Christian Panse
Community@cpanse · 47N 008E
proteome informatics; ms comp; visualization; maps; code; reproducible research @[email protected]
Agent Skills by Christian Panse
Showing 37 vetted skills indexed across 1 GitHub repositories.
cpansebfabric-query
Query and trace lineage of projects, samples, and datasets in B-Fabric LIMS.
cpansebfabric-tools
Registers datasets and queries project metadata in B-Fabric LIMS via btools CLI and Python API.
cpanseskill-audit
Lints SKILL.md files and scripts for credentials, broken references, and convention violations.
cpansefgcz-context
Provides canonical institutional context for FGCZ infrastructure, tools, and conventions.
cpansemulti-llm-review
Orchestrates parallel multi-LLM reviews of fgcz-skills plugins and aggregates findings by agreement strength.
cpansesample2barcode-generation
Generate Sample2Barcode CSV files for CellRanger multi demultiplexing on FGCZ SUSHI infrastructure.
cpansemixcr-analysis
Analyze bulk TCR and BCR repertoires from amplicon FASTQ data using MiXCR.
cpansedraugr-demultiplexing
Automates and troubleshoots sequencing run demultiplexing via draugr, B-Fabric, and bcl2fastq.
cpansegenome-reference-build
Build and deploy genome references on FGCZ infrastructure using ezRun conventions.
cpansespaceranger-fgcz
Runs SpaceRanger for Visium and VisiumHD spatial transcriptomics on FGCZ SUSHI infrastructure.
cpansenextflow-development
Run nf-core pipelines for RNA-seq, WGS/WES, and ATAC-seq analysis on local or GEO/SRA sequencing data.
cpanserapids-gpu-analysis
Accelerate single-cell and spatial transcriptomics analysis on NVIDIA GPUs using rapids_singlecell.
cpansecytetype-annotation
Annotate cell types in clustered Seurat objects using the CyteTypeR API.
cpansescvi-tools
Train deep generative models for single-cell integration, annotation, and multi-modal analysis with scvi-tools.
cpanserctd-py
Runs GPU-accelerated RCTD cell type deconvolution on spatial transcriptomics data via Python.
cpansescevan-analysis
Identifies malignant cells in scRNA-seq data via CNV analysis with SCEVAN on FGCZ infrastructure.
cpanseseurat-analysis
Analyze single-cell and spatial transcriptomics data with Seurat v5 on FGCZ infrastructure.
cpansecytotrace2-analysis
Predict cellular potency and differentiation states from scRNA-seq Seurat objects using CytoTRACE2.
cpansexenium-ccf-registration
Registers 10x Xenium spatial transcriptomics sections to the Allen Brain CCFv3 atlas using STalign LDDMM alignment.
cpanseslingshot-trajectory
Infer differentiation trajectories and pseudotime from Seurat v5 single-cell data using Slingshot.
cpanseinsitucnv-analysis
Detect copy number variations and tumor subclones in Xenium spatial transcriptomics data using infercnvpy.
cpansescomatic-analysis
Detect somatic mutations in single-cell RNA-seq and ATAC-seq data using the SComatic pipeline.
cpanseclusterprofiler-pathways
Performs GO, KEGG, and GSEA enrichment analysis on single-cell DEG lists using clusterProfiler.
cpansecellchat-analysis
Infer and compare cell-cell communication networks from single-cell RNA-seq data using CellChat.
Frequently Asked Questions About Christian Panse
FAQPage SchemaWhat tasks can I accomplish with cpanse's FGCZ skills?▼
You can process 10x Genomics data with CellRanger, run nf-core pipelines, perform Seurat/scVI/RCTD single-cell and spatial analysis, detect CNVs and somatic mutations, query B-Fabric LIMS for lineage, submit SUSHI jobs, deploy ShinyProxy apps, and render self-correcting R Markdown reports.
Who are these skills designed for?▼
Bioinformaticians, computational biologists, and core-facility staff at FGCZ or similar genomics centers. Personas include single-cell analysts running Seurat/scvi-tools, spatial transcriptomics researchers using Visium/Xenium, LIMS administrators managing B-Fabric metadata, and developers building SUSHI apps with ezRun and Ruby on Rails.
How do the skills run in practice on FGCZ infrastructure?▼
Skills execute on FGCZ's SLURM cluster using Lmod modules, SBATCH GPU templates for L40S nodes, and pixi environments. Jobs are submitted via SUSHI apps or sushi_fabric commands, datasets flow through B-Fabric and gStore, and reports render as R Markdown with automated retry-and-validate loops.
What prerequisites and dependencies are required?▼
Access to FGCZ infrastructure: B-Fabric credentials, gitlab.bfabric.org PAT authentication, SLURM compute nodes, and Lmod modules for R, Python, and CellRanger. Specific skills require Seurat v5, scvi-tools, infercnvpy, spacexr, MiXCR, or NVIDIA GPUs; genome reference builds must run on fgcz-r-029.
How is quality and consistency enforced across the skill marketplace?▼
The skill-audit linter deterministically detects plaintext credentials, personal paths, broken references, and version drift in CI or pre-commit hooks. The multi-llm-review skill then runs parallel Claude, Codex, and Cursor reviews with per-finding agreement scoring to catch remaining semantic issues.