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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

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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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