omicverse-single-cell-kb-alignment

name	omicverse-single-cell-kb-alignment
description	Build a triggerable kb reference and quantify single-cell FASTQs with OmicVerse alignment.single. Use when converting OmicVerse alignment notebooks into a reusable skill, when creating kallisto|bustools references with ref, or when running count on single-cell RNA-seq libraries.

OmicVerse Single-Cell kb Alignment

Goal

Build a kb reference from FASTA/GTF inputs, then quantify single-cell FASTQs into count matrices with OmicVerse alignment wrappers. This skill stops at the count matrix. The notebook's later QC, preprocessing, scaling, and PCA cells are generic follow-on work and should be handled separately.

Quick Workflow

1. Choose the reference workflow and sequencing technology.
2. Build the reference with ov.alignment.single.ref(...).
3. Run ov.alignment.single.count(...) on matching FASTQs.
4. Validate the returned paths and, if requested, load the emitted adata.h5ad.
5. Hand the count matrix to your downstream single-cell preprocessing workflow if needed.

Interface Summary

• ov.alignment.single.ref(index_path, t2g_path, fasta_paths=None, gtf_paths=None, cdna_path=None, workflow='standard', d=None, k=None, threads=8, overwrite=False, temp_dir='tmp', make_unique=False, include=None, exclude=None, dlist=None, dlist_overhang=1, aa=False, max_ec_size=None, nucleus=False, f2=None, c1=None, c2=None, flank=None, feature=None, no_mismatches=False, distinguish=False, **kwargs) builds the reference bundle.
• ov.alignment.single.count(index_path, t2g_path, technology, fastq_paths, output_path='.', whitelist_path=None, replacement_path=None, threads=8, memory='2G', workflow='standard', overwrite=False, temp_dir='tmp', tcc=False, mm=False, filter_barcodes=False, filter_threshold=None, loom=False, loom_names=None, h5ad=False, cellranger=False, gene_names=False, report=False, strand=None, parity=None, fragment_l=None, fragment_s=None, bootstraps=None, em=False, aa=False, genomebam=False, inleaved=False, batch_barcodes=False, exact_barcodes=False, numreads=None, store_num=False, long_read=False, threshold=0.8, platform='ONT', c1=None, c2=None, nucleus=False, **kwargs) quantifies reads and returns discovered output paths.
• ov.alignment.ref and ov.alignment.count are aliases; ov.alignment.single is the convenient namespace used by the notebook.

Boundary

• Keep this skill focused on kb reference building and count generation.
• Do not merge generic QC/preprocess/PCA into the same skill unless you want a separate downstream preprocessing skill.

Branch Selection

• Use workflow='standard' unless the reference bundle requires a special mode.
• Use nucleus=True or workflow='nucleus' for nucleus/velocity workflows.
• Other ref workflows in the source include lamanno, nac, kite, and custom.
• Use technology='10XV3' or another preset that matches the library chemistry.
• technology='BULK' disables barcode filtering; filter_barcodes and filter_threshold must not be set there.
• In count, tcc=True and mm=True are mutually exclusive; the wrapper prefers tcc.
• Use h5ad=True when you want a quick AnnData artifact for downstream processing.

Input Contract

• ref needs either fasta_paths plus gtf_paths, or a prebuilt reference bundle via d.
• count needs the reference index_path and t2g_path produced by ref.
• fastq_paths may be one file or a list of files; for paired-end 10x data, pass both R1 and R2 files.
• Keep output_path writable; the wrapper creates it if needed.

Minimal Execution Patterns

Reference build:

ref_info = ov.alignment.single.ref(
    index_path="ref/index.idx",
    t2g_path="ref/t2g.txt",
    fasta_paths="ref/transcripts.fa",
    gtf_paths="ref/genes.gtf",
)

Count matrix generation:

count_info = ov.alignment.single.count(
    index_path="ref/index.idx",
    t2g_path="ref/t2g.txt",
    technology="10XV3",
    fastq_paths=["reads_R1.fastq.gz", "reads_R2.fastq.gz"],
    output_path="counts",
    h5ad=True,
    filter_barcodes=True,
)

Validation

• ref_info["index_path"] and ref_info["t2g_path"] should exist after ref.
• count_info should include any output files that exist on disk, such as h5ad_file, matrix_file, barcodes_file, and genes_file.
• If h5ad=True, load the emitted adata.h5ad and confirm the expected shape.
• Confirm that technology='BULK' with filter_barcodes=True raises a ValueError.

Resource Map

• Read references/workflow-selection.md for workflow and technology branch choices.
• Read references/source-notebook-map.md to see how the notebook maps to the reusable alignment boundary.
• Read references/source-grounding.md for inspected signatures and source behavior.
• Read references/compatibility.md for dependency and runtime caveats.