Bucknell University
Biology 364/664 Spring 2019
Textbooks
OIS: Open Intro Statistics 3rd Edition, https://leanpub.com/openintro-statistics/read_full
R4DS: R for Data Science, by Garrett Grolemund and Hadley Wickham, http://r4ds.had.co.nz/
PRP: R Programming for Data Science, by Roger D. Peng, https://leanpub.com/rprogramming/read_full
EDA: Exploratory Data Analysis, by Roger D. Peng, https://leanpub.com/exdata/read_full
Hart: A Primer in Biological Data Analysis and Visualization Using R, by Gregg Hartvigsen, ISBN: 9780231166997
HBS: Handbook of Biological Statistics, by John H. McDonald , http://www.biostathandbook.com/
Helpful Links
Setting up R Studio, Git, and Github: https://happygitwithr.com/index.html
Troubleshooting Git: https://bookdown.org/rdpeng/RProgDA/version-control-and-github.html
Choosing the correct statistical test: https://stats.idre.ucla.edu/other/mult-pkg/whatstat/
Choosing colors for data visualization: https://blog.datawrapper.de/colors/
Open Science Framework: https://osf.io/
Preregistration at the Center for Open Science: https://cos.io/prereg/
Simply Statistics: A statistics blog by Rafa Irizarry, Roger Peng, and Jeff Leek: https://simplystatistics.org/
Retraction Watch: https://retractionwatch.com/
Scientists rise up against statistical significance: https://www.nature.com/articles/d41586-019-00857-9
Review of Gene Expression: https://www.khanacademy.org/science/biology/gene-regulation/gene-regulation-in-eukaryotes/a/overview-of-eukaryotic-gene-regulation (includes a review of alternative splicing and rna interference)
Google Flu: http://science.sciencemag.org/content/343/6176/1203
Integrated Genome Viewer. Download: http://software.broadinstitute.org/software/igv/download Online App: https://igv.org/app/
g:Profiler: https://biit.cs.ut.ee/gprofiler/gost
Cytoscape: https://cytoscape.org/
Videos
Illumina Sequencing: https://youtu.be/fCd6B5HRaZ8
Introduction to RNA-Seq for Researchers: https://youtu.be/7BLS_YY9HeM
Edward Tufte, The Future of Data Analysis: https://youtu.be/rHUDJ8RyseQ?t=151
Not Helpful (but Fun) Links
XKCD: https://xkcd.com/
Pedromics: https://www.facebook.com/pedromics
For Next Year
It’s too late for this stuff in 2019!
Common statistical tests are linear models (or: how to teach stats) https://lindeloev.github.io/tests-as-linear/
Reproducible Research Using RMarkdown and Git through RStudio https://rpubs.com/marschmi/105639
Explaining PCA to your family https://stats.stackexchange.com/questions/2691/making-sense-of-principal-component-analysis-eigenvectors-eigenvalues
From data to Viz: https://www.data-to-viz.com/
RNA-Seq Tutorial: https://galaxyproject.org/tutorials/rb_rnaseq/
Overview of Statistical Tests: https://www.r-bloggers.com/overview-of-statistical-tests/