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LLMs-Universal-Life-Science-and-Clinical-Skills- bioinformatics-singlecell

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.

install
source · Clone the upstream repo
git clone https://github.com/mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills- "$T" && mkdir -p ~/.claude/skills && cp -r "$T/Skills/User_Collections/Babu/bioinformatics-singlecell" ~/.claude/skills/mdbabumiamssm-llms-universal-life-science-and-clinical-skills-bioinformatics-sin && rm -rf "$T"
manifest: Skills/User_Collections/Babu/bioinformatics-singlecell/SKILL.md
source content

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.