Automate differential gene expression analysis with DESeq2 in Python.
npx skills add https://github.com/galeep/plugin-place --skill pydeseq2-galeep
Please help me install this Agent Skill. Skill: pydeseq2 Source: https://github.com/galeep/plugin-place/tree/main/plugins/sci-bioinformatics-genomics/skills/pydeseq2 Command: npx skills add https://github.com/galeep/plugin-place --skill pydeseq2-galeep
This Skill addresses the challenge of analyzing bulk RNA-seq count data for differential expression, offering a Python-based alternative to R's DESeq2 package.
Run the following Python command to perform a differential expression analysis with DESeq2 on your RNA-seq data:
python scripts/run_deseq2_analysis.py --counts counts.csv --metadata metadata.csv --design "~condition" --contrast condition treated control --output results/
High-intent search queries and answers about installing and using this skill.
You can perform differential gene expression analysis on RNA-seq data in Python by using PyDESeq2 to automate statistical testing across experimental conditions. This Skill handles the workflow from loading count data to identifying differentially expressed genes.
Yes, you can use DESeq2 for RNA-seq differential expression without R by leveraging this Python-based alternative. It provides a Pythonic interface to DESeq2's statistical functionality using PyDESeq2, pandas, and scipy.
To identify differentially expressed genes with DESeq2, you need RNA-seq count data in a CSV file and a corresponding metadata CSV file. These inputs define the gene counts and experimental conditions for the differential expression analysis.
You specify treatment and control groups for bulk RNA-seq differential expression by defining a design formula and contrast arguments in the command line. The design parameter models the condition, while the contrast parameter sets the specific groups to compare.
Yes, PyDESeq2 integrates with anndata and scikit-learn for RNA-seq data processing. The Skill requires anndata for data structure management and scikit-learn alongside numpy and scipy for statistical analysis during differential expression testing.
dralkhAnalyze bulk RNA-seq count data for differential gene expression with PyDESeq2.
tassiovaleAnalyze gene lists or ranked data for enriched biological pathways and gene sets.
Lord1EgyptPerform differential gene expression analysis on bulk RNA-seq count data with PyDESeq2.
galeepIdentify over-represented pathways and gene sets in gene lists.