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...
by Biostatsquid | Jan 23, 2023 | Learning, RNAseq, scRNAseq, Statistics
An overview of pathway enrichment analysis and how you can use it for your differential gene expression analysis data. In this post, you will find pathway enrichment analysis explained in a simple way with examples. I will try to give you a simple and practical...
