This document discusses principles and guidelines for reproducible and transparent research. It highlights the importance of rigorous statistical analysis, transparency in reporting standards used, replicates, statistics, randomizations, and inclusion/exclusion criteria. It provides examples of reporting standards like MIAME, which defines minimum information for microarray experiments, and the Gene Ontology, which provides consistent descriptions of genes. Data sharing is also discussed, noting it requires standards, data volume, and bandwidth. While useful standards and best practices exist, support from funders and journals could help further improve reproducibility.

1. "A Replicable Guide to Reproducible
Research"
Iddo Friedberg
Iowa State University

2. “Random Musings on
Fixing Data Shambles in Science"

5. NIH: Principles and Guidelines for
Reporting Research
Rigorous statistical
analysis
Transparency in
reporting
Data & material sharing
Best practices

6. Interpretable statistical analysis
Effect size and
significance
“EFFECT SIZE vs. SIGNIFICANCE: smoking increases your relative risk of lung cancer by
2,500 percent; eating two slices of bacon a day increases your relative risk for colorectal cancer
by 18 percent. Given the frequency of colorectal cancer, that means your risk of getting
colorectal cancer over your life goes from about 5 percent to 6 percent”

7. Interpretable statistical analysis
Multiple hypothesis
testing
“Either we have stumbled onto a rather
amazing discovery in terms of post-mortem
ichthyological cognition, or there is something
a bit off with regard to our uncorrected
statistical approach.”
Craig M. Bennett, et al (2010).
Neural Correlates of Interspecies
Perspective Taking in the Post-Mortem
Atlantic Salmon: An Argument For
Proper Multiple Comparisons Correction
Journal of Serendipitous and Unexpected
Results, 1 (1), 1-5

8. Transparency in reporting
Standards
Replicates
Statistics
Randomizations
Blinding
Inclusion & exclusion
criteria

9. Transparency in reporting
Standards
Replicates
Statistics
Randomizations
Blinding
Inclusion & exclusion
criteria

10. MIAME (2001)
Minimum Information
About a Microarray
Experiment
Experimental conditions
Controlled vocabularies for
organisms, gene functions

11. MIAME (2001)
A journal enforced
reporting standard
enables:
Reproducibility
Compare data
Compare method
effectiveness
Refutability

12. Some more standards
Standard Purpose
Gene Ontology Protein function
Human Phenotype Ontology Human disease
MIxS Genomic and Metagenomic data

13. The Gene Ontology
Pingzhao Hu, Gary Bader, Dennis A. Wigle & Andrew Emili
Nature Reviews Cancer 7, 23-34 (January 2007)

14. The Gene Ontology
Consistent descriptions
of genes
Mostly Human, mouse,
fly & yeast
Functional aspects:
Molecular function
Biological process
Cellular component

15. Data Sharing
Requires standards
Requires Volume
Requires Bandwidth

16. Shape of Things
Useful standards &
best practices exist in
many fields
Funders beginning to
understand needs for
standards / best
practices
Support for
cyberinfrastructure
Education / awareness
Journal-mandated
compliance
Funder based
compliance(?)
Good Could be better

17. ISMB/ECCB 2015, July 10-14