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...
by Biostatsquid | Mar 6, 2025 | Learning, Machine learning, RNAseq, scRNAseq, Statistics
A short but simple explanation of t-SNE – easily explained with an example! PCA, t-SNE, UMAP… you’ve probably heard about all these dimensionality reduction methods. In this series of blogposts, we’ll cover the similarities and differences...
by Biostatsquid | Apr 13, 2023 | Learning, RNAseq
Top tips and resources to perform cell type annotation on scRNAseq data Once you preprocess your single-cell RNA sequencing (scRNAseq) data, it is time for one of the biggest challenges in a standard scRNAseq pipeline: annotating cell types. The scientific community...
by Biostatsquid | Apr 12, 2023 | Learning, RNAseq, scRNAseq, Statistics
In this post, you will learn how to interpret a heatmap for differential gene expression analysis. Find out why heatmaps are a great way of visualising gene expression data with this simple explanation. Let’s dive in! Prefer to listen? Watch my Youtube video on...
by Biostatsquid | Jan 23, 2023 | Learning, RNAseq, scRNAseq, Statistics
What is gene set enrichment analysis and how can you use it to summarise your differential gene expression analysis results? This post will give you a simple and practical explanation of Gene Set Enrichment Analysis, or GSEA for short. You will find out: What is Gene...
