Individual skill
marker-panel-annotation
Assigns cell-type labels to clusters from a protein marker panel (multiplexed imaging such as MIBI, CODEX, IMC and CyCIF; mass and flow cytometry; CITE-seq protein), where the labels are judged against an expert reference. Within-dataset normalisation of cluster summaries, lineage first by positive defining markers, subtype only as far as the panel can determine it, marker-poor clusters assigned by exclusion, class-coverage sanity checks, and two independent annotators with adjudication. Use when the output is one label per cluster and the grader is agreement with an expert; for gating single cells in FCS data use flow-cytometry-analysis, and for transcript-based annotation use scanpy or scvi-tools.
backend/cli/skills/biology/marker-panel-annotation/SKILL.md
Source commit a08e0ad0ad3d6448123039ee4659163eb2e35d99
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- Last checked
- Nov 2, 2026
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- Repository last updated
- Nov 6, 2026
- License
- Apache-2.0
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- agent Repository topic
- ai Repository topic
- bun Repository topic
- cli Repository topic
- llm Repository topic
- ml Repository topic
- open-source Repository topic
- research Repository topic
- science Repository topic
- ai-agent Repository topic
- cell-type-annotation Declared skill tag
- multiplexed-imaging Declared skill tag
- mibi Declared skill tag
- codex Declared skill tag
- imc Declared skill tag
- cytometry Declared skill tag
- immunophenotyping Declared skill tag
- data Derived category
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# Marker-panel cluster annotation
An expert annotating clusters from a marker panel gets two things right that a
first-pass reading gets wrong: the *level* at which each label is stated, and
the clusters that carry almost nothing. Both directions cost equally under an
expert-agreement grader: a subtype the panel cannot support scores like a
wrong lineage, and a broad label where the panel determines the subtype
scores like a miss. Everything below follows from that.
## 1. Summarise within the dataset, never across
- Work one dataset (panel, platform, run) at a time. Intensities are not
comparable across platforms or panels; a threshold or z-score computed on
the pooled data is wrong for every dataset in it.