The R Project for Statistical Computing

When dealing with large data sets or simply performing statistical tests, standard tools like Excel often fall short. Some labs have software that can help to bridge the gap between what Excel can do and what you actually need to get done. The problem is that if you don’t already have this software, the cost for a license is prohibitively expensive for graduate students. Because of this researchers often turn to R. This is an open source programing language that has an extensive library of packages available. Not only are there packages for statistics, but there is also a comprehensive library of packages (1560 currently in the Bioconductor Repository) specifically for biological data. The best part of all is that all of these resources are opensource…that means FREE!

I understand that looking at a terminal full of code can seem daunting, but investing the time to learn a programming language will pay off in the long run. Over the past couple years of my graduate degree, I have become very familiar with the language and many of the core packages. Now if I need to do anything more than a simple t-test, R is what I turn to.

The purpose of this blog is to share some ideas of how to start integrating R in your daily research. As such I have put a strong emphasis on tools that are applicable to a wide variety of common analytical methods in the life sciences.


My Experience and Publications

I am a biomedical researcher studying neuroinflammation and CNS injury. I am experienced in the analysis of large datasets including mRNA and miRNA NGS data. While I may be trained as a bench scientist, I have a passion for computer science and I am always looking to expand my skills. Many of my publications include RNA-seq experiments that I have performed and/or analyzed. Working with this sort of data as well as smaller projects has allowed me to acquire diverse computational skills that I hope to share with others.

  • Rolfe, A. J. (2015). RE: Cerebral autoregulation in different hypertensive disorders of pregnancy. American Journal of Obstetrics and Gynecology, 212(6), 832. doi:10.1016/j.ajog.2015.01.037

  • Guo, L., Rolfe, A. J., Wang, X., Tai, W., Cheng, Z., Cao, K., Chen, X., Xu, Y., Sun, D., Li, J., Young, W., Fan, J. Ren, Y. (2016). Rescuing macrophage normal function in spinal cord injury with embryonic stem cell conditioned media. Molecular Brain,9(1). doi:10.1186/s13041-016-0233-3

  • Rolfe, A. J., Bosco, D. B., Wang, J., Nowakowski, R. S., Fan, J., & Ren, Y. (2016). Bioinformatic analysis reveals the expression of unique transcriptomic signatures in Zika virus infected human neural stem cells. Cell & Bioscience,6(1). doi:10.1186/s13578-016-0110-x

  • Rolfe, A. J., Bosco, D. B., Broussard, E. N., & Ren, Y. (2017). In Vitro Phagocytosis of Myelin Debris by Bone Marrow-Derived Macrophages. Journal of Visualized Experiments,(130). doi:10.3791/56322

  • Jianshe Lang, Yichen Cheng, Alyssa J Rolfe, Christy Hammack, Daniel Vera, Kathleen Kyle, Jingying Wang, Yi Ren, Chad Cowan, Hengli Tang (2018). A hPSC-derived macrophage model reveals distinct cellular responses to Zika and dengue viruses. Stem Cell Reports.doi.org/10.1016/j.stemcr.2018.06.006

  • Tian Zhou, Yiming Zheng, Li Sun, Alyssa J Rolfe, Xi Wang, Zhijian Chen, Zhaoshuai Huang, Xin Sun, Jinhua Li, Jianqing Fan, Timothy L. Megraw, Guixue Wang, Yi Ren. Engulfment and autophagic clearance of myelin debris by microvascular endothelial cells promotes inflammation and fibrotic scar formation after spinal cord injury. Nature Neuroscience. doi.org/10.1038/s41593-018-0324-9