Tag: single-cell epigenomics beginners guide
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How to Analyze Single-Cell ATAC-seq Data — A Complete Beginner’s Guide Part 7: Genome-Wide Differential TF Activity with chromVAR and limma
Meta description: Learn genome-wide differential transcription factor activity analysis for scATAC-seq: donor pseudobulk, chromVAR motif deviations and limma moderated t-tests, with R code. Stop hand-picking motifs. Score all 633 JASPAR2020 transcription factor motifs in CD14 monocytes with chromVAR, test severe versus healthy at the level of donors with limma, and learn how to read the…
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How to Analyze Single-Cell ATAC-seq Data — A Complete Beginner’s Guide Part 6: Footprinting Analysis
Go beyond “which motifs are enriched” and ask “is the transcription factor actually sitting there?” — run scATAC-seq footprinting two ways, with Signac Footprint() on fragments and TOBIAS on cell-type pseudobulk BAMs, and learn how far each answer can be trusted. Part 5 ended with a list of transcription factor families. In CD14 monocytes, the…
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How to Analyze Single-Cell ATAC-seq Data — A Complete Beginner’s Guide Part 5: Differential Accessibility Analysis
Compare chromatin accessibility between disease and health inside a single cell type — with a single-cell test, a pseudobulk test, motif enrichment, and pathway analysis — and learn why the two tests disagree. Part 4 ended with 30,571 cells carrying biological names: CD14 monocytes, CD8 T effectors, B cells, NK cells. Naming cells is not…
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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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Recent Posts
- How to Analyze Single-Cell ATAC-seq Data — A Complete Beginner’s Guide Part 7: Genome-Wide Differential TF Activity with chromVAR and limma
- How to Analyze Single-Cell ATAC-seq Data — A Complete Beginner’s Guide Part 6: Footprinting Analysis
- How to Analyze Single-Cell ATAC-seq Data — A Complete Beginner’s Guide Part 5: Differential Accessibility Analysis
- How to Analyze Single-Cell ATAC-seq Data: A Complete Beginner’s Guide Part 4: Cell Type Identification
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 single-cell epigenomics beginners guide SLURM somatic mutations Transcript whole genome sequencing