The management and interpretation of vast amounts of data is a growing challenge for medical researchers, yet many in the field lack data-handling skills. Spenser Johnson, a postgraduate researcher at Yale University, took matters into his own hands by proactively teaching himself how to code and later transitioned toward analytic platforms to apply high-level data analyses in cancer research.
Discovering an unmet need for data skills
While Spenser is currently a postgraduate researcher at Yale, his interest in working with data originated during his undergraduate years studying biochemistry at the University of Iowa. He explains, “During my time at Iowa, I became really interested in learning how to leverage the data that was publicly available for scientific research.” Spenser knew that being able to work with data would be invaluable no matter what direction his future research took him. However, Spenser explained that data skills are “not commonly taught, especially within curriculums that don’t focus on bioinformatics or computer science.”
The power of low code/no-code data management
In the lab, Spenser became aware of how much data was available, and he realized that there weren’t many researchers who could utilize it. That meant researchers were also missing out on data insights that could lead them to new projects. Spenser says, “I decided to upskill on data literacy and analytics on my own.” After teaching himself to code in R and Python, Spenser was introduced to Alteryx Designer by his father, Sam Johnson, who now works at Alteryx as Director of Resident Consulting. Spenser explained that his lack of training in coding made it difficult to get the environments and consoles for R and Python set up properly, much less to learn the syntax of each language. With Alteryx Designer, he said that “downloading the software and having it all installed in a self-contained package was perfect for an amateur” and “that [he] felt like [he] could hit the ground running.”
For Spenser, the difference in data analysis was significant, and he realized “it’s so much easier with drag and drop node styles where it’s very visual and intuitive.” Spenser also appreciated that “…you can see errors in thinking and figure out how to improve processes much faster.” Spenser preferred working in the low-code/no-code environment. He states, “It was so much nicer to not have to learn this syntax and everything that goes into coding. And it was much more visual and felt more intuitive.” Spenser appreciated that he could create an outline of his workflows with empty nodes and just fill them in as he progressed. When errors arose, it was easier to diagnose which specific node was the issue rather than going back to chunks of code.
Working with large sets of mRNA expression data
At Iowa, Spenser began using Alteryx Designer in his work with differential gene expression analysis, conducting investigations of a target set of genes using publicly available mRNA sequencing data. According to Spenser, “It was super cool to see how using these low-code/no-code environments, I could integrate really important data to drive projects and push things forward within the lab.” His research investigated the up-and-down regulation of key metabolic hubs during viral and bacterial infections. Now, Spenser uses similar techniques as a postgraduate researcher focusing on cancer research.
Using analytics in cancer treatment research at Yale
In his postgraduate research at Yale, Spenser is working in the field of cancer biology, specifically DNA damage repair and drug development. Spenser explains that when it comes to cancer treatment, “Every medicine you have, to a certain dose, is a poison. It’s just figuring out what dose is poisonous to your cancer cells versus your normal tissue.”
To find the answer, he’s working to analyze the relationship between gene dependence and chemotherapy sensitivity using a technique called CRISPR-Cas9 screening. This project opens avenues for understanding more about the way our cells repair DNA after different treatments, as well as integrating patient data to one day provide better care. The ultimate goal is to create customized treatments that kill cancer cells without significantly harming healthy, normal cells.
Graphic courtesy of Dr. Ranjit Bindra, MD, PhD; Harvey and Kate Cushing Professor of Therapeutic Radiology and Professor of Pathology
Bringing theoretical and real-world data together
Spenser is using the chemo-genomic interactions that have been identified from the CRISPR screen and integrating this information with patient data in order to provide real-world interventions that can benefit patients. Spenser explains that without patient data integration, the research “is not useless, but it’s definitely not anywhere near as effective as we can make it. So that’s what I’m currently working within Alteryx.”
Cancer treatment moving to precision medicine
Spenser explains that cancer treatment is getting much more precise: “The concept is called precision oncology, or precision medicine. It’s really moving away from just giving everyone with cancer the same drugs.” The goal is customized care: “The idea is each person would get a very specific group of drugs that would best target their tumor.”
The future of cancer research and the importance of data analysis
Spenser believes that taking an interdisciplinary approach to cancer treatment invites a promising future, but he sees that a lot of research is not yet as data-oriented as it could be. He views it as essential that researchers integrate both clinical data and patient outcomes with the chemo-genomic interaction data that is being discovered. Spenser believes it will be interesting in the years to come to see how “data are leveraged within different fields of science,” and he believes that this approach will be key to the next decades of cancer treatment.
Becoming proficient in problem-solving with data
Spenser reflects that using Alteryx “made me better at really understanding how to approach a lot of these data science problems.” In addition, the visual aspect of data management in Alteryx helped Spenser understand the process of how to best tackle the projects that he wants to take on. As opposed to coding, Alteryx made working with large sets of data much easier. He explains, “I don’t have to go out and figure out what to type in; it’s much simpler and more efficient.”
Analytics accessibility through SparkED
Spenser has been able to access a Designer license as well as instructional training through SparkED, Alteryx’s data analytics education program. He takes advantage of them and says, “It’s so nice to be able to be part of the SparkED community and have access to the greater Alteryx Maveryx community as well. Without that community of analytics professionals and other learners like me, it would be so much harder to learn these things on my own.” SparkED made Spenser feel like “I could step into this program and break the barriers of data science.” As opposed to other learning programs that Spenser explored that cost tens of thousands of dollars, having free access to SparkED made data analytics feel much more accessible. He concludes, “I really appreciate the opportunity to be involved in SparkED.”
SparkED offers free Alteryx Designer licenses to qualifying students in higher education, university/college professors, and their classes, as well as to career-changing individuals. For more information, please visit smartling.alteryx.com/sparked.