Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.
WHAT YOU BECOME
Perfect for these scenarios
Design eye-catching posters for conferences, festivals, or community events
Generate unique digital art pieces for personal or commercial use
Visualize complex data and statistics into engaging infographics
Create promotional visuals for social media, flyers, or advertisements
MEASURED GAIN
Proven benefits and measurable impact
Produce professional designs in minutes, not hours or days
Eliminate expenses for designers or stock imagery subscriptions
Guaranteed unique creations avoiding all copyright issues
WHAT YOU GET
Files, tags and the three-step install
Tip: Read the documentation and the code before first use, so you know what it does and which permissions it needs.
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This skill should be used when working with annotated data matrices in Python, particularly for single-cell genomics analysis, managing experimental measurements with metadata, or handling large-scale biological datasets. Use when tasks involve AnnData objects, h5ad files, single-cell RNA-seq data, or integration with scanpy/scverse tools.