw `&[u8]` byte sequences and returns `Mapping` structs containing CIGAR strings, mapping quality, strand, and splice-specific fields (`is_spliced: bool`, `trans_strand: Option`). + +At this boundary, binseq's 2-bit encoded sequences are decoded to ASCII bytes for the minimap2 C library. +This is a bit-unpack operation, not a format conversion: no quality score re-encoding, no read name formatting, no newline insertion. + +minimap2-rs also supports junction BED file guidance (`read_junction()`, `read_junction_lr()`) for improving splice alignment accuracy with known junction databases, and exposes junction scoring with canonical splice site detection (GT-AG, GC-AG, AT-AC). + +The choice of minimap2 over STAR for this pipeline reflects the availability of clean Rust FFI bindings (minimap2-rs) that accept raw byte sequences, versus STAR's Rust wrapper (star-aligner) which re-serializes to FASTQ text before the C FFI call and does not expose STARsolo's classification logic. +minimap2's `splice:sr` preset is the appropriate mode for short-read splice-aware alignment. + +### Annotation overlap and splice state classification + +Each alignment's CIGAR string is parsed to extract aligned segments separated by N (skip/intron) operators. +These segments are overlapped with exon coordinates from a GTF gene annotation to classify each alignment into one of four categories: exonic (all segments within exons with concordant junctions), intronic (no exon overlap), spanning (segments cross exon-intron boundaries), or discordant (splice junctions contradict transcript structure). + +This logic is ported from precellar's `transcriptome/align.rs` (~273 lines) which builds `SplicedRecord` structs from CIGAR parsing, and `transcriptome/annotate.rs` (~61 lines) which performs the exon overlap classification. +The port consumes minimap2-rs `Mapping` structs (which provide CIGAR as `Vec<(u32, u8)>` where operation code 3 is the N/intron operator) instead of noodles `RecordBuf`. + +Splice state is determined at the UMI level, not the individual read level. +If all reads supporting a UMI are exonic-only, the molecule is classified as spliced. +If any read is spanning, or all compatible transcripts have at least one intronic/boundary read, the molecule is classified as unspliced. +Everything else is ambiguous. + +### UMI deduplication + +UMIs within each cell-gene group are deduplicated using Hamming-distance-1 correction and fingerprint-based duplicate detection. +For ATAC-seq data (when the pipeline is extended to support it), deduplication uses 5-prime coordinate fingerprints instead. + +### Count matrix construction + +Deduplicated UMI counts are accumulated into a gene-by-cell count matrix with three splice state layers: spliced, unspliced, and ambiguous. +The matrix is represented as Arrow arrays in the X-Atlas-Orion list-column schema: each cell is a row with parallel `gene_token_id LIST(INT32)` and `gene_expression LIST(FLOAT32)` columns containing only non-zero entries, with gene token IDs sorted ascending as a schema invariant. + +The three splice state layers use the same list-column structure, stored as separate tables (or separate files in the pqdata directory layout). + +## Output formats + +All three output formats are produced from the same in-memory Arrow `RecordBatch` representation. +This is the boundary where this document ends and the companion architecture document begins. + +### Vortex files in pqdata directory layout + +The primary output uses vortex's Rust writer API to produce `.vortex` files organized in the pqdata directory convention: + +``` +dataset.vortex.pqdata/ + pqdata.json + obs.vortex # cell metadata + var.vortex # gene metadata (gene_token_id, ensembl_id, gene_name) + X.vortex # total count matrix (list-column schema) + layers/ + spliced.vortex # spliced UMI counts (list-column schema) + unspliced.vortex # unspliced UMI counts (list-column schema) + ambiguous.vortex # ambiguous UMI counts (list-column schema) + obsm/ + ... # embeddings (populated by downstream analysis) +``` + +Vortex accepts Arrow arrays zero-copy and automatically selects optimal encodings: sorted INT32 token ID lists trigger FastLanes delta compression, FLOAT32 expression values get ALP compression, and sparse arrays with >90% fill value use the sparse encoding. + +### Parquet files via duckdb-rs + +The same Arrow `RecordBatch` values are passed to duckdb-rs via `append_record_batch()`, then