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 | Apr 24, 2025 | scRNAseq, Statistics
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...
by Biostatsquid | Mar 18, 2025 | Learning, scRNAseq, Statistics
Understanding similarities and differences between dimensionality reduction algorithms: PCA, t-SNE and UMAP PCA, t-SNE, UMAP… you’ve probably heard about all these dimensionality reduction methods. In this series of blogposts, we’ll cover the...
by Biostatsquid | Mar 14, 2025 | Learning, scRNAseq, Statistics
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...
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 | Feb 28, 2025 | Machine learning, scRNAseq, Statistics
A short but simple explanation of PCA – 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...
