Tag: MatchRegionStats background peaks
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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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Recent Posts
- 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
- 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
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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