You have removed lowly expressed genes and low quality cells from your dataset and normalised it. But what now? Join Dr. Luise Seeker and Dr. Sarah Jaekel in this episode of Single Cell Science to learn more about the principle bioinformatic analysis workflow for single cell RNAseq datasets. We talk about variable genes, dimensional reduction, clustering, pseudotime analysis and more. This is the last of four stand-alone introductory episodes. Next time we will start to dig deeper. 
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So you got your sequencing data, but what now? Join Dr. Luise Seeker and Dr. Sarah Jaekel in this episode of Single Cell Science to learn about the principles of the bioinformatics analysis of a single cell RNAseq dataset. We introduce raw data processing pipelines, data quality control steps and discuss normalisation and will continue the discussion in the next episode.

In this episode of Single Cell Science  Dr. Luise Seeker and Dr. Sarah Jäkel  are presenting a general work flow in the lab to conduct a single cell RNAseq experiment. Which samples can be used? What are the advantages and disadvantages of working with cells or nuclei? How are transcripts of different cells captured? What do I need do before sending a sample off for sequencing? If you want to find out the answers to those questions and more, don't forget to tune in.

Single cell RNA sequencing is a new, powerful method to investigate cellular heterogeneity, differentiation and responses to treatments. It is used by scientists around the world and is becoming a regular component of scientific presentations and publications. 
In this first episode of Single Cell Science we describe on a high level, what this method is, why it is so powerful and what the limitations of previous methods were.