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Process barcoded spatial transcriptomics FASTQ pairs with st_pipeline, preserve upstream artifacts, convert the counts matrix into a standardized raw_counts.h5ad, and hand off cleanly to spatial-preprocess.
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| name | spatial-raw-processing |
| description | Process barcoded spatial transcriptomics FASTQ pairs with st_pipeline, preserve upstream artifacts, convert the counts matrix into a standardized raw_counts.h5ad, and hand off cleanly to spatial-preprocess. |
| version | 0.1.0 |
| author | OmicsClaw Team |
| license | MIT |
| tags | ["spatial","raw-processing","fastq","st_pipeline","visium","slideseq","upstream"] |
| metadata | {"omicsclaw":{"domain":"spatial","allowed_extra_flags":["--read1","--read2","--ids","--ref-map","--ref-annotation","--exp-name","--platform","--threads","--contaminant-index","--min-length-qual-trimming","--min-quality-trimming","--demultiplexing-mismatches","--demultiplexing-kmer","--umi-allowed-mismatches","--umi-start-position","--umi-end-position","--disable-clipping","--compute-saturation","--htseq-no-ambiguous","--transcriptome","--star-two-pass-mode","--stpipeline-repo","--bin-path"],"param_hints":{"st_pipeline":"[Truncated]"},"legacy_aliases":["spatial-raw-fastq-processing","spatial-st-pipeline"],"saves_h5ad":true,"requires_preprocessed":false,"requires":{"bins":"[Truncated]","env":"[Truncated]","config":"[Truncated]"},"emoji":"🧬","homepage":"https://github.com/TianGzlab/OmicsClaw","os":["macos","linux"],"install":["[Truncated]"],"trigger_keywords":["spatial raw processing","raw spatial fastq","spatial fastq","st_pipeline","st pipeline","barcode coordinates","ids file","visium raw fastq","slide-seq fastq","slideseq fastq","upstream spatial processing"]}} |
You are Spatial Raw Processing, the OmicsClaw skill for turning barcoded
spatial transcriptomics FASTQ pairs into a standardized raw-count AnnData via
st_pipeline. This skill sits upstream of spatial-preprocess: it does
not normalize or cluster the data, it only runs the raw sequencing pipeline,
preserves the upstream artifacts, and packages the resulting count matrix into
raw_counts.h5ad.
st_pipeline, keep track of IDs / STAR index / GTF paths, convert the output TSV into AnnData, and then remember which object should be passed into downstream OmicsClaw skills.raw_counts.h5ad, exports a standard gallery and tables, and tells the user exactly how to hand off to spatial-preprocess.st_pipeline object model, report structure, figure-data exports, and downstream routing instead of leaving raw TSV/BED/log files as disconnected artifacts.st_pipeline on a resolved R1/R2 FASTQ pair with barcode-coordinate metadata.spatial-preprocess.<expName>_stdata.tsv plus the IDs table into raw_counts.h5ad with layers["counts"], raw, and obsm["spatial"].upstream/st_pipeline/.figure_data/ CSVs plus a manifest for downstream customization.spatial-preprocess as the canonical next step and surfaces the exact handoff path in the report and .result.json| Format | Extension | Required Fields / Structure | Example |
|---|---|---|---|
| FASTQ pair | .fastq, .fq, .fastq.gz, .fq.gz | R1 and R2 from a barcoded spatial assay | sample_R1.fastq.gz, sample_R2.fastq.gz |
| FASTQ directory | directory | exactly one resolvable FASTQ pair, or use --read1/--read2 explicitly | fastq_run/ |
| Run config | .json, .yaml, .yml | keys such as input, ids, ref_map, ref_annotation, exp_name | st_run.yaml |
| Demo | n/a | --demo flag | built-in synthetic upstream outputs |
| Requirement | Where it should exist | Why it matters |
|---|---|---|
| R1 FASTQ with barcodes / UMI | --read1 or resolved from --input | Required by st_pipeline for barcode and UMI parsing |
| R2 FASTQ with transcript sequence | --read2 or resolved from --input | Required by st_pipeline for mapping and annotation |
| Barcode-to-coordinate map | --ids | Supplies (BARCODE, X, Y) so the output matrix can be placed onto spatial coordinates |
| STAR genome index | --ref-map | Required by upstream mapping |
| Gene annotation or transcriptome mode | --ref-annotation unless --transcriptome | Required to assign mapped reads to genes |
Important boundary:
.h5ad, Space Ranger matrix directories, or Xenium exports here. Those are matrix-level inputs and belong in spatial-preprocess.spatial-raw-processing is for sequencing-level spatial assays where you still have FASTQ files plus barcode-coordinate metadata.--read1/--read2, a pair-containing directory, or a JSON/YAML config.st_pipeline or use the OmicsClaw demo path.upstream/st_pipeline/.raw_counts.h5ad with raw counts in X, layers["counts"], raw, and obsm["spatial"].figure_data/.README.md, report.md, result.json, and the reproducibility bundle, then point the user to spatial-preprocess.# Standard usage with explicit FASTQ paths
