数据与复现生物信息与基因组学FreedomIntelligence/OpenClaw-Medical-Skills数据与复现

bio-imaging-mass-cytometry-quality-metrics

维护者 FreedomIntelligence · 最近更新 2026年4月1日

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.

OpenClawNanoClaw分析处理复现实验bio-imaging-mass-cytometry-quality-metrics🧬 bioinformatics (gptomics bio-* suite)bioinformatics — immunoinformatics & flow cytometryquality

原始来源

FreedomIntelligence/OpenClaw-Medical-Skills

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

维护者: FreedomIntelligence
许可: MIT
最近更新: 2026年4月1日

技能摘要

来自 SKILL.md 的关键信息

2 min

核心说明

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

原始文档

SKILL.md 摘录

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.