Filtered blog posts by category.
Single-cell RNA sequencing is all about exploring cell subpopulations and their distinguishing properties. In this blog, you will learn how to automatically select subset of cells based on different criteria, including generating own scores. Different subsets will then be explored using previously known, as well as newly discovered marker genes. We will work the brain cells from Drosophila melanogaster - a fruit fly, published in the study if Li et. al (2017).
Sequencing datasets often suffer from undesired technical variability, causing some cells to pick up more signal than others. The detection rate can vary considerably among genes. Ultimately, the measurements can also vary significantly when comparing data from different runs of the same experiment, taken on a different day, by a different technician, and so forth.
This calls for preprocessing and normalization methods, making the values comparable across cells, genes and experimental conditions.