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)
生产就绪的显微镜图像分析和定量成像数据技能,用于集落形态测定、细胞计数、荧光定量和成像衍生测量的统计分析。处理 ImageJ/CellProfiler 输出(面积、圆度、强度、细胞计数),执行 Dunnett 检验、Cohen d 效应大小、功效分析、Shapiro-Wilk 正态性检验、双向方差分析、多项式回归、带置信区间的自然样条回归以及比较形态测量。支持 CSV/TSV 测量表、多通道荧光数据、菌落群分析和神经元计数数据集。在分析显微镜测量数据、集落面积/圆度、细胞计数统计、群体分析、共培养比率优化或回答有关成像衍生定量数据的问题时使用。 来源:mims-harvard/tooluniverse。