written to parquet with `COPY ... TO ... (FORMAT PARQUET)`. +DuckDB's parquet writer applies aggressive dictionary encoding, run-length encoding, and page-level zstd compression, producing smaller files than the standard `parquet-rs` writer. +The output directory follows the same pqdata layout with `.parquet` extensions. + +This path serves tools and environments without vortex support, and provides the broadest compatibility for network queries via DuckDB's httpfs extension. + +### Arrow IPC + +For in-process hand-off to downstream Rust or Python consumers (e.g., a post-processing step that computes embeddings or materializes ArrayRecord files), Arrow IPC serialization avoids any file format overhead. +This is not a persistent storage format but a zero-serialization-cost interprocess boundary. + +## Key dependencies + +- **binseq** (Arc Institute): binary sequence format library, CBQ variant with columnar storage, 2-bit encoding, memory-mapped parallel access +- **xsra** (Arc Institute): SRA archive extraction to FASTQ or binseq formats +- **bqtools** (Arc Institute): FASTQ to binseq conversion +- **minimap2-rs**: Rust FFI bindings to minimap2 v2.30, splice-aware alignment with CIGAR output, junction BED support +- **arrow-rs**: Apache Arrow Rust implementation, the in-memory interchange format shared by all output paths +- **vortex**: columnar file format with zero-copy Arrow compatibility, FastLanes/ALP/sparse encodings, Rust writer API +- **duckdb-rs**: DuckDB Rust crate, accepts Arrow RecordBatch, writes optimized parquet +- **seqspec**: read structure specification for platform-agnostic barcode/UMI extraction +- **noodles**: bioinformatics I/O library for GTF annotation parsing (exon coordinates) + +## Comparison with alternative approaches + +The pipeline's genome-based splice quantification differs from the two established approaches in the field. + +The alevin-fry/piscem approach builds a "splici" reference containing both spliced transcripts and intronic sequences, maps reads against it using pseudo-alignment (k-mer lookup in a colored compacted de Bruijn graph), and classifies by which reference sequences match. +This is faster (no genome alignment) but loses splice junction evidence: a read landing entirely within a single exon is classified by reference identity alone, with no information about whether the molecule was actually spliced. + +The STARsolo approach aligns to the genome with STAR, uses CIGAR N operators and exon annotation overlap to classify reads (essentially the same logic this pipeline ports), and integrates the classification with STARsolo's built-in UMI counting. +This is the closest equivalent to what our pipeline does, but STARsolo's classification logic is embedded in C++ and not exposed through the star-aligner Rust FFI wrapper. + +Our pipeline takes the STARsolo-equivalent approach (genome alignment + annotation overlap) but implements it with minimap2-rs for alignment and a Rust port of precellar's classification logic. +The result is a pipeline that operates entirely in Rust, consumes binseq natively, and produces Arrow arrays directly, with no subprocess orchestration, no intermediate file formats, and no language boundary crossings except the minimap2 C FFI. diff --git a/docs/notes/scilake/architecture/decisions/ingestion.md b/docs/notes/scilake/architecture/decisions/ingestion.md new file mode 100644 index 0000000..7e048c8 --- /dev/null +++ b/docs/notes/scilake/architecture/decisions/ingestion.md @@ -0,0 +1,92 @@ +# Data ingestion tooling decision + +## Status + +Proposed + +## Context + +Omicslake ingests data from heterogeneous sources: GEO FTP, Zenodo, S3, Kaggle API, CellxGene Census, and direct HTTP downloads. +Sources vary in protocol, authentication, schema stability, and update patterns. +The ingestion layer must handle downloads, schema inference, and incremental updates while preserving the zero-copy DuckLake registration pattern. + +## Options evaluated + +### dlt (data load tool) + +Python-native ELT framework with built-in source connectors. + +**Strengths:** +- Production-ready sources for filesystem (S3, GCS, local), REST APIs, SQL databases +- Automatic schema inference and evolution (flattens nested JSON, handles type changes) +- State management for incremental loading (cursor-based, lag windows) +- `load_info` provides operator visibility into pipeline runs + +**Critical limitation:** +dlt's