by Biostatsquid | Nov 6, 2025 | Learning, RNAseq, Statistics
How to interpret MA plots How to interpret MA plots In this blogpost, we will go over the basics of an MA plot which is a very useful visualisation for genomics and transcriptomics data. We will go over the basics of MA plots and how to interpret them. This is the...
by Biostatsquid | Aug 24, 2025 | Learning, Statistics
How to interpret ANOVA (analysis of variance) easily explained! When you’re working with data and want to determine whether different groups have significantly different averages, simply eyeballing the numbers won’t cut it. That’s where ANOVA (Analysis of...
by Biostatsquid | Jun 24, 2025 | Statistics
Setting thresholds for differential gene expression (DGE) analysis is crucial and depends on several factors. In essence, for a list of genes, we are trying to define what counts as biologically meaningful versus just statistically significant. The question is…...
by Biostatsquid | Jun 6, 2025 | Learning, Statistics, Statistics in R, Tutorials
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...
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...
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