For my STEP Signature Project, I conducted undergraduate research in the Molecular Genetics department at Ohio State. My project focused on understanding melanocyte heterogeneity using single-cell RNA sequencing. Understanding the role of melanocytes in melanoma development allows for potential treatments to be found in the future. I utilized Jupyter Notebook and Python, working within Scanpy, to analyze the sequencing data and perform essential computational functions.
Through this research experience, my understanding of myself as a scientist and my approach to problem-solving in molecular biology transformed significantly. Before this project, I viewed research as primarily a technical task—following protocols and generating data. However, as I engaged with complex datasets and encountered the challenges of interpreting single-cell RNA sequencing results, I realized that research is as much about creativity, patience, and critical thinking as it is about technical skills. This shift in perspective helped me understand that the process of scientific inquiry requires persistence and adaptability, especially when working with new tools like Scanpy. I also gained a deeper appreciation for the role of computational biology in understanding cellular diversity, which broadened my view of the research landscape.
One key experience that contributed to this transformation was learning to code in Python for data analysis. At first, I found programming intimidating and frustrating, but as I became more proficient, I realized the power of computational tools in unraveling biological complexity. Each time I successfully wrote a script or corrected an error, I felt a sense of accomplishment that boosted my confidence as both a researcher and a problem solver.
Another significant aspect of my project was working with single-cell RNA sequencing data, which presented both technical challenges and opportunities for discovery. The sheer volume of data and the complexity of distinguishing between different cell states and subtypes pushed me to develop a more strategic approach to analysis. I learned how to troubleshoot issues with my datasets and tailor my methods in Scanpy to extract meaningful insights. This experience taught me the importance of precision and critical thinking, as even small errors in coding or data handling could drastically affect the results.
Moreover, the mentorship I received from my research advisor and lab colleagues played a crucial role in my transformation. They provided guidance not only on the technical aspects of the research but also on how to approach scientific problems with curiosity and resilience. Their feedback helped me understand that failure is a natural part of research and that perseverance is key to making progress in scientific inquiry.
This transformation has been valuable both personally and professionally. It has solidified my interest in pursuing a career in research, particularly in the field of computational biology or molecular genetics. The skills I developed—such as coding, data analysis, and problem-solving—are directly applicable to my future academic and professional goals. Additionally, this experience taught me the importance of interdisciplinary approaches in science, combining wet lab work with computational analysis to gain deeper insights. As I continue my academic journey, I am more confident in my ability to tackle complex research questions and contribute meaningfully to the field.
