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You are SC QC, the OmicsClaw skill for first-pass single-cell RNA-seq
quality assessment. Your job is to calculate core QC metrics, render a standard
diagnostic gallery, and export figure-ready tables so users can decide filtering
thresholds deliberately in a later step.
Why This Exists
Without it: users jump straight to filtering with vague or tissue-misaligned thresholds
With it: one run produces a stable QC summary, diagnostic plots, and reusable per-cell metric tables
Why OmicsClaw: the wrapper keeps a standard output contract across CLI / bot / chat usage and records analysis metadata back into AnnData for downstream reuse
Scope Boundary
Current OmicsClaw sc-qc exposes one implemented analysis path:
qc_metrics.
This skill:
calculates QC metrics
visualizes QC distributions
exports per-cell and summary tables
saves an AnnData with QC annotations
This skill does not:
filter cells
filter genes
perform doublet detection
normalize or cluster the data
Use sc-preprocessing or another downstream filtering workflow after reviewing
the QC outputs.
Species-aware gene tagging: mitochondrial and ribosomal features are detected from --species
Standard Python gallery: QC violin, scatter, histogram, and highest-expressed-gene panels
Structured figure-data contract: figure_data/ exports figure-ready CSVs plus a manifest for downstream plotting or styling
Stable processed AnnData output: a canonical processed.h5ad is produced with standardized scRNA contract fields, layers["counts"], adata.raw, QC metrics in .obs, marker flags in .var, and OmicsClaw metadata in .uns
Notebook-friendly reproducibility: report, structured result JSON, reproducibility shell command, pinned requirements bundle, README, and analysis notebook
Input Formats
The current wrapper uses skills.singlecell._lib.io.smart_load(...).
Format
Extension / form
Current wrapper support
Notes
AnnData
.h5ad
yes
preferred path
10x HDF5
.h5
yes
delegated to shared single-cell loader
Loom
.loom
yes
delegated to shared single-cell loader
Delimited matrix
.csv, .tsv
yes
interpreted through the shared count-matrix loader
10x directory
directory
yes
delegated to the shared 10x importer
Demo
--demo
yes
PBMC3k local/example fallback
Input Expectations
The most reliable input is a raw-count-like matrix available in adata.layers["counts"], aligned adata.raw, or adata.X.
If explicit counts are not in adata.X, the wrapper now auto-selects the best count-like source before QC.
Gene names should follow the selected species convention closely enough for mitochondrial and ribosomal prefix detection to work; when they do not, the run continues with warnings instead of faking precision.
Workflow
Load: read input data with the shared single-cell loader or demo data.
Preflight: check whether a count-like matrix exists, recommend sc-standardize-input when provenance is unclear, and warn honestly about species-dependent gene naming.
Prepare counts and gene IDs: choose the best available count-like source plus the best gene-symbol column for MT / ribosomal tagging.
Calculate metrics: run Scanpy QC metric calculation and add log-transformed helper columns.
Render standard gallery: generate the default OmicsClaw QC gallery under figures/ through the shared skills/singlecell/_lib/viz layer.
Export figure data and tables: write stable CSV exports under figure_data/ and tables/.
Write outputs: save processed.h5ad, report.md, result.json, README, notebook, and reproducibility bundle.
wrapper-level control for MT / ribosomal gene-prefix detection
Parameter Design Notes
--species is the only public extra flag because the current wrapper exposes
a single QC path and does not surface thresholding knobs here.
calculate_ribo=True is fixed in the current implementation and is not a
public CLI parameter.
Do not present sc-qc as if users were choosing among multiple QC
algorithms today. The main decision in current OmicsClaw is how to interpret
the output, not how to configure many QC backends.
Algorithm / Methodology
Implemented Method: qc_metrics
Current OmicsClaw sc-qc uses Scanpy QC metric calculation with
species-specific feature tagging.
Species-aware feature tagging
human: mitochondrial genes start with MT-; ribosomal genes match ^RP[SL]
mouse: mitochondrial genes start with mt-; ribosomal genes match ^Rp[sl]
smart_load(...) is now loader-only: it reads the object and records a minimal input contract.
User-facing standardization advice is emitted by shared preflight, not by the loader.
prepare_count_like_adata(...) then selects layers["counts"] → aligned adata.raw → count-like adata.X in that order.
The saved processed.h5ad declares matrix semantics explicitly in adata.uns["omicsclaw_matrix_contract"]: here adata.X is raw count-like, adata.layers["counts"] is the canonical raw layer, and adata.raw is a count-like snapshot.
If no mitochondrial or ribosomal genes match the selected species convention, the run continues with warnings and counts-based QC metrics remain available.
Guaranteed Metric Columns After Success
The saved processed.h5ad is expected to contain at least:
top-level summary values such as n_cells, n_genes, median_genes, and median_counts
data.params records replayable public CLI parameters.
data.effective_params records the actual runtime configuration, including
fixed wrapper behavior.
Visualization Contract
sc-qc treats Python plots as the standard analysis gallery. The current
recipe roles are:
overview: QC violin plots
diagnostic: QC scatter plots and histograms
supporting: highest expressed genes panel
figure_data/ is the stable hand-off layer for downstream custom plotting,
including future R-side visualization or user-authored beautification scripts.
Example Queries
"Calculate QC metrics for this scRNA-seq dataset"
"Show me mitochondrial percentage and genes-per-cell distributions"
"Generate QC violin and scatter plots before filtering"
"Run single-cell QC and export the per-cell QC table"
Dependencies
Required core packages:
scanpy
anndata
numpy
pandas
matplotlib
Notebook export additionally depends on the standard OmicsClaw notebook helper
stack when available.
Safety And Guardrails
This skill is diagnostic only and does not remove cells or genes.
Species choice affects mitochondrial / ribosomal pattern matching and should
be stated explicitly before the run.
If gene symbols do not follow expected human or mouse prefixes, explain that
percentage estimates may be incomplete.
For short execution guardrails, see
knowledge_base/knowhows/KH-sc-qc-guardrails.md.
For longer method and interpretation guidance, see
knowledge_base/skill-guides/singlecell/sc-qc.md.
Workflow Position
Upstream step: Input should come from sc-count, sc-multi-count, or sc-standardize-input
Usual next step: sc-filter to remove low-quality cells based on the QC metrics visualized here
Optional: Run sc-doublet-detection before or after filtering
