• 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

Built With

  • deseq2
  • ggplot
  • r
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