Top 5 Single-cell Genomics Papers of 2021

In the age of Big Data in biology, data science and machine learning have flourished and benefitted from their interdisciplinary application to biology. As a graduate student in this discipline, I read a lot of papers to stay up to date on the literature (and still have a large reading list to catch up on!), and thought I would share what have been some of the best papers I've read this year. In about 80–90% of the single-cell papers you'll encounter, depending on the research question, there will be at least one or two tSNE or UMAP plots to visualize the data they've collected, usually single-cell RNA-sequencing (scRNA-seq) data, where individual cells are profiled for their RNA abundance across the genome. These unsupervised dimensionality reduction methods have been more or less accepted as the status quo for data visualization in the world of single-cell genomics, so it took Academic Twitter by storm this summer when a new preprint boldly challenged that norm, arguing that these methods do little to preserve the latent structure of the data it seeks to convey to our 3D minds. Using the extreme example of preserving equidistant cells in high-dimensional space, and later relaxing it to near-equidistance, they show how tSNE and UMAP distort the orientation of groups of cells with near-equidistance spacing in the original space, clustering them with groups of cells that are evenly spread further apart.