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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
Discover how genes work together inside specific cell types — and how those cooperative programs shift between disease and treatment Introduction: Gene Co-expression Networks in Single-Cell Data What We Have Learned So Far If you have followed Parts 1-11 of this tutorial series, you have built a comprehensive picture of the PBMC periodontitis dataset (GSE174609):
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How to Analyze Single-Cell RNA-seq Data — Complete Beginner’s Guide Part 11: Copy Number Variation Analysis Using CopyKAT
Learn how to detect tumor cells, infer chromosomal copy number changes, and uncover subclonal structure directly from single-cell RNA-seq data — no matched DNA sequencing required Introduction: Reading the Cancer Genome Through Gene Expression What Is Copy Number Variation and Why Does It Matter in Cancer? If you have followed this tutorial series, you have
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No More Command-Line Only: Run Jupyter Lab, RStudio, and VS Code Interactively in Your Browser on Any HPC Cluster with Pixi
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. You have your scRNA-seq environment set
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How to Analyze Single-Cell RNA-seq Data – Complete Beginner’s Guide Part 10: Cell-Cell Communication Analysis Using NicheNet
Introduction: Taking Cell-Cell Communication Analysis to the Next Level Picking Up Where Part 9 Left Off In Part 9 of this series, we used CellChat to map the full landscape of immune cell communication in our periodontitis dataset. CellChat answered a sweeping question: Who is talking to whom, through which signaling pathways, and how does
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How to Analyze Cell-Cell Communication in Single-Cell RNA-seq Data — Complete Beginner’s Guide Part 9: CellChat Analysis
Introduction: Understanding How Cells Communicate What Is Cell-Cell Communication Analysis? If you’ve followed Parts 1–8 of this tutorial series, you’ve already accomplished a great deal: You now know who the cells are. You know what genes they express and how they change between conditions. But there’s a dimension of biology we haven’t explored yet: how
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How to Analyze Single-Cell RNA-seq Data – Complete Beginner’s Guide Part 7-2: Trajectory Analysis Using Slingshot
Introduction: Understanding Alternative Trajectory Methods If you completed Part 7 of this tutorial series, you learned trajectory analysis using Monocle 3. We explored how to: But here’s an important principle in computational biology: No single method is perfect for every dataset or biological question. Different trajectory inference tools make different assumptions, use different algorithms, and
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How to Analyze Single-Cell RNA-seq Data from Patient-Derived Xenograft (PDX) Models — Complete Beginner’s Guide Part 8: Processing Human-Mouse Mixed Samples
Introduction: What Makes PDX Single-Cell Data Unique? What Are Patient-Derived Xenograft (PDX) Models? If you’ve followed Parts 1–7 of this series, you’ve been working with single-species scRNA-seq data — cells from one organism, aligned to one reference genome. Part 8 introduces a fundamentally different type of sample: Patient-Derived Xenograft (PDX) models. In a PDX experiment,
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How to Analyze Single-Cell RNA-seq Data – Complete Beginner’s Guide Part 7: Trajectory and Pseudotime Analysis Using Monocle 3
Introduction: Understanding Cell State Transitions What Is Trajectory Analysis and Why Do We Need It? If you’ve completed Parts 1-6 of this tutorial series, you’ve successfully: But here’s a fundamental limitation of clustering: cells don’t exist in discrete categories. The biological reality: The computational challenge: Clustering algorithms force cells into discrete groups, creating artificial boundaries
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How to Convert BAM Files Back to FASTQ Files: A Practical Guide for NGS Analysis
Introduction: When and Why You Need BAM-to-FASTQ Conversion The NGS Data Conversion Challenge In next-generation sequencing (NGS) analysis, you’ll encounter data in different formats depending on where you are in your workflow. Sometimes you need to convert between these formats, particularly from BAM (aligned reads) back to FASTQ (raw sequencing reads). Why Do Data Repositories
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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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Recent Posts
- 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
- How to Analyze Single-Cell RNA-seq Data – Complete Beginner’s Guide Part 10: Cell-Cell Communication Analysis Using NicheNet
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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