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.

Transcript

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
   

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2. In *omics research, these two approach are
   often pitted against each other.
   Data-driven
   research
   Hypothesis-driven
   research
   vs.
   

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3. What drives your scientific pursuit?
   Is it hypotheses? data? methods?
   Or is is curiosity? geography? availability?
   How has it changed over time?
   

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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
   

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5. Part 2: Custom colors and themes for
   data-visualization in R
   

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6. What did I learn as a
   geographically- driven intern?
   

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7. 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.
   

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8. 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.
   

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9. As a result, I highly recommend learning
   multiple programming languages.
   

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10. What did I learn a as
    curiosity-driven lab technician?
    

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11. Lab websites can be a little out of date.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
    

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12. Applying the same methods to diverse
    questions can lead to many publications.
    Unified by a
    central hypothesis.
    Not unified.
    Collaborative.
    

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13. What did I learn as a
    methods-driven PhD student?
    

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14. 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
    

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15. 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
    

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16. Good communication is critical for
    collaboration for speedy collaboration.
    Hypothesis-driven
    explorers
    Data-driven
    researchers
    questions
    results
    

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17. 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
    

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18. 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.
    

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19. 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
    

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20. What am I learning as a
    data-driven postdoc.
    

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21. Open and reproducible research requires a lot
    of infrastructure and community support.
    Data Commons Pilot Phase Consortium
    

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22. It can be difficult to find meaning quickly in
    large RNA-seq projects.
    https://github.com//macmanes-lab/
    DoveParentsRNAseq
    

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23. `factoextra` can identify the genes that load
    most strongly on principal components
    https://github.com//macmanes-lab/
    DoveParentsRNAseq
    

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24. `apaTables`saves time and facilitates reproducible
    writing reporting ANOVA results as data frames.
    24
    https://github.com/macmanes-lab/DoveParentsRNAseq
    

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25. `wgcna`is great for finding coexpressed
    genes
    https://github.com//macmanes-lab/
    DoveParentsRNAseq
    

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26. `sonify`can transform data into music!
    https://github.com//raynamharris/musicalgenes
    

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27. 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
    

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28. 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
    

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29. What questions do you have?
    Dr. Rayna M. Harris
    @raynamharris
    Postdoc, UC Davis
    

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30. And now, some hands-on dataviz
    making custom themes and colors
    https://github.com/raynamharris/tutorials
    

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