sc-velocity-prep

sc-velocity-prep

Use from a step

This is CLI_ONLY: it runs external preparation tools or imports their files.

from skills._sdk.notebook import run_cli

run_cli("sc-velocity-prep", "--input", "data/velocity.loom",
        "--base-h5ad", "data/clustered.h5ad",
        inputs=["data/velocity.loom", "data/clustered.h5ad"])

The demo splits PBMC3k counts into fixed 82% spliced, 14% unspliced and 4% ambiguous layers. It checks packaging, not velocity biology or external tool installation. There is no _api.py for this skill.

When to use

The user has raw Cell Ranger output (BAM + barcodes), STARsolo output, or paired FASTQs and needs an AnnData with layers["spliced"] / layers["unspliced"] (and optional layers["ambiguous"]) before running sc-velocity. Two backends:

• velocyto (default) — runs velocyto run against a Cell Ranger BAM using a GTF. Produces a .loom and reads it back into AnnData.
• starsolo — re-runs alignment from FASTQ via STARsolo with the Velocyto solo subworkflow, or loads existing STARsolo Velocyto output directly when detected.

--base-h5ad lets you merge the velocity layers into an already-processed AnnData (preserves obs / obsm / clustering).

For velocity estimation itself use sc-velocity. For non-velocity scRNA preprocessing use sc-preprocessing.

Inputs & Outputs

Inputs

• Input kinds: file, directory
• Modalities: scrna
• File types: .loom, .fastq, .fq
• FASTQ structure: valid first record; paired layout
• Directory layouts (any): paired-fastq, cellranger-output, starsolo-velocity

Outputs

• tables/top_velocity_genes.csv
• tables/velocity_layer_summary.csv
• figures/velocity_gene_balance.png
• figures/velocity_layer_fraction.png
• figures/velocity_layer_summary.png
• figures/velocity_top_genes_stacked.png
• processed.h5ad
• velocity_input.h5ad
• figures/manifest.json, figure_data/manifest.json, plot-data CSV files
• reproducibility/commands.sh, reproducibility/requirements.txt
• Conditional backend artifacts under artifacts/; imported loom/STARsolo inputs do not create new BAM files.
• report.md
• result.json
• Processed AnnData — spliced, unspliced layers; ambiguous only when supplied by the source

Flow

1. Resolve --input (Cell Ranger dir / STARsolo dir / FASTQ / .loom).
2. For velocyto: locate BAM + barcodes, validate --gtf (or auto-pick from resources/singlecell/references/gtf/), run velocyto run, load .loom.
3. For starsolo: detect existing STARsolo Velocyto output and load directly, OR re-run STARsolo Velocyto with --reference + --chemistry + auto-detected --whitelist.
4. Optionally merge layers into --base-h5ad.
5. Compute layer totals (tables/velocity_layer_summary.csv) and top-gene balance.
6. Save processed.h5ad, tables, figures, report.md, result.json.

