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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, a bioinformatics scientist and engineer working across genomics, proteomics, molecular modeling, HPC, and AI/ML. He has led large‑scale multi‑omics platform development at DNANexus and DataXight, and he is the creator of RIVER, a scalable, AI‑ready infrastructure for reproducible biomedical data analysis. Introduction: Why Environment Management Is Critical for
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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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Recent Posts
- How to Analyze Single-Cell RNA-seq Data – Complete Beginner’s Guide Part 7-2: Trajectory Analysis Using Slingshot
- 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
- How to Analyze Single-Cell RNA-seq Data – Complete Beginner’s Guide Part 7: Trajectory and Pseudotime Analysis Using Monocle 3
- How to Convert BAM Files Back to FASTQ Files: A Practical Guide for NGS Analysis
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Alternative Splicing Analysis ATAC-seq BAM cancer genomics 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 somatic mutations Transcript VCF whole genome sequencing