Is my genomic data sent to the cloud?

No, this skill is designed for local-first processing, meaning all bioinformatics computations and data handling occur entirely on your local machine.

Does this skill support multi-factor experimental designs?

Yes, you can specify complex formulas such as '~ batch + condition' to account for batch effects or multiple experimental variables during analysis.

What input formats are required for the RNA-seq DE skill?

The skill requires a count matrix (CSV/TSV) where rows are genes and columns are samples, plus a corresponding metadata table containing sample IDs and experimental conditions.

Can I use this for single-cell data?

While optimized for bulk RNA-seq, it is fully compatible with pseudo-bulk count matrices generated from single-cell RNA-seq pipelines.

RNA-seq Differential Expression Analysis

Name: RNA-seq Differential Expression Analysis
Author: ClawBio

byClawBio

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753

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Ciencia de Datos y ML

Performs end-to-end differential expression analysis for bulk and pseudo-bulk RNA-seq data with integrated visualization.

This skill automates the bioinformatics workflow for identifying differentially expressed genes from transcriptomics count matrices. It streamlines the entire process from input validation and quality control (QC) to normalization and statistical testing using standardized formulas and contrasts. Designed for researchers and computational biologists, it generates publication-ready figures including PCA, Volcano, and MA plots, alongside a complete reproducibility package containing environment details and checksums, all while ensuring your data remains processed locally.

Características Principales

01Automated QC and low-count filtering for data integrity

02Comprehensive reporting with Volcano and MA plots

03Interactive PCA visualization for sample clustering and batch effect detection

04753 GitHub stars

05Flexible contrast testing using DESeq2-style formulas

06Built-in reproducibility tracking with checksums and environment files

Casos de Uso

01Generating standardized bioinformatics reports and visualizations for research publications

02Analyzing pseudo-bulk counts from single-cell data for cell-type-specific differential expression

03Identifying significant biomarkers between treatment and control groups in bulk RNA-seq

Características Principales

01Automated QC and low-count filtering for data integrity

02Comprehensive reporting with Volcano and MA plots

03Interactive PCA visualization for sample clustering and batch effect detection

04753 GitHub stars

05Flexible contrast testing using DESeq2-style formulas

06Built-in reproducibility tracking with checksums and environment files

Casos de Uso

01Generating standardized bioinformatics reports and visualizations for research publications

02Analyzing pseudo-bulk counts from single-cell data for cell-type-specific differential expression

03Identifying significant biomarkers between treatment and control groups in bulk RNA-seq