Gotchas

• BAM-backed velocyto needs a GTF. sc_velocity_prep.py raises ValueError("BAM-backed velocyto preparation requires a GTF file. Pass --gtf /abs/path/to/genes.gtf, or keep one under resources/singlecell/references/gtf/. ..."). Auto-detection only fires if a project-local GTF lives at the recommended path.
• FASTQ-backed STARsolo needs a STAR index AND explicit chemistry. sc_velocity_prep.py raises ValueError("FASTQ-backed STARsolo velocity preparation requires a STAR genome directory. ...") if --reference is missing and nothing's at resources/singlecell/references/starsolo/. sc_velocity_prep.py raises ValueError("FASTQ-backed STARsolo velocity preparation requires an explicit --chemistry.") when --chemistry auto is left as the default — STARsolo cannot infer 10x v2 vs v3 vs v4 from FASTQ alone.
• STARsolo whitelist is auto-guessed; missing → hard fail. sc_velocity_prep.py raises ValueError("Could not infer a compatible STARsolo whitelist. Pass --whitelist /abs/path/to/3M-february-2018.txt, or keep the whitelist under resources/singlecell/references/whitelists/. ..."). The guesser uses the reference path + chemistry; a non-standard reference layout breaks it.
• STARsolo Velocyto matrix loader has a fallback for index-name quirks. sc_velocity_prep.py is documented as "with a local fallback for index-name quirks"; _load_starsolo_velocyto_dir_safe raises FileNotFoundError(f"Could not locate STARsolo Velocyto matrices under: {path}") when nothing matches even with the fallback. Common when STARsolo finished partial / was killed mid-run.
• --input mandatory unless --demo (parser.error, exit code 2). sc_velocity_prep.py calls parser.error("--input required when not using --demo"). Once provided, main raises FileNotFoundError(f"Input path not found: {input_path}") for a missing path.
• --method choices are exactly velocyto / starsolo. sc_velocity_prep.py declares the choices via argparse; kb-python is mentioned in upstream-prep docstrings but is not a valid --method value here. Use the dedicated kb-python tooling outside OmicsClaw if you need that path.
• sc_velocity_prep.py:main defaults to eight threads. Reference directories named in error messages are suggestions, not shipped assets. Verify the velocyto command can import before a long BAM run; migration preflight found undefined symbol: __log10_finite in the installed copy despite its presence on PATH.
• sc_velocity_prep.py:main writes a new .raw snapshot and records .X as raw counts even when --base-h5ad contains normalized .X. _lib/upstream.py:merge_velocity_layers also replaces layers["counts"] with the velocity input's counts (the sum of its available splicing layers for imported data). Inspect this existing contract mismatch before treating the merged object's .X or .raw as counts; the spliced/unspliced layers remain separate.

Key CLI

# Demo (proportional PBMC3k layers; no velocyto / STARsolo)
python skills/singlecell/scrna/sc-velocity-prep/sc_velocity_prep.py --demo --output /tmp/sc_velo_prep_demo

# velocyto from a Cell Ranger run (BAM-backed)
python skills/singlecell/scrna/sc-velocity-prep/sc_velocity_prep.py \
  --input /data/cellranger_run/ --output results/ \
  --method velocyto --gtf /refs/Homo_sapiens.GRCh38.gtf

# Load existing STARsolo Velocyto output directly
python skills/singlecell/scrna/sc-velocity-prep/sc_velocity_prep.py \
  --input /data/starsolo_run/ --output results/ \
  --method starsolo

# Re-run STARsolo from FASTQ (chemistry must be explicit)
python skills/singlecell/scrna/sc-velocity-prep/sc_velocity_prep.py \
  --input /data/fastqs/ --output results/ \
  --method starsolo --reference /refs/star_index --chemistry 10xv3

# Merge velocity layers into an existing processed AnnData
python skills/singlecell/scrna/sc-velocity-prep/sc_velocity_prep.py \
  --input /data/cellranger_run/ --output results/ \
  --method velocyto --gtf /refs/Homo_sapiens.GRCh38.gtf \
  --base-h5ad /path/to/clustered.h5ad

See also

• references/parameters.md — every CLI flag, per-backend tunables
• references/methodology.md — when velocyto vs STARsolo wins; whitelist conventions
• references/output_contract.md — layers["spliced"] / layers["unspliced"] / layers["ambiguous"] schema
• Adjacent skills: sc-count / sc-multi-count (upstream — produce the Cell Ranger / STARsolo output this skill consumes), sc-velocity (downstream — consumes layers["spliced"] + layers["unspliced"]), sc-clustering (parallel — pass clustered output as --base-h5ad to keep clusters when adding velocity layers)

Dependencies

Python packages this skill's script needs. They are not installed for you — check before a long run.

anndata, matplotlib, numpy, pandas, scanpy, scipy, seaborn