DuckLake destination uses `INSERT INTO ... SELECT * FROM read_parquet()` — it copies data into the catalog rather than registering external files. +This conflicts with the marhar zero-copy pattern where external parquet files should be registered without duplication. + +### Direct implementation + +Custom HTTP/FTP/API handlers without framework. + +**Strengths:** +- Full control over download logic +- No framework learning curve +- Direct integration with DuckLake registration + +**Limitations:** +- Reimplements schema inference, incremental state, retry logic +- Higher maintenance burden for heterogeneous sources +- No standardized error handling + +## Decision + +**dlt for ingestion, decoupled from storage.** + +Architecture: +1. dlt sources handle download + schema normalization +2. dlt outputs parquet to designated staging directory (not DuckLake) +3. Custom registration layer reads parquet metadata and registers with DuckLake via `ducklake_add_data_files()` + +This preserves dlt's strength in handling heterogeneous sources while maintaining marhar's zero-copy semantics. + +## Implementation pattern + +```python +# dlt pipeline outputs to staging +pipeline = dlt.pipeline( + pipeline_name="geo_ingest", + destination="filesystem", # NOT ducklake + dataset_name="staging" +) +pipeline.run(geo_source()) + +# Custom registration (separate step) +duckdb.sql(""" + CREATE TABLE catalog.dataset AS + SELECT * FROM read_parquet('staging/*.parquet') WITH NO DATA; + + CALL ducklake_add_data_files('catalog', 'dataset', 'staging/*.parquet'); +""") +``` + +## Rationale + +The research showed dlt's DuckLake destination copies data rather than registering files. +Decoupling preserves both benefits: +- dlt handles the complexity of heterogeneous sources, incremental state, and schema evolution +- Zero-copy registration avoids storage duplication for large bioinformatics datasets + +## Consequences + +- Two-phase ingestion: dlt → staging → DuckLake registration +- Staging directory management (cleanup after successful registration) +- Schema validation between dlt output and DuckLake expected schema +- dlt state persists independently of DuckLake catalog state + +## References + +- ~/projects/dlt — Local dlt source +- ~/projects/dlt/dlt/destinations/impl/ducklake/ — DuckLake destination (copies, not zero-copy) +- Research: dlt suitability assessment (agent ad54ccb) diff --git a/docs/notes/scilake/architecture/decisions/metadata-store.md b/docs/notes/scilake/architecture/decisions/metadata-store.md new file mode 100644 index 0000000..0c33d11 --- /dev/null +++ b/docs/notes/scilake/architecture/decisions/metadata-store.md @@ -0,0 +1,120 @@ +# Metadata store decision + +## Status + +Proposed + +## Context + +Omicslake needs to track dataset metadata including: +- Source provenance (URL, accession, DOI, license, download timestamp) +- Schema versions for each dataset +- Lineage (which workflow produced which output, tool versions) +- Changes over time (when datasets are re-ingested or updated) + +The metadata store must support versioning to track schema evolution and enable reproducibility. + +## Options evaluated + +### Dolt / Doltgres + +Git-like versioning for SQL databases. + +**Strengths:** +- Native row-level versioning with branching and merging +- Diff/merge capabilities for collaborative schema evolution +- Full audit trail at row level +- SQL-compatible interface (MySQL or Postgres wire protocol) + +**Critical limitation:** +Both Dolt and Doltgres are server-centric, not embeddable libraries. +They must run as separate processes with network connections. +There is no embedded SQLite-like mode. +Python integration requires spawning a server or connecting over TCP. + +### SQLite + git + +Embedded database with file-level version control. + +**Strengths:** +- Embedded operation; no separate process +- Zero dependencies; integrates trivially with Python +- Git provides versioning at file level +- Schema migrations via standard patterns (Alembic, raw SQL) +- Well-understood tooling + +**Limitations:** +- No row-level versioning; changes tracked at file/commit level +- Merge conflicts require manual resolution +- No native diff of table contents (requires tooling) + +## Decision + +**SQLite + git** for metadata storage. + +## Rationale + +Dolt's advantages (row-level versioning, merge conflict resolution) matter most for collaborative data modification scenarios with