BiostatLEARN
Simple and clear explanations of biostatistics methods, statistical concepts and more!
I try to keep them maths-free and straight to the point, with many examples of biological applications.
Latest posts
Heatmaps for gene expression analysis – simple explanation with an example
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...
Gene Set Enrichment Analysis (GSEA) – simply explained!
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...
Pathway enrichment analysis for DGE – simply explained
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...
Multiple testing correction methods: FDR, q-values vs p-values
A simple explanation of what is multiple testing and how it can negatively affect your data. We will also cover some of the most common multiple testing correction methods.In this post I will try to give you a simple and practical explanation of multiple testing....
Correlation does not imply causation
Simple explanation of what is correlation, positive and negative correlation, and the correlation coefficient r.In this post I will try to give you a simple and practical explanation of correlation. Correlation is one of the most used statistical techniques. However,...
Principal Component Analysis (PCA) simply explained
In this post I will try to give you a simple and practical explanation on what is Principal Component Analysis and how to use it to visualise your biological data. Principal Component Analysis, or PCA, is a widely used technique to visualise multidimensional datasets....
Heatmaps for gene expression analysis – simple explanation with an example
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...
Gene Set Enrichment Analysis (GSEA) – simply explained!
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...
Pathway enrichment analysis for DGE – simply explained
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...
Multiple testing correction methods: FDR, q-values vs p-values
A simple explanation of what is multiple testing and how it can negatively affect your data. We will also cover some of the most common multiple testing correction methods.In this post I will try to give you a simple and practical explanation of multiple testing....
Correlation does not imply causation
Simple explanation of what is correlation, positive and negative correlation, and the correlation coefficient r.In this post I will try to give you a simple and practical explanation of correlation. Correlation is one of the most used statistical techniques. However,...
Principal Component Analysis (PCA) simply explained
In this post I will try to give you a simple and practical explanation on what is Principal Component Analysis and how to use it to visualise your biological data. Principal Component Analysis, or PCA, is a widely used technique to visualise multidimensional datasets....