--> --- name: bio-bioinformatics-singlecell description: Single-cell and multi-omic analysis for hematology, oncology, and translational biology. Use when working with scRNA-seq, CITE-seq, scATAC-seq, multiome, trajectory analysis, batch correction, cell typing, differential expression, or publication-ready figures in Scanpy, scvi-tools, Seurat, or MuData workflows. tool_type: mixed primary_tool: Unknown measurable_outcome: Execute skill workflow successfully with valid output within 15 minut...
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# Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu>
# All Rights Reserved.
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---
name: bio-bioinformatics-singlecell
description: Single-cell and multi-omic analysis for hematology, oncology, and translational
biology. Use when working with scRNA-seq, CITE-seq, scATAC-seq, multiome, trajectory
analysis, batch correction, cell typing, differential expression, or publication-ready
figures in Scanpy, scvi-tools, Seurat, or MuData workflows.
tool_type: mixed
primary_tool: Unknown
measurable_outcome: Execute skill workflow successfully with valid output within 15
minutes.
allowed-tools:
- read_file
- run_shell_command
---
# Single-Cell Analysis
Run practical single-cell analysis workflows with emphasis on QC discipline, interpretable annotations, and reproducible outputs.
## Workflow
1. Confirm assay type, species, reference build, sample design, and expected outputs before touching code.
2. Inspect raw inputs and metadata first; check barcode structure, feature naming, batch labels, and sample-level covariates.
3. Apply assay-appropriate QC thresholds rather than hard-coding generic cutoffs across datasets.
4. Normalize, integrate, cluster, and annotate with methods that match the study design; preserve raw counts when downstream models need them.
5. Separate exploratory clustering from biologic claims; validate major labels with marker genes, orthogonal metadata, or reference mapping.
6. Report thresholds, software versions, random seeds, and the exact objects written to disk.
## Guardrails
- Flag doublets, ambient RNA, batch leakage, and low-complexity samples before interpreting clusters.
- Do not overstate automated annotation; list competing labels when marker support is mixed.
- For disease cohorts, distinguish malignant state, lineage identity, and treatment effect.
- Preserve donor and sample identity through every merge or integration step.
## References
- Read `references/cell_markers.md` for lineage and megakaryocyte markers.
- Read `references/workflow-checklist.md` for a compact end-to-end checklist.
<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->