concurrent writers. +Omicslake's metadata tracking has different characteristics: +- Single writer (the ingestion pipeline) +- Schema changes are rare and can be manually coordinated +- Audit trail needs are satisfied by git commit history +- Embedding without server process is strongly preferred + +SQLite + git provides: +- Embedded operation matching Dagster's local-first development model +- Git versioning aligns with existing marhar patterns (git-tracked .ducklake files) +- Standard Python tooling (sqlite3, sqlalchemy) without framework overhead + +## Metadata schema + +```sql +-- Dataset provenance +CREATE TABLE datasets ( + id TEXT PRIMARY KEY, + name TEXT NOT NULL, + source_url TEXT, + accession TEXT, + doi TEXT, + license TEXT, + format TEXT, -- h5ad, rds, csv, parquet, etc. + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + updated_at TIMESTAMP +); + +-- Ingestion runs +CREATE TABLE ingestion_runs ( + id TEXT PRIMARY KEY, + dataset_id TEXT REFERENCES datasets(id), + started_at TIMESTAMP, + completed_at TIMESTAMP, + status TEXT, -- pending, running, success, failed + tool_versions JSON, -- {"omicsio": "1.2.0", "dlt": "0.4.0"} + output_path TEXT, + row_count INTEGER, + checksum TEXT +); + +-- Schema versions +CREATE TABLE schema_versions ( + id TEXT PRIMARY KEY, + dataset_id TEXT REFERENCES datasets(id), + version INTEGER, + schema JSON, -- column names, types, nullability + created_at TIMESTAMP, + migration_notes TEXT +); +``` + +## Consequences + +- Metadata database is a single SQLite file, git-tracked +- Schema migrations managed via numbered SQL files or Alembic +- No real-time collaboration; changes coordinated through git workflow +- Lineage queries use standard SQL joins +- Dagster can emit metadata to SQLite via custom I/O manager + +## References + +- Research: dolt suitability assessment (agent a0c2262) +- biomni patterns: identified metadata gaps (no version tracking, no provenance) diff --git a/docs/notes/scilake/architecture/decisions/orchestration.md b/docs/notes/scilake/architecture/decisions/orchestration.md new file mode 100644 index 0000000..6b3d9b2 --- /dev/null +++ b/docs/notes/scilake/architecture/decisions/orchestration.md @@ -0,0 +1,111 @@ +# Workflow orchestration decision + +## Status + +Proposed + +## Context + +Omicslake needs to orchestrate data ingestion pipelines that download from heterogeneous sources, convert formats, track lineage, register with DuckLake, and publish to HuggingFace Hub. +The complexity ranges from simple single-step conversions to multi-stage pipelines with dependencies, retries, and provenance tracking. + +## Options evaluated + +### Dagster + +Asset-centric workflow orchestrator with built-in lineage tracking. + +**Strengths:** +- Asset model directly represents datasets; dependencies are explicit in the graph +- Built-in lineage and column-level metadata via `MaterializeResult` +- Resources abstract heterogeneous data sources (S3, HTTP, APIs) +- Python-native; integrates cleanly with omicsio and DuckDB +- No external cluster required; can run locally or in production mode +- Pre-built integrations for S3, GCP, databases + +**Limitations:** +- Learning curve for asset graph composition and resource wiring +- No built-in HuggingFace Hub connector (requires custom ~50 LOC) +- DuckLake registration requires custom implementation + +### Flyte SDK v2 + +Async-first workflow engine with pure Python control flow. + +**Strengths:** +- True Python control flow (loops, conditionals) without DSL constraints +- `asyncio.gather()` for native distributed parallelism +- `flyte.io.File/Dir` handles file transport across storage backends +- Tracing infrastructure for function-level observability +- Can run locally without cluster via `flyte.init()` + +**Limitations:** +- Lineage is implicit (execution history), not explicit data provenance +- Only BigQuery connector is stable; GEO/Zenodo/Kaggle require custom wrappers +- Migration from pyrovelocity's v1 flytekit requires rewrites +- No data catalog integration + +### Temporal + +Durable execution engine with event-sourced workflow history. + +**Strengths:** +- Long-running downloads survive failures; automatic checkpoint/resume +- Built-in retry with configurable backoff for transient network failures +- Event-sourced history creates audit trail +- Excellent observability via OTEL integration + +**Limitations:** +- Requires separate server cluster (Temporal Cloud or self-hosted) +- Tracks execution lineage, not data lineage +- No format conversion integration; purely orchestration +- Infrastructure overhead exceeds benefit for data-focused pipelines + +### Marhar pattern (no orchestrator) + +Shell scripts + SQL + DuckDB CLI. + +**Strengths:** +- Zero framework overhead; uses only standard tools +- Scripts are transparent and readable +- Fast iteration; `duckwatch` enables interactive development +- Native DuckDB integration with full SQL syntax + +**Limitations:** +- Manual ordering; DAG enforcement prevents silent failures at scale +- No automatic retry on transient failures +- No lineage tracking beyond git history +- Breaks down beyond ~5 sequential steps + +## Decision + +**Dagster** for pipelines requiring lineage tracking, retry logic, or >5 steps. + +**Marhar pattern** remains valid for simple, one-off conversions where Dagster overhead is unjustified. + +## Rationale + +Dagster's asset-centric model aligns with scilake's core abstraction: datasets as versioned, traceable artifacts. +The research findings showed: + +1. Dagster provides automatic lineage without extra implementation +2. Asset dependencies make data flow explicit and debuggable +3. Python-native resources integrate cleanly with omicsio and DuckDB +4. No external cluster required reduces operational burden + +Flyte v2 and Temporal are better suited for compute-heavy workflows where durability matters more than data lineage. +Omicslake's primary concern is provenance tracking, where Dagster excels. + +## Consequences + +- Dagster assets become the primary abstraction for dataset ingestion +- Custom resources needed for: GEO FTP, Zenodo, Kaggle API, HuggingFace Hub +- DuckLake registration implemented as downstream asset depending on parquet materialization +- Learning curve for team members unfamiliar with Dagster concepts +- Simple pipelines can still use marhar scripts without Dagster + +## References + +- ~/projects/scilake-workspace/dagster — Local Dagster source +- Dagster assets documentation: https://docs.dagster.io/concepts/assets +- Research: Dagster suitability assessment (agent a29b7f8) diff --git a/docs/notes/scilake/architecture/overview.md b/docs/notes/scilake/architecture/overview.md new file mode 100644 index 0000000..94caee1 --- /dev/null +++ b/docs/notes/scilake/architecture/overview.md @@ -0,0 +1,166 @@ +# Scilake architecture + +Scilake is a data provenance and reproducible workflow system for scientific data engineering. +It bridges computational modeling pipelines and data-driven web applications through DuckDB, DuckLake, SQLMesh, and parquet. + +## Scope + +Scilake addresses three interconnected problems. + +Heterogeneous format ingestion: source datasets arrive as h5ad (AnnData), RDS (R objects), mtx.gz with metadata CSVs, pickle files, TSV/CSV tables, and FTP archives. +Each requires format-specific handling before standardization to parquet. + +Lineage and provenance tracking: existing parquet files lack documented lineage. +Scilake traces data back to original sources (GEO accessions, Zenodo DOIs, S3 URIs) and encodes reproducible workflows. + +DuckLake catalog generation: the output layer produces parquet files registered with DuckLake catalogs, published to HuggingFace Hub for downstream query via httpfs and the `hf://` protocol. + +## Architecture layers + +``` +┌─────────────────────────────────────────────────────────────────────────┐ +│ Publish Layer │ +│ HuggingFace Hub (git-lfs) <- DuckLake catalog + parquet │ +├─────────────────────────────────────────────────────────────────────────┤ +│ Transformation Layer │ +│ SQLMesh models (staging, analytics) <- declarative SQL on DuckLake │ +├─────────────────────────────────────────────────────────────────────────┤ +│ Catalog Layer │ +│ DuckLake registration (zero-copy) <- parquet files │ +├─────────────────────────────────────────────────────────────────────────┤ +│ Metadata Layer │ +│ SQLite (lineage, schema, provenance) + git versioning │ +├─────────────────────────────────────────────────────────────────────────┤ +│ Convert Layer │ +│ omicsio (h5ad->parquet) | RDS->AnnData->parquet | dlt normalization │ +├─────────────────────────────────────────────────────────────────────────┤ +│ Ingest Layer │ +│ dlt sources (S3, HTTP, API) | custom sources (GEO FTP, Kaggle) │ +├─────────────────────────────────────────────────────────────────────────┤ +│ Orchestration Layer │ +│ Dagster assets (lineage, scheduling, retry) │ +└─────────────────────────────────────────────────────────────────────────┘ +``` + +## Layer summary + +| Layer | Responsibility | Primary tools | Inputs | Outputs | +|-------|---------------|---------------|--------|---------| +| Orchestration | Scheduling, retry, lineage | Dagster | Pipeline definitions | Coordinated execution | +| Ingest | Download, schema inference | dlt, custom handlers | URLs, APIs | Raw files | +| Convert | Format standardization | omicsio, rds2anndata, DuckDB | Raw files | Parquet | +| Metadata | Provenance tracking | SQLite + git | Ingestion events | Lineage records | +| Catalog | DuckLake registration | DuckDB + ducklake | Parquet files | Queryable catalog | +| Publish | Distribution | huggingface_hub | Catalog + parquet | hf:// URLs | + +## Ingest layer + +This layer downloads data from heterogeneous sources and performs initial schema inference. + +dlt sources handle most cases: the `filesystem` source covers S3, GCS, and local files, `rest_api` covers Zenodo and CellxGene APIs, and custom resources handle GEO FTP and Kaggle API access. +The output is raw files in a staging directory with dlt state tracking incremental updates. + +## Convert layer + +This layer transforms source formats to standardized parquet. + +The conversion paths are: h5ad to parquet via omicsio (preserving AnnData semantics), RDS to AnnData via rds2anndata then to parquet via omicsio, mtx.gz with CSV to AnnData via scanpy assembly then to parquet via omicsio, and CSV/TSV/pickle directly to parquet via DuckDB. +The output is parquet files with consistent schema conventions. + +## Metadata layer + +This layer tracks provenance, lineage, and schema versions in SQLite. + +The schema includes three tables: `datasets` for source provenance (URL, accession, DOI, license), `ingestion_runs` for execution history (timestamps, tool versions, checksums), and `schema_versions` for schema evolution tracking. +The SQLite file is git-tracked, with changes committed alongside ingestion runs. + +## Catalog layer + +This layer registers parquet files with DuckLake using a zero-copy pattern. + +```sql +-- Create table structure without loading data +CREATE TABLE catalog.dataset AS +SELECT * FROM read_parquet('path/*.parquet') WITH NO DATA; + +-- Register files (metadata only, no data copy) +CALL ducklake_add_data_files('catalog', 'dataset', 'path/*.parquet'); +``` + +The output is a DuckLake catalog consisting of `.ducklake` metadata and registered parquet files. + +## Publish layer + +This layer publishes DuckLake catalogs to HuggingFace Hub for remote query access. +Git-lfs tracks `.parquet` and `.ducklake` files, which are pushed to a HuggingFace dataset repo. +Downstream consumers query via `hf://datasets/org/repo/path.ducklake` using the DuckDB httpfs extension. + +## Layer boundaries + +Each layer communicates through well-defined interfaces. + +| Boundary | Interface | Format | +|----------|-----------|--------| +| Ingest to Convert | File paths | Raw files in staging | +| Convert to Catalog | File paths | Parquet in output dir | +| All to Metadata | Events | SQLite inserts | +| Catalog to Publish | Directory | DuckLake catalog tree | + +Dagster assets encode these dependencies explicitly, enabling partial re-execution (re-run convert without re-ingest), lineage queries (which source produced which output), and failure isolation (ingest failure does not corrupt the catalog). + +## Format landscape + +Source formats requiring conversion to parquet: + +| Format | Conversion path | Tool | Examples | +|--------|-----------------|------|----------| +| h5ad | h5ad to AnnData to Arrow to parquet | omicsio | NeurIPS BMMC, scPerturb, Replogle | +| RDS | RDS to AnnData to parquet | rds2anndata + omicsio | ZSCAPE, Weinreb LARRY | +| mtx.gz + CSV | assemble to AnnData to parquet | scanpy + omicsio | GEO raw counts | +| CSV/TSV | direct load | DuckDB | DepMap, BindingDB, eQTL tables | +| pickle | unpickle to DataFrame to parquet | pandas/DuckDB | genebass, gwas_catalog | +| FTP tarball | extract then route by contents | custom | GEO supplementary | + +## Key architectural decisions + +| Decision | Choice | Alternatives considered | Rationale | +|----------|--------|------------------------|-----------| +| Workflow orchestration | Dagster | Flyte v2, Temporal | Asset-centric lineage; Python-native; no external cluster required | +| Ingestion