This workshop provides a quick (~2 hour), hands-on introduction to current computational analysis approaches for studying single-cell RNA sequencing data. We will discuss the data formats used frequently in single-cell analysis, typical steps in quality control and processing of single-cell data, and some of the biological questions these data can be used to answer.
This workshop provides a one-day (~5 hour), hands-on introduction to current computational analysis approaches for studying single-cell RNA sequencing data. We will discuss the data formats used frequently in single-cell analysis, typical steps in quality control and processing of single-cell data, and some of the biological questions these data can be used to answer.
Given we only have 2 hours, we will start with the data already processed into a count matrix, which contains the number of sequencing reads mapping to each gene for each cell. The steps to generate such a count matrix depend on the type of single-cell sequencing technology used and the experimental design. For a more detailed introduction to these methods we recommend the long-form single-cell RNA seq analysis workshop from the BioCellGen group ([available here](https://biocellgen-public.svi.edu.au/mig_2019_scrnaseq-workshop/public/)) and the analysis of single-cell RNA-seq data course put together by folks at the Sanger Institute [available here](https://scrnaseq-course.cog.sanger.ac.uk/website/processing-raw-scrna-seq-data.html). Briefly, a count matrix is generated from raw sequencing data using the following steps:
