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Life lessons and scientific insight from methods-, hypothesis-, and data-driven research

Rayna M Harris
February 26, 2020

Life lessons and scientific insight from methods-, hypothesis-, and data-driven research

A short presentation for "Meet and Analyze Data (MAD) at the University of California Davis, a graduate-student-run organization. I describe a handful of life lessons that I've learned and scientific insights that I've gained during my journey as a scientist.

Rayna M Harris

February 26, 2020

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  1. Life lessons and scientific insight from methods-, hypothesis-, and dataviz-driven

    research Dr. Rayna M. Harris raynamharris Postdoc, UC Davis February 26, 2020 Meet and Analyze Data, UC Davis
  2. In *omics research, these two approach are often pitted against

    each other. Data-driven research Hypothesis-driven research vs.
  3. What drives your scientific pursuit? Is it hypotheses? data? methods?

    Or is is curiosity? geography? availability? How has it changed over time?
  4. Part 1: Lessons learned in my scientific career Methods- driven

    PhD student Geographically- driven intern Curiosity- driven technician Data- driven Postdoc By Caitlin Friesen By Rayna Harris docktodish.com By Rayna Harris Etsy
  5. Immersion is great for learning languages. Me in Costa Rica

    learning to speak spanish while diving for specimens and culturing microscope mushrooms that live in the ocean.
  6. Learning a new language opens a whole new world of

    opportunity (sometimes years later). Me translating Carpentry lessons into Spanish at a hackathon in Germany and teaching UNIX in Argentina.
  7. Lab websites can be a little out of date. What

    I wanted to do every day: SCUBA dive What I actually did every day: hormone assays and qPCR Lab website: We study social behavior in fish from the Great Lakes of Africa
  8. Applying the same methods to diverse questions can lead to

    many publications. Unified by a central hypothesis. Not unified. Collaborative.
  9. My methodology of choice was something I called “reverse genomics”

    at the time. Harris, R. M., & Hofmann, H. A. (2013). Ecological Genomics, 149–168. doi:10.1007/978-94-007-7347-9_8 Subject of my NSF GRFP Subject of my PhD thesis
  10. How do mouse brains store memories? Me, a data-driven explorer:

    Look at the activity of all these immediate early genes, like Arc, Jun, Egr1, and Fos! Hypothesis-driven collaborator: What about genes encoding proteins that stabilize memory, like PKM, CAMKII, KIBRA and NSF? Harris RM, Kao HY, Alarcón JM, Fenton AA Hofmann, HA. bioRxiv 2020.02.05.935759; doi: https://doi.org/10.1101/2020.02.05.935759
  11. I learned to use and write workflows. I recommend learning

    to automate them too. https://github.com/raynamharris/ IntegrativeProjectWT2015 Harris RM, Kao HY, Alarcón JM, Fenton AA Hofmann, HA. bioRxiv 2020.02.05.935759; doi: https://doi.org/10.1101/2020.02.05.935759
  12. Protect yourself from sample and data loss! A note that

    kept my samples alive when this freezer died. Lost data is easier to recover when you have a backup.
  13. RNA-seq is useful but insufficient for identifying cell types. Additional

    analyses are needed. Northcutt AJ, et al. 2019 PNAS 2019, 116 (52) 26980-26990; DOI: 10.1073/pnas.1911413116
  14. Open and reproducible research requires a lot of infrastructure and

    community support. Data Commons Pilot Phase Consortium
  15. It can be difficult to find meaning quickly in large

    RNA-seq projects. https://github.com//macmanes-lab/ DoveParentsRNAseq
  16. `factoextra` can identify the genes that load most strongly on

    principal components https://github.com//macmanes-lab/ DoveParentsRNAseq
  17. `apaTables`saves time and facilitates reproducible writing reporting ANOVA results as

    data frames. 24 https://github.com/macmanes-lab/DoveParentsRNAseq
  18. We can learn more by working together and combining different

    approaches. Data-driven explorers Hypothesis-driven researchers Methods-driven developers tools results knowledge questions Imagine inspired by lab meeting led by C. Titus Brown
  19. Many thanks to folks in the Instituto Nacional de Biodiversidad,

    Costa Rica College of Natural Science, UT Austin Neural Systems & Behavior Course, MBL Data-Intensive Biology Lab, UC Davis Birds, Brains & Behavior Lab, UC Davis Science & Technology Studies, UC Davis Funded by NSF, NIH
  20. And now, some hands-on dataviz making custom themes and colors