scRNA seq: The Future of RNA Sequencing

Inspiration

Single cell RNA seq (scRNAseq) has recently been popularized as it provides researchers with the ability to determine which genes are being expressed in certain cell types within different conditions. This helps in downstream applications, such as being able to target a specific gene in its role in a certain disease. The problem however, is that different researchers use different methods in performing scRNAseq. With there being so many different methods of performing scRNAseq, variation may occur merely due to the differences in how the scRNAseq was performed. I sought to make a pipeline that is easy to follow, and one that will allow others to use on their own data.

What it does

This pipeline takes in matrices containing information such as the RNA nucleotide sequence that was taken from the sequencer device, and first performs quality control steps to remove poor quality cells. The data is then normalized, more cell filtering is performed. Based on what genes are being expressed, the cells are plotted into clusters. From there, you now have your data annotated to show what cells you have present in your sample (ex. your sample contained B cells), as well as what genes were found to be expressed/absent.

What's next for scRNA seq: The Future of RNA Sequencing

I want to implement this scRNA seq pipeline into my own work as I am currently a co-op bioinformatics student in the Tokuyama lab at the Life Science Institute at UBC where we study endogenous retroviruses.

Built With

• r