References

General resources

• UCLA Statistical Consulting: https://stats.oarc.ucla.edu/

• Statistics for Biologists portfolio: https://www.nature.com/collections/qghhqm

• Motulsky, H. (2014). Intuitive biostatistics: a nonmathematical guide to statistical thinking. Oxford University Press, USA.

R, Rstudio, markdown, Quarto

• Commonly used R packages for different tasks

• R packages in the tidyverse, which includes:

  • dplyr: https://dplyr.tidyverse.org/
  • ggplot2: https://ggplot2.tidyverse.org/
  • tidyr: https://tidyr.tidyverse.org/

• Wickham, H., Çetinkaya-Rundel, M., & Grolemund, G. (2023). R for data science. “O’Reilly Media, Inc.”.

  • Full book available here: https://r4ds.hadley.nz/

• Xie, Y., Allaire, J. J., & Grolemund, G. (2018). R markdown: The definitive guide. Chapman and Hall/CRC.

  • Full book available here: https://bookdown.org/yihui/rmarkdown/

• Cheatsheets on various topics, including markdown: https://posit.co/resources/cheatsheets/

• Quarto: “Next gen” of markdown

• Imai, K., & Williams, N. W. (2022). Quantitative Social Science: An Introduction in Tidyverse. Princeton University Press.

Genomics

Computational Genomics with R

Bareyre, F. M., & Schwab, M. E. (2003). Inflammation, degeneration and regeneration in the injured spinal cord: insights from DNA microarrays. Trends in neurosciences, 26(10), 555-563.

ggplot

• Wickham, H. (2010). A layered grammar of graphics. Journal of computational and graphical statistics, 19(1), 3-28.

• Wickham, H. (2016). ggplot2: elegant graphics for data analysis. Springer.

• Wilkinson, L. (2005). The Grammar of Graphics (2nd ed.). Statistics and Computing, New York: Springer.

Graphical comparisons

• Cumming, G., & Finch, S. (2005). Inference by Eye: Confidence Intervals and How to Read Pictures of Data. American Psychologist, 60(2), 170–180. https://doi.org/10.1037/0003-066X.60.2.170

• Cumming, G. (2009). Inference by eye: Reading the overlap of independent confidence intervals. Statistics in medicine, 28(2), 205-220.

Missing data

Enders, C. K. (2022). Applied missing data analysis. Guilford Publications.

Enders, C. K. (2023). Missing data: An update on the state of the art. Psychological Methods. https://doi.org/10.1037/met0000563

Little, R. J., & Rubin, D. B. (2019). Statistical analysis with missing data (Vol. 793). John Wiley & Sons.

National Research Council, Division of Behavioral, Committee on National Statistics, & Panel on Handling Missing Data in Clinical Trials. (2010). The prevention and treatment of missing data in clinical trials.

Rubin, D. B. (1976). Inference and missing data. Biometrika, 63(3), 581-592.

Tests of proportions

Agresti, A. (1996). An introduction to categorical data analysis.

Agresti, A. (2012). Categorical data analysis (Vol. 792). John Wiley & Sons.

Special Topics for Final Project

Heterogeneity

• Richter SH. Systematic heterogenization for better reproducibility in animal experimentation. Lab animal. 2017 Sep;46(9):343-9.

• Usui T, Macleod MR, McCann SK, Senior AM, Nakagawa S. Meta-analysis of variation suggests that embracing variability improves both replicability and generalizability in preclinical research. PLoS biology. 2021 May 19;19(5):e3001009.

• Voelkl B, Vogt L, Sena ES, Würbel H. Reproducibility of preclinical animal research improves with heterogeneity of study samples. PLoS biology. 2018 Feb 22;16(2):e2003693.

\(p\)-value misuses

• Cohen, J. (1994). The earth is round (p<. 05). American psychologist, 49(12), 997.

• Head ML, Holman L, Lanfear R, Kahn AT, Jennions MD. The extent and consequences of p-hacking in science. PLoS Biol. 2015 Mar 13;13(3):e1002106.

• Gelman A, Stern H. The difference between “significant” and “not significant” is not itself statistically significant. The American Statistician. 2006 Nov 1;60(4):328-31.

• Greenland S, Senn SJ, Rothman KJ, Carlin JB, Poole C, Goodman SN, Altman DG. Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. European journal of epidemiology. 2016 Apr;31(4):337-50.

• Rosnow, R. L., & Rosenthal, R. (1989). Statistical procedures and the justification of knowledge in psychological science. American Psychologist, 44(10), 1276-1284.

Reporting in pre-clinical sciences

• Avey MT, Moher D, Sullivan KJ, Fergusson D, Griffin G, Grimshaw JM, Hutton B, Lalu MM, Macleod M, Marshall J, Mei SH. The devil is in the details: incomplete reporting in preclinical animal research. PLoS One. 2016 Nov 17;11(11):e0166733.

• Percie du Sert N, Ahluwalia A, Alam S, Avey MT, Baker M, Browne WJ, Clark A, Cuthill IC, Dirnagl U, Emerson M, Garner P. Reporting animal research: Explanation and elaboration for the ARRIVE guidelines 2.0. PLoS biology. 2020 Jul 14;18(7):e3000411.

• Serghiou S, Contopoulos-Ioannidis DG, Boyack KW, Riedel N, Wallach JD, Ioannidis JP. Assessment of transparency indicators across the biomedical literature: How open is open?. PLoS biology. 2021 Mar 1;19(3):e3001107.

Reproducibility in science

• Begley CG, Ellis LM. Raise standards for preclinical cancer research. Nature. 2012 Mar;483(7391):531-3.

• Begley CG, Ioannidis JP. Reproducibility in science: improving the standard for basic and preclinical research. Circulation research. 2015 Jan 2;116(1):116-26.

• Errington TM, Denis A, Perfito N, Iorns E, Nosek BA. Reproducibility in Cancer Biology: Challenges for assessing replicability in preclinical cancer biology. eLife. 2021 Dec 7;10:e67995.

Sex as a biological variable

• Beery AK. Inclusion of females does not increase variability in rodent research studies. Current opinion in behavioral sciences. 2018 Oct 1;23:143-9.

• Garcia-Sifuentes Y, Maney DL. Reporting and misreporting of sex differences in the biological sciences. eLife 2021;10:e70817

• Woitowich NC, Beery A, Woodruff T. Meta-research: a 10-year follow-up study of sex inclusion in the biological sciences. eLife. 2020 Jun 9;9:e56344.