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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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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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Recent Posts
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- How to Analyze Single-Cell RNA-seq Data — Complete Beginner’s Guide Part 17: Infer Signaling Pathway Activity with decoupleR and PROGENy
- How to Analyze Single-Cell RNA-seq Data — Complete Beginner’s Guide Part 16: Build Gene Regulatory Networks with decoupleR and CollecTRI
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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