My sequencing was done by Ion Torrent which made Qiime a bit difficult to use because of lacking documentation. I followed, mostly, this post by Carli Jones: https://forum.qiime2.org/t/possible-analysis-pipeline-for-ion-torrent-16s-metagenomics-kit-data-in-qiime2/13476.


  1. Import fastq using “manifest” file format
  2. Perform DADA2
  3. Explore feature counts
  4. Create phylogenetic tree using SEPP (fragment insertion)
  5. Alpha rarefaction
  6. Core metrics analysis

Qiime outputs, R plots

For this project data visualization is the most important part because it’s pilot data for a new project. qiime2R has really good documentation; for details see https://github.com/jbisanz/qiime2R.

First I uploaded the Qiime2 output files needed to make my plot which was very straightforward following the documentation by Dr. Jordan Bisanz.

colnames(metadata)[colnames(metadata) == "sampleid"] = "SampleID"
shannon<-read_qza("shannon_vector.qza")$data %>% rownames_to_column("SampleID")


Again, using code from the qiime2r documentation, I plotted the first two PCs with sizing by Alpha Diversity.

uwunifrac$data$Vectors %>%
  select(SampleID, PC1, PC2) %>%
  left_join(metadata) %>%
  left_join(shannon) %>%
  ggplot(aes(x=PC1, y=PC2, color=`samplegroup`, size=shannon)) +
  geom_point(alpha=0.8) + #alpha controls transparency and helps when points are overlapping
  theme_q2r() +
  scale_size_continuous(name="Shannon Diversity") +
  scale_color_discrete(name="Patient") + 
  scale_color_viridis(discrete = T, name = "patient")


Heidi E. Steiner
Heidi E. Steiner
Senior Clinical Data Scientist

I love coding in R, biostatistics, and reproducible clinical research.