- Traditional RNA-seq data analysis starts with read counts and aims to identify crucial genes that have statistical difference based on a selected criteria.
- With the same data collected from NanoString technology, however, researchers have access to spatial information
What it does
- In the example of kidney scans, there is a direct mapping between read counts and the region of sequencing.
- We thought it would be interesting to see if highly DE genes identified by DESeq2 for the overall dataset would differ from those going through the same work flow, but conditioned on the sequencing region, glomerulus or tubule.
- We found two distinct groups of highly differentially expressed genes
How we built it
Using DeSeq2 in R
Challenges we ran into
Visually demonstrate the p-value significance
Accomplishments that we're proud of
First time using R to process such a large dataset
What's next for DeSeq Analysis by Region
Allele expression analysis using spatial data