Data & ReproBioinformatics & GenomicsFreedomIntelligence/OpenClaw-Medical-SkillsData & Reproduction

bio-imaging-mass-cytometry-quality-metrics

Maintainer FreedomIntelligence · Last updated April 1, 2026

Quality metrics for IMC data including signal-to-noise, channel correlation, tissue integrity, and acquisition QC. Use when assessing data quality before analysis or troubleshooting problematic acquisitions.

OpenClawNanoClawAnalysisReproductionbio-imaging-mass-cytometry-quality-metrics🧬 bioinformatics (gptomics bio-* suite)bioinformatics — immunoinformatics & flow cytometryquality

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-imaging-mass-cytometry-quality-metrics

Maintainer: FreedomIntelligence
License: MIT
Last updated: April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

Python: numpy/scipy for SNR calculation and channel correlation analysis.
Assess quality of my IMC acquisition" → Evaluate IMC data quality through signal-to-noise ratios, channel correlations, tissue integrity scores, and acquisition-specific QC metrics. Python: numpy/scipy for SNR calculation and channel correlation analysis.

Source Doc

Excerpt From SKILL.md

Batch QC Summary

Goal: Generate a consolidated quality report across all acquisitions in a batch to identify samples requiring re-acquisition or exclusion.

Approach: For each image, compute SNR, tissue integrity, segmentation metrics, and artifact counts, then aggregate into a summary table with pass/fail calls based on combined threshold criteria.