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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
You already know the decoupleR engine from Part 16. Swap the network, swap one method, and you go from “which transcription factors are active” to “which signaling pathways are active” — in about half the code. In Part 16 you inferred per-cell transcription factor activity with decoupleR and CollecTRI, and the analysis landed on a…
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How to Analyze Single-Cell RNA-seq Data — Complete Beginner’s Guide Part 16: Build Gene Regulatory Networks with decoupleR and CollecTRI
Infer per-cell transcription factor activity from a curated, signed regulatory network — no de novo network learning, no motif databases, just one fast linear model. In Part 15 you learned to redraw the entire scRNA-seq series with cleaner code. Now we return to biology with a new question: which transcription factors are driving each cell…
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How to Analyze Single-Cell RNA-seq Data — Complete Beginner’s Guide Part 15: Better Visualization with scplotter
Recreate every plot from this series with cleaner code and publication-ready output — one function, one line, no patchwork gymnastics. Parts 1 through 14 of this series took the GSE174609 periodontitis PBMC dataset from raw FASTQ files all the way through quality control, integration, clustering, annotation, differential expression, trajectory inference, cell communication, and RNA velocity.…
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How to Analyze Single-Cell RNA-seq Data — Complete Beginner’s Guide Part 14: Cell Fate Probability Analysis with CellRank
From velocity arrows to fate decisions — discover the probability that each pancreatic progenitor cell will become a specific hormone-producing endocrine cell type In Part 13 you used scVelo to attach a directional velocity arrow to every cell — a vector pointing toward where each cell is heading transcriptionally. Velocity arrows tell you the direction…
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How to Analyze Single-Cell RNA-seq Data — Complete Beginner’s Guide Part 13: RNA Velocity Analysis with scVelo
From static snapshots to directional transcriptional dynamics — discover where your cells are going, not just where they are If you have followed Parts 1–12 of this tutorial series, you have built a comprehensive picture of the immune cell landscape in the GSE174609 periodontitis dataset. Every one of these analyses worked with a static snapshot…
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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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Recent Posts
- 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
- How to Analyze Single-Cell RNA-seq Data — Complete Beginner’s Guide Part 15: Better Visualization with scplotter
- How to Analyze Single-Cell RNA-seq Data — Complete Beginner’s Guide Part 14: Cell Fate Probability Analysis with CellRank
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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