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20190712-iqbio.pdf

Patrick Kimes
July 12, 2019
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 20190712-iqbio.pdf

Patrick Kimes

July 12, 2019
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  1. Replicability Hackathon 2019
    IQ BIO REU, UPR-RP
    Data Sciences, Dana-Farber Cancer Institute
    Biostatistics, Harvard TH Chan School of Public Health
    Patrick Kimes, PhD Kelly Street, PhD

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  2. HEALTHCARE
    INNOVATION
    REPLICATHON
    12 y 13 de julio de 2019

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  3. Hi, I’m Kelly

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  4. Hi, I’m Patrick
    PhD, Statistics
    BA, Mathematics

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  5. I’d like to thank the Academy…
    Keegan
    Korthauer, PhD
    Alejandro
    Reyes, PhD
    for making this
    Patricia Ordóñez, PhD
    Juan S. Ramírez-Lugo, PhD
    Rafael Irizarry, PhD
    for making this possible

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  6. Code of Conduct
    https://github.com/pkimes/PR2019replicathon/blob/master/code_of_conduct.md
    Assume competence in the people you interact with.
    There are no stupid questions.
    Be considerate in speech and actions, and actively seek
    to acknowledge and respect the boundaries of fellow
    community members.
    Take care of each other. Alert one of the organizers or
    facilitators if you notice a dangerous situation, someone
    in distress, or a potential violation of this Code of
    Conduct, even if it seems inconsequential.
    We do not tolerate harassment in any form.

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  7. Replicability Hackathon 2019
    IQ BIO REU, UPR-RP
    Data Sciences, Dana-Farber Cancer Institute
    Biostatistics, Harvard TH Chan School of Public Health
    Patrick Kimes, PhD Kelly Street, PhD

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  8. http://jtleek.com/talks.html

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  9. View Slide

  10. “many scientific studies are difficult or impossible to
    replicate or reproduce.”

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  11. https://doi.org/10.1371/journal.pmed.0020124

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  12. https://doi.org/10.1371/journal.pmed.0020124
    “Simulations show that for most study designs and settings,
    it is more likely for a research claim to be false than true.”

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  13. “… replications of 100 experimental
    and correlational studies …”
    https://www.ncbi.nlm.nih.gov/pubmed/26315443

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  14. “39% of effects were subjectively
    rated to have replicated the
    original results.”
    “… replications of 100 experimental
    and correlational studies …”
    https://www.ncbi.nlm.nih.gov/pubmed/26315443

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  15. 39%??
    https://www.ncbi.nlm.nih.gov/pubmed/26315443

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  16. http://jtleek.com/talks.html

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  17. http://jtleek.com/talks.html

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  18. http://jtleek.com/talks.html

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  19. http://jtleek.com/talks.html

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  20. http://jtleek.com/talks.html

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  21. http://jtleek.com/talks.html

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  22. http://jtleek.com/talks.html

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  23. what’s going on?

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  24. let’s clarify some language
    reproducibility
    replicability

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  25. let’s clarify some language
    reproducibility
    replicability
    the ability to take the original data
    and the computer code used to
    analyze the data and reproduce all of
    the numerical findings from the study
    https://simplystatistics.org/2016/08/24/replication-crisis/

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  26. let’s clarify some language
    reproducibility
    replicability
    the ability to take the original data
    and the computer code used to
    analyze the data and reproduce all of
    the numerical findings from the study
    https://simplystatistics.org/2016/08/24/replication-crisis/

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  27. let’s clarify some language
    reproducibility
    replicability
    the ability to repeat an entire study,
    independent of the original investigator
    without the use of original data
    https://simplystatistics.org/2016/08/24/replication-crisis/

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  28. let’s clarify some language
    reproducibility
    replicability
    the ability to repeat an entire study,
    independent of the original investigator
    without the use of original data
    https://simplystatistics.org/2016/08/24/replication-crisis/

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  29. let’s clarify some language
    reproducibility
    replicability

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  30. replicability

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  31. what’s going on?
    replicability

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  32. replicability
    crisis:
    experiments are replicated.
    results not so much.

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  33. should we expect
    scientific results to
    always replicate?
    replicability

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  34. Psychology studies reproducibility 2
    https://www.nytimes.com/2015/09/01/opinion/psychology-is-not-in-crisis.html?
    “But the failure to replicate is not a cause for alarm;
    in fact, it is a normal part of how science works.”

