How to add boxplots or density plots side-by-side a scatterplot: a single cell case study

introduce ggside using single cell data The ggside R package provides a new way to visualize data by combining the flexibility of ggplot2 with the power of side-by-side plots. We will use a single cell dataset to demonstrate its usage. ggside allows users to create side-by-side plots of multiple variables, such as gene expression, cell type, and experimental conditions. This can be helpful for identifying patterns and trends in scRNA-seq data that would be difficult to see in individual plots.

How to deal with overplotting without being fooled

Sign up for my newsletter to not miss a post like this The problem Let me be clear, when you have gazillions of data points in a scatter plot, you want to deal with the overplotting to avoid drawing misleading conclusions. Let’s start with a single-cell example. Load the libraries: library(dplyr) library(Seurat) library(patchwork) library(ggplot2) library(ComplexHeatmap) library(SeuratData) set.seed(1234) prepare the data data("pbmc3k") pbmc3k #> An object of class Seurat #> 13714 features across 2700 samples within 1 assay #> Active assay: RNA (13714 features, 0 variable features) ## routine processing pbmc3k<- pbmc3k %>% NormalizeData(normalization.