CSTAT Spring 2026 Workshops

Statistical Analysis of Bulk RNA-seq Data + Quick Intro to R and RStudio (Hybrid - Zoom/In-Person)

CSTAT Seminar series: Statistical Analysis of RNA-seq Data Part 1

January 30, 2026 - Noon - 3:00 pm

Presented by Dr. Arash Yunesi, CSTAT - Hybrid Zoom + In-Person - Chemistry Building Computer Lab (CEM) 235

Part 1 of a 3 part series.

Bulk RNA-seq, historically called RNA-seq, is one of the earliest technologies in this group. It is still widely used due to its affordability, the large number of datasets available publicly, and biological reasons. In this workshop, we will analyze a dataset from bulk RNA-seq and go through the main analyses and visualizations together. Our hands-on analyses will include quality control, pre-processing, variance stabilizing transformations, multiple testing, differentially expressed gene (DEG) analysis, gene set enrichment analysis (GSEA), deconvolution, and visualizations. We will start this workshop with a quick intro to R and RStudio before the analyses, and finish with troubleshooting and answering questions. Bring your own laptop.

Statistical Analysis of single cell RNA-seq (scRNA-seq) Data (Hybrid - Zoom/In-Person)

CSTAT Seminar series: Statistical Analysis of RNA-seq Data Part 2

February 27, 2026 - Noon - 3:00 pm

Presented by Dr. Arash Yunesi, CSTAT - Hybrid Zoom + In-Person - Chemistry Building Computer Lab (CEM) 235

Part 2 of a 3 part series

scRNA-seq is currently the most widely used technology in this group. It differs from the traditional bulk RNA-seq in that cells are separated, tagged, and sequenced one by one, giving us a more detailed information of the inner workings of cells and tissue. scRNA-seq produces a large matrix of data from each sample and it provides opportunities for more sophisticated and complex analyses. In this workshop, we will cover preprocessing, quality control, variable gene selection, variance stabilizing transformations, dimensional reduction, unsupervised clustering, annotations, marker gene selection, and data integration for scRNA-seq data. For this workshop we will focus on Seurat, a specialized package to work with scRNA-seq data and perform various analyses and visualizations. Bring your own laptop.

Statistical Analysis of spatial RNA-seq Data (Hybrid - Zoom/In-Person)

CSTAT Seminar series: Statistical Analysis of RNA-seq Data Part 3

April 3, 2026 - Noon - 3:00 pm

Presented by Dr. Arash Yunesi, CSTAT - Hybrid Zoom + In-Person - Chemistry Building Computer Lab (CEM) 235

Part 3 of a 3 part series

Spatial RNA-seq technologies are the newest member of this group of RNA-seq technologies. For spatial RNA-seq, thin slices of tissue are sequenced or imaged and thus the spatial information of the measurements is retained. The spatial RNA-seq technologies are divided into two subgroups, sequencing-based and imaging-based methods. For this workshop we focus on sequencing-based methods, however the analyses can be applied to imaging-based technologies as well. We will cover preprocessing, quality control, spatially variable gene selection, variance stabilizing transformations, domain detection and unsupervised clustering, cell-type deconvolution, marker gene selection, and many interesting visualizations. Bring your own laptop.

Introduction to non-linear modeling via regression splines, using R (In Person)

April 24, 2026 - Noon - 3:00 pm

Presented by Dr. John Gregory Hixon, CSTAT - In Person - Chemistry Building Computer Lab (CEM) 235

Do you suspect that there might be non-linear relationships in your data but don’t know what specific shape they might be or how to analyze for them? If so, this workshop is for you!

Regression splines allow for the flexible modeling of non-linear relationships regardless of their functional form between continuous covariates and response variables. This workshop will introduce participants to the concept and use of spline functions in the R software. Topics covered:

• What are regression splines
• How to implement them in regression models using the mgcv and splines packages in R
• How to report and depict those findings -- including use of some of R’s 3D visualization tools

Prerequisites: Familiarity with R and R Studio and with basic linear regression concepts.