🌱 Welcome to the RNA-Seq Q&A Guide

This guide is your hands-on companion for learning and applying RNA sequencing (RNA-Seq) data analysis — one question at a time.

You’ll explore each step of the RNA-Seq pipeline using real tools, reproducible workflows, and well-commented code. From quality control and quantification to differential expression and biological interpretation, this guide shows how scripting, statistics, and bioinformatics come together in practice.

Whether you’re a student, researcher, or self-taught enthusiast, you’ll gain confidence using Python, R, shell scripting, and reproducible workflows — including tools like DESeq2, Salmon, edgeR, FastQC, Snakemake, and more.

Each Q&A includes a clear explanation, relevant code in both Python and R when applicable, and builds toward real-world problem solving. You’re not just learning RNA-Seq — you’re learning to think like a modern data-driven bioinformatician.

🌐 The CDI Learning Path

This guide is part of the Complex Data Insights (CDI) learning system — a fully free and open-source project licensed under the MIT License.

CDI breaks down complex topics into four progressive layers, designed to be explored individually or as an integrated journey:

  • 🔍 EDA (Exploratory Data Analysis)
    Understand your data — explore its structure, patterns, and quirks.

  • 📊 VIZ (Visualization)
    Communicate findings through clear and compelling visuals.

  • 📐 STATS (Statistical Analysis)
    Test hypotheses and quantify uncertainty using sound statistical methods.

  • 🤖 ML (Machine Learning)
    Build models to predict, classify, and uncover deeper insights.

CDI helps you grow — one Q&A at a time.