R Tutorial: Typical workflow

Want to learn more? Take the full course at https://learn.datacamp.com/courses/single-cell-rna-seq-with-bioconductor-in-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- In this video, we are going to go over the typical steps to analyze single-cell RNA-seq data. The development of new methods and protocols for scRNA-Seq is currently a very active area of research, and several protocols have been published over the last few years. The image here taken from Svensson et al paper shows on the y-axis that the number of cells per dataset has increased from 1 cell for the very first dataset in 2009 to up to 1 million cells for datasets generated today. The methods can be categorized in different ways, but the two most important aspects are quantification and capture. For quantification, there are two types of technologies, full-length and tag-based. The full-length protocols try to achieve a uniform coverage of each RNA sequence. By contrast, tag-based technologies only capture either the two ends of each RNA. The choice of quantification method has important implications for what types of analyses the data can be used for. Then, the strategy used for capture mostly determines throughput. The three most widely used options are microwell-, microfluidic- and droplet-based. For more details about the different technologies, you can go to the Hemberg lab website, it's the reference number 2 at the bottom of this slide and a great reference for analyzing scRNA-Seq. After this brief overview of the different technologies, let's now get an overview of the different steps of a typical workflow to analyze single-cell RNA-seq. Each of these steps is actually a chapter of the course, so we won't go into details here, but just look at the big picture. The very first step when working with scRNA-Seq data is to filter out low-quality cells to ensure that technical effects do not distort downstream analysis resul