by Biostatsquid | Apr 1, 2026 | Learning, scRNAseq
One of the most common questions in single-cell RNA sequencing analysis is deceptively straightforward: how many cells express a given gene, and are the expression levels truly comparable across datasets? If you have ever stared at your results wondering whether your...
by Biostatsquid | Dec 14, 2025 | Learning, scRNAseq
Comparing top integration methods for scRNAseq data When we want to combine multiple scRNA-seq datasets to answer bigger questions, we encounter batch effects – unwanted technical variations that arise from differences in how the experiments were performed....
by Biostatsquid | Nov 30, 2025 | Learning, scRNAseq
An overview of the most popular integration methods for single-cell data Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of biology by allowing us to measure gene expression in individual cells rather than bulk tissue samples. This...
by Biostatsquid | Nov 15, 2025 | Learning, RNAseq, scRNAseq, scRNAseq, Tutorials
MSigDB gene sets: easy msigdbr in R MSigDB gene sets: easy msigdbr in R Welcome to this comprehensive guide on MSigDB (Molecular Signatures Database) and the msigdbr R package! If you’ve ever wondered which gene sets to use for your pathway enrichment analysis, or...
by Biostatsquid | Jul 15, 2025 | RNAseq, scRNAseq, Tutorials, Uncategorized
Hey! You’re looking at an old post. Newer version here: clusterProfiler tutorial in R R TUTORIAL: Perform pathway enrichment analysis with clusterProfiler() in R Table of contents 5 Before you start 5 Step-by-step clusterProfiler tutorial 9 Step 0: Set up your...
by Biostatsquid | Apr 27, 2025 | Learning, RNAseq, scRNAseq
Understanding the structure of Seurat objects version 5 – step-by-step simple explanation! If you’ve worked with single-cell RNAseq data, you’ve probably heard about Seurat. In this blogpost, we’ll cover the the Seurat object structure,in...
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