From the Childhood Cancer Data Lab
Recently, the Childhood Cancer Data Lab packed up and headed to the University of Minnesota (UMN) to host a workshop for 19 researchers. Participants with a variety of skill levels and backgrounds joined us from UMN, St. Jude Children’s Research Hospital, the Mayo Clinic, and the Medical University of South Carolina.
This workshop was particularly exciting because it was an event four years in the making! Back in March 2019, we held our second data science training workshop (ever!) in Houston, TX. In attendance were Dr. Lindsay Williams and Dr. Lauren Mills from UMN, who joined us to learn about the reproducible analysis of bulk and single-cell transcriptomic data. By the end of the workshop, Drs. Williams and Mills were convinced that others at UMN would benefit tremendously from our training program. They invited the Data Lab team to bring our expertise to the university!
The COVID-19 pandemic threw a wrench into our plans. But this year, we could finally resume planning, and we made it to Minneapolis in August!
Designing the course
We wanted to create a hands-on workshop that would meet the needs of the training group and help advance their goals. Dr. Williams helped by surveying principal investigators at the university to find out how our training materials could make the biggest impact in their labs. We decided to teach a combination of our bulk RNA-sequencing and reproducible research practices modules. Overall, we hoped to help attendees better understand their own data, become more comfortable with learning new methods, and provide them with language and tools to collaborate more effectively with their bioinformatics colleagues!
The workshop began with an introduction to the R programming language, the Tidyverse, and techniques to achieve reproducible results in computational cancer research. Next, we introduced pipelines for the quality control, processing, and downstream analysis of bulk RNA-seq data. Finally, we covered common approaches for pathway analysis, including over-representation analysis (ORA), gene set enrichment analysis (GSEA), and gene set variation analysis (GSVA).
Here’s a glimpse of the program we put together for the group. You can view the full workshop schedule and materials here.
On the final day, participants had the chance to present the projects they worked on throughout the course. We were thrilled to see that attendees were already applying skills they learned to explore their own scientific questions. We’ve even heard that participants have formed a working group at UMN to continue practicing with the workshop exercises. They hope to keep this going as a way to generally support each others’ bioinformatics in the future.