scRNA-seq Workbench

Version 0.4.0 — with knowledge base · research preview.

A single-cell RNA-seq analysis plugin for Codex, Claude Code and DeepSeek Harness. Workbench combines executable analysis tools with a bundled knowledge base covering study design, quality control, scVI, cell annotation and differential expression.

Analysis workflow

Start with your data or a biological question. The agent checks sample relationships, defines which datasets to analyze together, and runs the relevant stages.

Stage	Skill	Main outputs
Study selection and exploration	scrna-research	Relevant studies, experimental context and expression summaries
Sequencing report review	sequencing-report-review	Sequencing metrics and delivery checks
Quality control	scrna-qc	QC plots, filtering records, doublet assessment when applicable and filtered counts
Representation and clustering	scrna-scvi-umap	scVI model, cell-cycle diagnostics, candidate clusters and UMAPs
Cell annotation	scrna-cell-annotation	Marker expression, candidate cell types and human review records
Condition comparison	scrna-condition-de	Donor-level pseudobulk counts and differential-expression results

New representation workflows use scVI with cell-cycle covariates, with or without technical batches. Raw counts and the full retained gene set are preserved.

Several neighbor counts and Leiden resolutions are presented for selection. Annotation follows tissue-specific marker evidence and HPA-guided review, with additional investigation of unresolved populations. Final labels require human review; condition DE requires biological donor replicates and reviewed annotations.

Use scrna-research to plan an analysis, or enter a specific stage when suitable intermediate results already exist.

Bundled knowledge base

The knowledge base defines how the agent groups datasets, chooses parameters, investigates uncertain annotations and reports results. All six Skills point to the relevant sections.

It ships inside the plugin. Synchronization checks prevent outdated copies, and each run records the policy hash with its parameters and software versions. See how agents use the knowledge base.

Get started

1. Install the plugin and dependencies, then connect it to your agent.
2. Run the 400-cell example to check your environment.
3. Follow the research workflow or analyze your own data.

Calculations run in your local or server Python environment. Install requirements-scvi.txt for representation analysis; condition DE has additional dependencies.

Example request:

Analyze these datasets with scRNA-seq Workbench. Read the knowledge base, establish the analysis groups, and run QC and scVI. Show clustering options for my selection and marker-expression figures for annotation review. Save the analysis records and figures in my project directory.

Analysis reports and validation

GSE144024: manual and AI analyses

The study collection covers human fetal liver (FL), yolk sac (YS) and hESC-derived Day0/Day6 cells.

Record	Method and grouping	Results
Manual analysis	scVI; separate FL and YS, joint hESC Day0/Day6	Final submitted annotations, annotated UMAPs, QC report and marker-expression PDFs
AI analysis 1	PCA; all four sources analyzed jointly	Exploratory annotations, marker evidence, composition and sensitivity results
AI analysis 2	PCA; independent FL, YS and hESC analyses	Three cohort reports, UMAPs and expanded marker assessment

The manual record preserves the user's final submitted results. AI annotations remain exploratory and await human review. Both AI runs predate the mandatory-scVI update and have not been rerun with v0.4.0.

Current software checks

Version 0.4.0 passed 42 local tests, including CPU scVI training with and without batches, count preservation, cell-cycle covariate registration, model save/reload and candidate graph outputs. Package checks cover knowledge-base synchronization, Skill entry points and standalone installation files.

These synthetic tests assess execution, not biological annotation accuracy. See the validation record, validation details and CI results. Earlier five-study analyses remain available as historical results.

Development

Development guide · Changelog · Collaboration · Data and publication sources

Code is MIT licensed.