tooling | dlt (decoupled) | Direct HTTP/FTP | Schema inference; incremental loading; heterogeneous source support | +| DuckLake registration | Zero-copy | dlt DuckLake destination | Preserves storage efficiency; no data duplication | +| Transformations | SQLMesh | dbt, raw SQL views | Incremental processing; DuckLake integration; declarative models | +| Metadata store | SQLite + git | Dolt, Doltgres | Embeddable; no server process; git provides versioning | +| Parquet conversion | omicsio | Direct DuckDB | omicsio handles AnnData semantics (X, obs, var, layers) | + +ADR-style documentation for each decision is in the `decisions/` directory. + +## Data flow + +``` +Source (GEO/Zenodo/S3/API) + | + v [Ingest: dlt or custom] +Staging (raw files) + | + v [Convert: omicsio/rds2anndata/DuckDB] +Parquet files (standardized) + | + |---> [Metadata: SQLite] --- lineage, schema, provenance + | + v [Catalog: DuckLake zero-copy registration] +DuckLake catalog (.ducklake + .parquet) + | + v [Publish: huggingface_hub] +HuggingFace Hub (hf://datasets/sciexp/...) + | + v [Query: httpfs] +DuckDB client (DuckDB-WASM or server) +``` + +## References + +- dagster-sqlmesh: Dagster + SQLMesh integration example +- ducklake-dagster-crypto-pipeline: DuckLake + dlt + Dagster integration example +- sciexp-fixtures: working DuckLake reference implementation +- SQLMesh DuckLake integration: https://sqlmesh.readthedocs.io/en/stable/integrations/engines/duckdb/#ducklake-catalog-example +- Tobiko DuckLake tutorial: https://www.tobikodata.com/blog/ducklake-sqlmesh-tutorial-a-hands-on diff --git a/modules/devshell.nix b/modules/devshell.nix index 9be55aa..b598ce7 100644 --- a/modules/devshell.nix +++ b/modules/devshell.nix @@ -85,17 +85,17 @@ in { devShells = rec { - omicsio312 = mkDevShell { - name = "omicsio-3.12"; + data312 = mkDevShell { + name = "data-3.12"; pythonVersion = "py312"; }; - omicsio313 = mkDevShell { - name = "omicsio-3.13"; + data313 = mkDevShell { + name = "data-3.13"; pythonVersion = "py313"; }; - default = omicsio313; + default = data313; }; }; } diff --git a/packages/scilake/.ci.json b/packages/scilake/.ci.json new file mode 100644 index 0000000..924bd14 --- /dev/null +++ b/packages/scilake/.ci.json @@ -0,0 +1,4 @@ +{ + "build-images": false, + "images": ["scilake", "scilake-dev"] +} diff --git a/packages/scilake/README.md b/packages/scilake/README.md new file mode 100644 index 0000000..d7f3a91 --- /dev/null +++ b/packages/scilake/README.md @@ -0,0 +1 @@ +# scilake diff --git a/packages/scilake/package.json b/packages/scilake/package.json new file mode 100644 index 0000000..77bfb17 --- /dev/null +++ b/packages/scilake/package.json @@ -0,0 +1,91 @@ +{ + "description": "Domain-agnostic lakehouse orchestration for scientific data", + "devDependencies": { + "@semantic-release/changelog": "^6.0.3", + "@semantic-release/github": "^12.0.0", + "conventional-changelog-conventionalcommits": "^9.0.0", + "cross-env": "^10.0.0", + "semantic-release": "^25.0.0", + "semantic-release-major-tag": "^0.3.2", + "semantic-release-monorepo": "^8.0.2" + }, + "engines": { + "node": ">=18" + }, + "license": "MIT", + "name": "scilake", + "packageManager": "bun@1.3.4", + "private": true, + "release": { + "branches": [ + { + "name": "main" + }, + { + "name": "beta", + "prerelease": true + } + ], + "extends": "semantic-release-monorepo", + "npmPublish": false, 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+--cov=src/scilake/ +--cov-report=term-missing +--cov-report=xml +--durations=0 +""" +log_level = "INFO" +testpaths = [ + "src/scilake/tests", +] + +[tool.coverage.run] +omit = [ + "src/scilake/tests/*", +] diff --git a/packages/scilake/src/scilake/__init__.py b/packages/scilake/src/scilake/__init__.py new file mode 100644 index 0000000..061605c --- /dev/null +++ b/packages/scilake/src/scilake/__init__.py @@ -0,0 +1,3 @@ +"""Domain-agnostic lakehouse orchestration for scientific data.""" + +__version__ = "0.0.0" diff --git a/packages/scilake/src/scilake/py.typed b/packages/scilake/src/scilake/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/packages/scilake/src/scilake/tests/__init__.py b/packages/scilake/src/scilake/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/packages/scilake/src/scilake/tests/test_scilake.py b/packages/scilake/src/scilake/tests/test_scilake.py new file mode 100644 index 0000000..939c070 --- /dev/null +++ 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