oc run spatial-raw-processing \
--read1 sample_R1.fastq.gz --read2 sample_R2.fastq.gz \
--ids ids/barcodes.txt --ref-map refs/star_index --ref-annotation refs/genes.gtf \
--output /tmp/spatial_raw_out
# Resolve the FASTQ pair from an input directory
oc run spatial-raw-processing \
--input fastq_run/ \
--ids ids/barcodes.txt --ref-map refs/star_index --ref-annotation refs/genes.gtf \
--platform visium --output /tmp/spatial_raw_out
# Use a local st_pipeline repository clone instead of a global install
oc run spatial-raw-processing \
--read1 sample_R1.fastq.gz --read2 sample_R2.fastq.gz \
--ids ids/barcodes.txt --ref-map refs/star_index --ref-annotation refs/genes.gtf \
--stpipeline-repo /path/to/st_pipeline \
--output /tmp/spatial_raw_out
# Enable saturation reporting
oc run spatial-raw-processing \
--read1 sample_R1.fastq.gz --read2 sample_R2.fastq.gz \
--ids ids/barcodes.txt --ref-map refs/star_index --ref-annotation refs/genes.gtf \
--compute-saturation --output /tmp/spatial_raw_out
# Config-driven execution
oc run spatial-raw-processing --input st_run.yaml --output /tmp/spatial_raw_out
# Demo mode
oc run spatial-raw-processing --demo --output /tmp/spatial_raw_demo
# Direct script entrypoint
python skills/spatial/spatial-raw-processing/spatial_raw_processing.py \
--read1 sample_R1.fastq.gz --read2 sample_R2.fastq.gz \
--ids ids/barcodes.txt --ref-map refs/star_index --ref-annotation refs/genes.gtf \
--output /tmp/spatial_raw_out
st_pipelinest_pipeline_run from either an installed package, a PATH binary, or --stpipeline-repo.X, layers["counts"], and raw so downstream OmicsClaw preprocessing remains consistent.Key parameters:
| Parameter | Default | Description |
|---|---|---|
platform | visium | Output/report label only; does not switch algorithms |
threads | 4 | Worker count forwarded to upstream st_pipeline |
min_length_qual_trimming | 20 | Upstream read-length filter after quality trimming |
min_quality_trimming | 20 | Upstream base-quality threshold |
demultiplexing_mismatches | 2 | Allowed barcode mismatches during demultiplexing |
demultiplexing_kmer | 6 | Barcode k-mer size used during demultiplexing |
umi_allowed_mismatches | 1 | UMI clustering tolerance |
umi_start_position | 18 | UMI start position in R1 |
umi_end_position | 27 | UMI end position in R1 |
compute_saturation | False | Request upstream sequencing saturation summaries |
Current OmicsClaw behavior: the wrapper intentionally exposes only the first-pass tuning knobs most users need. If a run requires deeper upstream control, the preserved upstream metadata makes it clear what was executed and where to rerun custom
st_pipelinevariants.
OmicsClaw treats spatial-raw-processing visualization as a layered contract:
figure_data/ tables for downstream customization.figure_data/ without rerunning the upstream pipeline.Current gallery roles include:
overview: coordinate-level raw count structurediagnostic: upstream read attrition and coordinate-level feature complexitysupporting: raw QC distributions and top detected genesuncertainty: optional saturation summaries when enabled upstreamoutput_directory/
├── README.md
├── report.md
├── result.json
├── raw_counts.h5ad
├── upstream/
│ └── st_pipeline/
│ ├── <expName>_stdata.tsv
│ ├── <expName>_reads.bed
│ ├── <expName>_pipeline.log
│ ├── st_pipeline.stdout.txt
│ ├── st_pipeline.stderr.txt
│ └── omicsclaw_stpipeline_run.json
├── figures/
│ ├── *.png
│ └── manifest.json
├── tables/
│ ├── run_summary.csv
│ ├── stage_summary.csv
│ ├── spot_qc.csv
│ ├── gene_qc.csv
│ └── top_genes.csv
├── figure_data/
│ ├── raw_processing_run_summary.csv
│ ├── stage_summary.csv
│ ├── raw_spot_qc.csv
│ ├── raw_gene_qc.csv
│ ├── raw_top_genes.csv
│ └── manifest.json
├── r_visualization/
│ ├── README.md
│ └── raw_processing_publication_template.R
└── reproducibility/
├── analysis_notebook.ipynb
├── commands.sh
├── requirements.txt
└── r_visualization.sh
raw_counts.h5ad: raw-count handoff object for spatial-preprocess.upstream/st_pipeline/: preserved upstream outputs for auditing or manual reruns.tables/: complete tabular exports for downstream analysis.figure_data/: plot-ready CSV exports that mirror the standard gallery.report.md: explains what was run and how to continue downstream.knowledge_base/knowhows/KH-spatial-raw-processing-guardrails.mdknowledge_base/skill-guides/spatial/spatial-raw-processing.mdknowledge_base/skill-guides/spatial/spatial-preprocess.mdBoundary reminder:
spatial-raw-processing: sequencing-level upstream processingspatial-preprocess: matrix-level QC, normalization, HVG selection, embedding, and clusteringRequired:
anndata, numpy, pandas, scipy, matplotlibst_pipeline available on PATH, installed as a package, or provided through --stpipeline-repoOptional but recommended:
Rscript plus ggplot2 if you want to use the provided R visualization template