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
Comparing multiple groups: Kruskal-Wallis test in R
When working with biological data, we often want to compare measurements across multiple groups. However, these measurements aren't always normally distributed. In such cases, non-parametric methods like the Kruskal-Wallis test and Dunn’s post-hoc test are ideal...
Understanding Seurat objects – simply explained!
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 particular the new Seurat...
SCTransform – simple and intuitive explanation
SCTransform (Single-Cell Transform) is a normalization method primarily used in scRNA-seq data analysis. It was developed to address limitations in standard normalization approaches when dealing with single-cell data. You can check how to apply SCTransform on your...
Why do genes with the highest logFC not have the lowest p-value?
That's a really good and very common question in differential gene expression analysis! It feels intuitive that the larger the difference in expression (log fold change, or logFC), the more significant it should be (i.e., the smaller the p-value), but that’s not...
PCA vs UMAP vs t-SNE
Understanding similarities and differences between dimensionality reduction algorithms: PCA, t-SNE and UMAPPCA, t-SNE, UMAP... you've probably heard about all these dimensionality reduction methods. In this series of blogposts, we'll cover the similarities and...
Easy UMAP – explained with an example
A short but simple explanation of UMAP- 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 between them, easily...
Comparing multiple groups: Kruskal-Wallis test in R
When working with biological data, we often want to compare measurements across multiple groups. However, these measurements aren't always normally distributed. In such cases, non-parametric methods like the Kruskal-Wallis test and Dunn’s post-hoc test are ideal...
Understanding Seurat objects – simply explained!
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 particular the new Seurat...
SCTransform – simple and intuitive explanation
SCTransform (Single-Cell Transform) is a normalization method primarily used in scRNA-seq data analysis. It was developed to address limitations in standard normalization approaches when dealing with single-cell data. You can check how to apply SCTransform on your...
Why do genes with the highest logFC not have the lowest p-value?
That's a really good and very common question in differential gene expression analysis! It feels intuitive that the larger the difference in expression (log fold change, or logFC), the more significant it should be (i.e., the smaller the p-value), but that’s not...
PCA vs UMAP vs t-SNE
Understanding similarities and differences between dimensionality reduction algorithms: PCA, t-SNE and UMAPPCA, t-SNE, UMAP... you've probably heard about all these dimensionality reduction methods. In this series of blogposts, we'll cover the similarities and...
Easy UMAP – explained with an example
A short but simple explanation of UMAP- 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 between them, easily...