Generating a ROC curve with ggplot2 in R: Balancing the specificity and sensitivity of ASVs (CC058)
Channel: Riffomonas Project
Date: 2020-12-21

We've seen that by varying the threshold used to define an ASV we can modulate the amount that genomes are split into multiple bins and the amount that different species are lumped together. Receiver operator characteristic (ROC) curves are useful tools for visualizing this tradeoff. In this episode we take a deeper look at the tradeoff and go back to get more data to give us more resolution. This episode is part of a larger arc of episodes investigating the sensitivity and specificity of amplicon sequence variants (ASVs), also known as exact sequence variants (ESVs). ASVs are growing in popularity for analyzing microbial communities using 16S rRNA gene sequences. Pat demonstrates these concepts by live coding at the command line interface using GitHub Flow and RStudio.

0:00 Introduction
3:45 Sensitivity & specificity
10:23 Building a ROC curve
12:04 Sens/Spec for 3%
15:24 Balancing sens & spec
19:27 Getting close to perfect
24:40 Getting more data
31:11 Committing & Conclusion

The accompanying blog post contains the exercises and solutions can be found at http://www.riffomonas.org/code_club/2020-12-21-roc-curves