CSEE 2026 Workshop

Workshop overview

Welcome to the CSEE 2026 Workshop — an AZ2CH R crash course in reproducible analysis of ecological communities.

Over the next three hours we learn R fundamentals → data wrangling → publication-quality visualizations → multivariate statistics — all through a worked microbiome metabarcoding analysis on a real tick microbiome dataset.

Download files Metabarcoding slides

Schedule

Time	Block	Topic	Presenter
1:00 – 1:10	—	Workshop overview
1:10 – 1:20	I	R basics review	Dr. María José Gómez Quijano
1:20 – 1:40	I	Data science / `dplyr` review	Dr. María José Gómez Quijano
1:40 – 2:00	II	Grammar of Graphics review (`ggplot2`)	Charlotte Ngo
2:00 – 2:20	III	Ordination & multivariate methods (lecture)	Dr. Robert I. Colautti
2:20 – 2:30	—	Coffee break (10 min)
2:30 – 2:50	III	From samples to a community matrix	Dr. Robert I. Colautti
2:50 – 3:05	III	Single vs repeated subsampling	Sreevatshan K. Srinivasan
3:05 – 3:20	III	α-diversity analysis	Sreevatshan K. Srinivasan
3:20 – 3:50	III	β-diversity analysis	Sreevatshan K. Srinivasan
3:50 – 4:00	—	Wrap-up, Q&A, resources

Datasets used today

otu_table.csv — ASV abundance table (41 tick samples × 2,059 ASVs)
taxonomy_table.csv — taxonomic assignments per ASV (kingdom → species)
sample_metadata.tsv — per-sample metadata (location, tissue type, Stock_Conc, total_reads, …)

Source: Paulson AR, Lougheed SC, Huang D, Colautti RI. 2023. Multiomics Reveals Symbionts, Pathogens, and Tissue-Specific Microbiome of Blacklegged Ticks (Ixodes scapularis) from a Lyme Disease Hot Spot in Southeastern Ontario, Canada. Microbiology Spectrum 11:e01404-23. doi:10.1128/spectrum.01404-23

Workshop outline

Three parts, taught in sequence with one coffee break. The same tick microbiome dataset threads through Parts II and III, so the visual literacy you build in Part II carries directly into the metabarcoding analysis in Part III.

Part I · FUNdamental R

R basics, R Markdown, and data wrangling with the tidyverse / dplyr. Students who already know R can use this block as a quick refresher; students new to R get the workflow they'll need for Parts II and III.

Topics: install R/RStudio, IDE tour, R Markdown setup, YAML, code chunks, equations, packages, help; objects, vectors, sequences, data frames, subsetting, opening CSVs, data types; the pipe operator, filter(), select(), arrange(), mutate(), missing data, summary statistics, joining/merging, pivoting.

Part II · Visualizations

Base R graphics, then a deeper dive into ggplot2 and the Grammar of Graphics. Customization (color, shape, themes), histograms, boxplots, and regression overlays — using the same tick microbiome dataset that Part III analyzes.

Topics: why R for graphing; accessibility and color choices; base R quick plots, axis labels, point color and size; ggplot2 layers, aesthetics, titles/labels, color, shape, themes; histograms and boxplots; regression lines.

Part III · Microbiome Metabarcoding

End-to-end worked example: a 16S rRNA microbiome dataset from the blacklegged tick Ixodes scapularis (Paulson et al. 2023). Students go from raw OTU/ASV tables → QC/filtering → rarefaction → α-diversity (Richness, Evenness, Shannon, Simpson) → β-diversity (Jaccard, Bray–Curtis, NMDS, neighbor-joining trees with ggtree).

Topics: library setup (tidyverse, vegan, ape, ggtree); OTU/taxonomy/metadata input; chloroplast filtering, sequencing-error QC, read-depth filtering; location-label cleanup, rarefaction curves, single-subsampling; α-diversity by tissue type and by location; β-diversity with Jaccard and Bray–Curtis distance + neighbor-joining trees (ggtree); NMDS ordination.

Reproducible sample tracking: before any of this, samples need to be tracked end-to-end — that's what BaRcodeR is for (free, from this lab).

What we’ll learn together

Three hours, no prior coding required. By the end of the workshop we’ll all have:

An installed and verified R + RStudio environment.
A reproducible project skeleton you can use for your own thesis or paper.
A real community-ecology dataset wrangled, plotted, and analyzed.
The conceptual map for what a "reproducible research workflow" actually is — and why it matters once you're 6 months into a postdoc.

Before you arrive

Please install R and RStudio Desktop before the workshop. Then in RStudio, run:

install.packages(c("tidyverse", "vegan", "ape", "here", "rmarkdown", "knitr"))
# ggtree is on Bioconductor:
if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")
BiocManager::install("ggtree")

Also download the .zip files (See 'Download CSEE2026 files' button) — the student workbook with empty code chunks you'll fill in as we go.

If the install hits a snag, that's fine — we'll troubleshoot in the first 15 minutes. Bring your laptop, a charger, and a willingness to make messy mistakes. (You'll make several. That's the point.)

Who this is for

You've never opened R before, or you've opened it twice and bounced off. You're a graduate student, postdoc, or working biologist who knows the science but feels stuck whenever the analysis turns into code. Or you're an instructor curious about evidence-based teaching of computational fluency to non-CS learners. Welcome.