Category: Single Cell Sequencing
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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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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 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
- 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