Production-ready skill for analyzing microscopy-derived measurement data using pandas, numpy, scipy, statsmodels, and scikit-image. Designed for BixBench imaging questions covering colony morphometry, cell counting, fluorescence quantification, regression modeling, and statistical comparisons.
IMPORTANT: This skill handles complex multi-workflow analysis. Most implementation details have been moved to references/ for progressive disclosure. This document focuses on high-level decision-making and workflow orchestration.
BixBench Coverage: 21 questions across 4 projects (bix-18, bix-19, bix-41, bix-54)
生產就緒的顯微鏡影像分析和定量成像資料技能,用於集落形態測定、細胞計數、螢光定量和成像衍生測量的統計分析。處理 ImageJ/CellProfiler 輸出(面積、圓度、強度、細胞計數),執行 Dunnett 檢定、Cohen d 效應大小、效能分析、Shapiro-Wilk 常態性檢定、雙向變異數分析、多項式迴歸、有置信區間的自然樣條迴歸、比較形態測量。支援 CSV/TSV 測量表、多通道螢光資料、菌落群分析和神經元計數資料集。在分析顯微鏡測量數據、集落面積/圓度、細胞計數統計、群體分析、共培養比率優化或回答有關成像衍生定量數據的問題時使用。 來源:mims-harvard/tooluniverse。