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How to Analyze Circular RNA-seq Data for Absolute Beginners Part 13-2: Advanced CircRNA Detection and Differential Expression with CIRI3
Introduction: Advancing Beyond CIRCexplorer2 with CIRI3 In Part 13 of my RNA-seq tutorial series, we explored circular RNA (circRNA) analysis using CIRCexplorer2, learning how these fascinating non-linear RNA molecules form through back-splicing and play important roles in gene regulation, disease mechanisms, and potential therapeutic applications. While CIRCexplorer2 provides an excellent introduction to circRNA analysis, the
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How to Analyze Single-Cell RNA-seq Data – Complete Beginner’s Guide Part 6: Understanding Seurat and SingleCellExperiment Objects
Introduction: Why Understanding Data Objects Matters The “Black Box” Problem in Single-Cell Analysis If you’ve worked through Parts 1-5 of this tutorial series, you’ve successfully: But here’s what many beginners (and even experienced analysts) struggle with: Where is my data actually stored? How do I access specific information? Why do some functions only work with
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Setting Up Single-Cell RNA-seq Analysis Environment with Pixi: 10x Faster Setup, Zero Version Conflicts
This tutorial is contributed by Giang Nguyen, founder of G Labs, providing consulting, software development, infrastructure engineering, and bioinformatics services to support scalable research and production workflows. He helps teams design, build, and optimize cloud/HPC platforms, develop custom tools and pipelines, and deliver reproducible, production-ready solutions for data-intensive science. Introduction: Why Environment Management Is Critical
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How to Analyze Single-Cell RNA-seq Data – Complete Beginner’s Guide Part 5: Cell Type-Specific Differential Expression, Proportion Testing, and Functional Pathway Analysis
Introduction: From Cell Types to Biological Mechanisms In Parts 1-4 of this tutorial series, we’ve taken scRNA-seq data from raw sequencing reads through quality control, integration, clustering, and cell type annotation. We now have a beautifully annotated dataset where every cell has a biological identity (CD4+ T cells, monocytes, etc.) and metadata linking it to
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How to Analyze Single-Cell RNA-seq Data – Complete Beginner’s Guide Part 4: Cell Type Identification
Introduction: From Clusters to Biological Identities In Part 1, 2, 3 of this tutorial series, we’ve taken our scRNA-seq data from raw FASTQ files through quality control, integration, and clustering. We now have groups of cells that cluster together based on transcriptional similarity—but what are these cells? Cell type identification transforms abstract “Cluster 0, Cluster
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How to Analyze Single-Cell RNA-seq Data – Complete Beginner’s Guide Part 3: Integration and Clustering
Introduction: Why Integration Matters in Multi-Sample scRNA-seq Analysis In Part 1 and Part 2 of this tutorial series, we processed PBMC samples from the GSE174609 dataset through the complete pipeline: from raw FASTQ files to quality-controlled count matrices. Now we face a critical question: How do we analyze multiple samples together to identify cell types
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How to Analyze Single-Cell RNA-seq Data – Complete Beginner’s Guide Part 2: Quality Control and Cell Filtering
Introduction: Learning QC Through a Single-Sample Deep Dive Quality control in single-cell RNA sequencing is complex, with multiple layers of filtering and validation. Before tackling multi-sample experiments, it’s essential to understand the QC workflow thoroughly using a single sample. This focused approach allows you to: IMPORTANT: This QC workflow should be applied independently to each
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How to Analyze Single-Cell RNA-seq Data – Complete Beginner’s Guide Part 1: From FASTQ to Count Matrix
A comprehensive step-by-step tutorial for analyzing 10x Genomics single-cell RNA sequencing data using Cell Ranger Introduction: Understanding Single-Cell RNA Sequencing The revolution in molecular biology has been marked by our ability to zoom in from cell populations to individual cells. This shift reveals hidden heterogeneity that bulk measurements mask. Single-cell RNA sequencing (scRNA-seq) is one
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How to Build Gene Regulatory Networks from RNA-seq Data Using GENIE3 – Complete Step-by-Step Guide For Absolute Beginners
Introduction: Understanding Gene Regulatory Network Inference In the complex choreography of cellular function, transcription factors (TFs) act as master conductors, orchestrating when and where genes are expressed. Understanding which transcription factors regulate which target genes is fundamental to deciphering how cells respond to stimuli, how developmental programs unfold, and how diseases emerge from regulatory dysfunction.
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How to Build Gene Co-expression Networks from RNA-seq Data Using WGCNA – Complete Step-by-Step Guide For Absolute Beginners
Introduction: Understanding Gene Co-expression Networks In the intricate machinery of living cells, genes rarely act in isolation. Instead, they work together in coordinated networks, with groups of genes being co-expressed to carry out specific biological functions. Understanding these relationships is fundamental to deciphering how cells respond to stimuli, how diseases develop, and how we might
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Recent Posts
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- How to Analyze Single-Cell RNA-seq Data – Complete Beginner’s Guide Part 12: Build Gene Co-expression Networks Using hdWGCNA
- How to Analyze Single-Cell RNA-seq Data — Complete Beginner’s Guide Part 11: Copy Number Variation Analysis Using CopyKAT
- No More Command-Line Only: Run Jupyter Lab, RStudio, and VS Code Interactively in Your Browser on Any HPC Cluster with Pixi
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Alternative Splicing Analysis ATAC-seq BAM ChIP-seq chromatin accessibility CNV DESeq2 Differential Expression edgeR FASTQ GATK Mutect2 gene expression heatmap HOMER HPC Isoform limma MACS2 MAF miRNA miRNA-seq MSigDB Normalization peak calling RNA-seq SLURM somatic mutations Transcript VCF whole genome sequencing