Why do boxplots fail for single-cell data?

Many genes in single-cell datasets have over 75% zero values. This causes the first quartile, median, and third quartile to all equal zero, which collapses the visual box into a single flat line.

Does this skill work with Python?

Yes, it is specifically designed for Python environments using scientific libraries like Matplotlib, NumPy, and SciPy.

Can I still use boxplots for sparse data?

Yes, but the skill recommends calculating Y-axis limits based on whisker extent rather than data maximums to prevent outliers from compressing the primary visualization.

When should I use bar plots instead of boxplots?

Bar plots showing mean ± SEM are recommended when more than 50% of your data points are zeros, as the mean remains informative even when the median is zero.

How does this skill handle spatial expression colors?

It suggests using color maps like 'YlOrRd' (Yellow-Orange-Red) instead of 'Reds' so that low-expression values remain visible against white backgrounds.

Sparse Expression Visualization

Name: Sparse Expression Visualization
Author: smith6jt-cop

bysmith6jt-cop

Ciencia de Datos y ML

Optimizes the visualization of sparse single-cell gene expression data by implementing alternative plotting patterns that prevent boxplot collapse.

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This skill provides specialized patterns for visualizing high-sparsity datasets, which are particularly common in single-cell RNA sequencing (scRNA-seq) analysis. When traditional boxplots fail due to a high frequency of zeros—resulting in misleading collapsed lines at the origin—this skill guides Claude to implement effective alternatives such as bar plots with mean and SEM error bars. It includes logic for whisker-based Y-axis scaling, optimized color maps for spatial expression, and specific strategies to prevent outlier compression, ensuring scientific data is represented accurately and legibly for research and publication.

Características Principales

Best practices for handling zero-heavy scRNA-seq data
Implementation of Bar Plots with Mean ± SEM error bars
Whisker-based Y-axis scaling to preserve visual detail
0 GitHub stars
Automatic detection of collapsed boxplots in sparse datasets
Optimized color mapping for spatial gene expression

Casos de Uso

Correcting misleading flat plots where median and IQR are zero
Generating publication-quality scientific figures for sparse biological data
Visualizing gene expression levels across diverse cell types in scRNA-seq studies

Acerca de

Características Principales

Best practices for handling zero-heavy scRNA-seq data
Implementation of Bar Plots with Mean ± SEM error bars
Whisker-based Y-axis scaling to preserve visual detail
0 GitHub stars
Automatic detection of collapsed boxplots in sparse datasets
Optimized color mapping for spatial gene expression

Casos de Uso

Correcting misleading flat plots where median and IQR are zero
Generating publication-quality scientific figures for sparse biological data
Visualizing gene expression levels across diverse cell types in scRNA-seq studies