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 | Apr 23, 2025 | Learning, Statistics
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...
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...
