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How to Analyze Single-Cell ATAC-seq Data: A Complete Beginner’s Guide Part 4: Cell Type Identification
You have 30,571 cells sorted into 17 clusters. Every cluster is a real group of cells that share an open-chromatin landscape. And not one of them has a name. That is where Part 3 left off, and it is where every scATAC-seq analysis stalls if you stop there. “Cluster 4 is more accessible than cluster…
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How to Analyze Single-Cell ATAC-seq Data — A Complete Beginner’s Guide Part 3: Integration and Clustering
Build a consensus peak set, normalize sparse chromatin with TF-IDF, correct batch effects with Harmony and reciprocal LSI, and find biologically meaningful clusters Prerequisites: This tutorial assumes you have completed Part 1: From FASTQ to Peaks and Part 2: Thorough Quality Control with Signac. You should have the four <sample>_qc.rds objects Part 2 produced, the…
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How to Analyze Single-Cell ATAC-seq Data — A Complete Beginner’s Guide Part 2: Thorough Quality Control with Signac
Separate real nuclei from empty droplets, dead cells, doublets, and artifacts using TSS enrichment, nucleosome signal, FRiP, blacklist ratio, and AMULET. In Part 1 you took raw FASTQ files from a real COVID-19 case/control cohort, ran cellranger-atac count, and ended up with a fragments file, a set of peaks, and a peak-by-barcode matrix. Cell Ranger…
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How to Analyze Single-Cell ATAC-seq Data — A Complete Beginner’s Guide Part 1: From FASTQ to Peaks
Learn what chromatin accessibility tells you, then take a real 10x Genomics scATAC-seq dataset from raw FASTQ files all the way to a peak-by-cell matrix with Cell Ranger ATAC and MACS3. Prerequisites: Comfort on the Linux command line (navigating folders, running commands) and roughly 100 GB of free disk space. No prior epigenomics or programming…
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How to Choose the Best Genome Aligner for a Specific NGS Dataset — A Beginner’s Guide to Read Mapping Tools
From short-read DNA to single-cell and long-read RNA — match your sequencing experiment to the right aligner, and learn exactly how to install and run it Every next-generation sequencing (NGS) analysis begins with the same deceptively simple question: where did each of my millions of short DNA or RNA reads come from in the genome?…
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How to Set Up a Bulk RNA-seq Pipeline on an HPC Cluster — A Complete Beginner’s Guide to Nextflow and nf-core/rnaseq
From raw FASTQ files to a gene count matrix — let Nextflow orchestrate hundreds of SLURM jobs for you, with full reproducibility and zero manual bookkeeping If you have RNA-seq FASTQ files sitting on your HPC cluster, you face a choice. You can write a separate SLURM submission script for every sample and every processing…
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How to Choose Your scRNA-seq QC Tools (Part2-2): SoupX vs DecontX and DoubletFinder vs scDblFinder
A decision-focused quick tip for picking the right ambient RNA correction and doublet detection tools — run side by side on real 10x PBMC data Introduction: Same Job, Different Tools In Part 2 of this series, we built a full quality control (QC) workflow that corrected ambient RNA contamination and removed doublets before any downstream…
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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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Categories
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- chromatin accessibility (14)
- Database (4)
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Recent Posts
- How to Analyze Single-Cell ATAC-seq Data: A Complete Beginner’s Guide Part 4: Cell Type Identification
- How to Analyze Single-Cell ATAC-seq Data — A Complete Beginner’s Guide Part 3: Integration and Clustering
- How to Analyze Single-Cell ATAC-seq Data — A Complete Beginner’s Guide Part 2: Thorough Quality Control with Signac
- How to Analyze Single-Cell ATAC-seq Data — A Complete Beginner’s Guide Part 1: From FASTQ to Peaks
Tags
Alternative Splicing Analysis ATAC-seq BAM ChIP-seq chromatin accessibility DESeq2 Differential Expression edgeR FASTQ GATK Mutect2 gene expression GSE282769 heatmap HOMER HPC Isoform limma MACS2 miRNA miRNA-seq MSigDB Normalization peak calling Pixi bioinformatics environment RNA-seq SLURM somatic mutations Transcript VCF whole genome sequencing