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  35. https://simplystatistics.org/2016/08/24/replication-crisis/
    “the replication crisis in science is largely attributable to
    a mismatch in our expectations of how often findings
    should replicate and how difficult it is to actually
    discover true findings in certain fields.”

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  36. https://simplystatistics.org/2013/08/01/the-roc-curves-of-science/
    “…I argue that the rate of
    discoveries is higher in
    biomedical research than in
    physics. But, to achieve this higher
    true positive rate, biomedical
    research has to tolerate a higher
    false positive rate.”

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  37. should we expect
    scientific results to
    always replicate?
    replicability
    not always

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  38. crisis:
    experiments are replicated.
    results not so much.
    or maybe that’s science?
    replicability

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  39. 39%??

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  40. replicability crisis
    opportunity

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  41. Replicability Hackathon 2019
    IQ BIO REU, UPR-RP
    so what about …
    … ?

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  42. our challenge
    https://www.ncbi.nlm.nih.gov/pubmed/22460902
    https://www.ncbi.nlm.nih.gov/pubmed/22460905

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  43. our challenge
    https://www.ncbi.nlm.nih.gov/pubmed/22460902
    https://www.ncbi.nlm.nih.gov/pubmed/22460905

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  44. drug sensitivity in
    cancer cell lines

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  45. drug sensitivity in
    cancer cell lines
    what is a cell line?

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  46. drug sensitivity in
    cancer cell lines
    cell line
    cell culture from a
    single cell that can
    grow indefinitely given
    appropriate conditions
    • easily grown
    • relatively inexpensive
    • amenable to high-
    throughput testing
    https://www.ncbi.nlm.nih.gov/pubmed/26248648

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  47. drug sensitivity in
    cancer cell lines
    what do we mean by
    drug sensitivity?

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  48. drug sensitivity in
    cancer cell lines
    viability
    relative measure of
    cell line abundance
    after treatment

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  49. drug sensitivity in
    cancer cell lines
    dose response curve
    model fit to viability
    as a function of drug
    concentration

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  50. drug sensitivity in
    cancer cell lines
    IC50
    IC50
    concentration at
    which cell growth is
    inhibited 50%

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  51. drug sensitivity in
    cancer cell lines
    AUC
    area under the activity
    curve
    AUC

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  52. drug sensitivity in
    cancer cell lines
    got it!
    … but why?

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  53. drug sensitivity in
    cancer cell lines
    clinical
    https://www.ncbi.nlm.nih.gov/pubmed/26248648

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  54. drug sensitivity in
    cancer cell lines
    important, impactful
    research!

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  55. our challenge
    https://www.ncbi.nlm.nih.gov/pubmed/22460902
    https://www.ncbi.nlm.nih.gov/pubmed/22460905

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  56. https://www.ncbi.nlm.nih.gov/pubmed/24284626

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  57. https://www.ncbi.nlm.nih.gov/pubmed/24284626

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  58. https://www.ncbi.nlm.nih.gov/pubmed/27905415

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  59. “Upon careful analysis of the same data,
    we have come to quite different and
    much more positive conclusions.”
    https://www.ncbi.nlm.nih.gov/pubmed/27905415

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  61. crisis:
    experiments are replicated.
    results not so much.
    or maybe yes?
    replicability
    opportunity

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  62. enough with the what,
    on to the data

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  63. our challenge
    https://www.github.com/pkimes/PR2019replicathon

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  64. raw data

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  65. summarized data

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  66. summarized data

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  67. model
    summarized data

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  68. our challenge
    datasets

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  69. our challenge
    datasets

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  70. our challenge
    datasets
    > setwd(‘path/to/PR2019replicathon’)
    > raw <- readRDS(‘data/rawPharmacoData.rds’)
    > ls()
    [1] “raw”

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  71. our challenge
    template

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  72. our challenge
    template

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  73. our challenge
    template

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  74. our challenge
    tutorials

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  75. our challenge
    tutorials

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  76. our challenge
    do these
    studies agree?

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  77. Let’s Hack!

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  78. getting setup with

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  80. View Slide

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  82. feel free to specify a
    different location

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  83. ready to go!

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  84. https://pkimes.github.io/PR2019replicathon/

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  85. Let’s Hack!
    Gigaprobe
    learnlispandRTFM

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