Which R packages does this skill support for RNA-seq?

The skill provides implementation patterns for DESeq2, edgeR, and limma-voom, covering the most common statistical methods for differential expression.

Does it provide tools for data visualization?

Absolutely. It includes code for generating volcano plots (EnhancedVolcano), complex heatmaps (ComplexHeatmap), MA plots, and PCA visualizations.

How does this skill handle biological pathway analysis?

It utilizes the clusterProfiler package to perform Gene Ontology (GO) enrichment, KEGG pathway analysis, and GSEA (Gene Set Enrichment Analysis).

Can I use this skill for single-cell RNA-seq data?

Yes, it includes a comprehensive Seurat workflow covering QC, normalization, dimensionality reduction (PCA/UMAP), and clustering.

Does the skill address technical noise like batch effects?

Yes, it includes methods for batch effect correction using ComBat (sva package) and limma's removeBatchEffect function.

Genomics & Bioinformatics Analysis

Name: Genomics & Bioinformatics Analysis
Author: choxos

bychoxos

Data Science & ML

Performs comprehensive genomics and bioinformatics statistical analysis using R and Bioconductor packages.

About

This skill provides a structured framework for conducting advanced genomic research, offering standardized workflows for differential expression analysis, single-cell RNA-seq, and pathway enrichment. It leverages industry-standard Bioconductor packages such as DESeq2, Seurat, and clusterProfiler to help researchers move from raw count matrices to biological insights. Whether you are performing GWAS, correcting for batch effects with ComBat, or generating publication-ready visualizations like volcano plots and complex heatmaps, this skill provides the implementation patterns needed for rigorous bioinformatic pipelines.

Key Features

Single-cell RNA-seq workflows including clustering and UMAP with Seurat
Microarray preprocessing and batch effect correction techniques
Functional enrichment analysis for GO, KEGG, and Gene Set Enrichment (GSEA)
Differential expression analysis using DESeq2, edgeR, and limma-voom
Publication-quality visualization for heatmaps, volcano plots, and PCA
0 GitHub stars

Use Cases

Identifying biomarkers and differentially expressed genes in RNA-seq studies
Analyzing cell-type specific expression patterns in single-cell datasets
Interpreting biological pathways and gene ontologies from experimental results

About

Key Features

Single-cell RNA-seq workflows including clustering and UMAP with Seurat
Microarray preprocessing and batch effect correction techniques
Functional enrichment analysis for GO, KEGG, and Gene Set Enrichment (GSEA)
Differential expression analysis using DESeq2, edgeR, and limma-voom
Publication-quality visualization for heatmaps, volcano plots, and PCA
0 GitHub stars

Use Cases

Identifying biomarkers and differentially expressed genes in RNA-seq studies
Analyzing cell-type specific expression patterns in single-cell datasets
Interpreting biological pathways and gene ontologies from experimental results