RNAseq: A five course meal

These slides are from a talk I gave as part of a two-week next-generation sequencing analysis workshop (https://angus.readthedocs.io/en/2019/). In this talk, I provide a framework for understanding RNA-seq workflows that is based on the R for Data Science workflow of "import, tidy, transform, visualize, model, and communicate" mantra (https://r4ds.had.co.nz/). I also provide strategies for "healthy" workflows by making analogies to a five-course meal.

Transcript

1. RNAseq: A five course meal #DIBSI2019 Rayna M. Harris @raynamharris 1
2. Rayna M. Harris Postdoctoral scholar at UC Davis Scientist, Educator, Community Builder, Translator 2
3. Special thanks to the Data Intensive Biology Lab and the Birds, Brain, & Banter Lab http://calisilab.ucdavis.edu/ http://ivory.idyll.org/lab/ 3
4. I learned R and RNAseq in communities of practice The University of Texas at Austin, University California, Davis, Data Carpentry, Software Carpentry, The Carpentries-es, @cienciaPR @RLadiesGlobal @RLadiesBA @r4ds_es #DISBI2018 4
5. Who reads books? 5
6. Read R for Data Science 6
7. R 4 Data Science workflow https://r4ds.had.co.nz/ 7
8. Data science as a five course meal 8
9. Data snacks prevent “hanger” Appetizer 9
10. Data snacks and source code `library()` `source()` `data()` Substantial, complex, noteworthy, valuable Provides insights into what follows 10
11. Soup & salad, tidy then transform Salad Soup 11
12. Data wrangling Appetizer Salad Soup Wrangle 12
13. Data wrangling Simultaneously fun and painful Learn from your mistakes Write good documentation Write tests 13
14. The tidyverse and scientific python are your data wrangling friends 14
15. The DESeq2 salad http://bioconductor.org/packages/devel/bioc/vignettes/DESeq2/inst/doc/DESeq2.html 15
16. DESeq2 colData and counts https://biojupies.cloud/notebook/AHnOIzJEq 16
17. ncol(counts) == nrow(colData) https://biojupies.cloud/notebook/AHnOIzJEq 17
18. Summarized experiment soup http://bioconductor.org/packages/devel/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#differential-expression-analysis 18
19. “You can’t have any pudding if you don’t eat yer meat. How can you have any pudding if you don’t eat yer meat?” Pink Floyd Dessert 19
20. “Communication breakdown, it’s always the same I’m having a nervous breakdown, drive me insane.” Led Zeppelin Nuts 20
21. Procrastigraphing: excessive creation of data visualizations Communicate Nuts Dessert 21
22. Modeling is challenging. So many choices. Main course 22
23. If you were at a banquet, would you order? A. Lobster B. Steak C. Lobster and steak D. Other E. Lobster, steak, and other 23
24. If you were an RNAseq workflow, would you use: A. R B. Python C. R and Python D. Other E. An R/Python/other mix 24
25. FlavoRs of differential gene expression models library("DESeq2") dds <- DESeqDataSetFromMatrix(countData = cts, colData = coldata, design = ~ condition) results(dds, contrast=c("condition","B","A")) http://bioconductor.org/packages/devel/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#differential-expression-analysis 25
26. Strategies for a well-balanced RNAseq analysis R and/or Python or other library() data() counts dds colData Rmarkdown Jupyter GitHub pages ~ condition Wrangle Explore ggplot() matplotlib Communicate 26
27. BioJupies for instant RNA-seq visualizations in the cloud https://biojupies.cloud/notebook/AHnOIzJEq https://twitter.com/i/status/1062745599538282498 27
28. https://biojupies.cloud/notebook/AHnOIzJEq Quickly assess sample quality and variation 28
29. https://biojupies.cloud/notebook/AHnOIzJEq Quickly view results of gene ontology enrichment analyses 29
30. https://biojupies.cloud/notebook/AHnOIzJEq Quickly explore patterns of differential gene expression 30
31. https://biojupies.cloud/notebook/AHnOIzJEq But how many dots are red and blue? ? ? 31
32. https://biojupies.cloud/notebook/AHnOIzJEq But, what if I want to view many volcano plots? 32
33. Convert .Rmd files to GitHub pages to communicate results https://macmanes-lab.github.io/austinCORT/ 33
34. Open source options for programming 34
35. My progress: from a novice with data and tools https://github.com/raynamharris/DissociationTest Harris, Kao, Alarcón, Hofmann, Fenton 2017 https://www.biorxiv.org/content/10.1101/153585v1 35
36. To a practitioner with reproducible workflows https://github.com/raynamharris/DissociationTest Harris, Kao, Alarcón, Hofmann, Fenton 2019 https://onlinelibrary.wiley.com/doi/10.1002/hipo.23095 36
37. Onward to experiments with many factors https://github.com/macmanes-lab/DoveParentsRNAseq/ Made for a poster at the Society for Behavioral Neuroendocrinology 37
38. Identify time-like changes in specific genes https://github.com/macmanes-lab/DoveParentsRNAseq/ 38
39. Find meaningful principle components of variation https://github.com/macmanes-lab/DoveParentsRNAseq/ 39
40. Create functions to run all pairwise comparisons `contrast = c(“treatment”, “varB”, “varA”)` https://github.com/macmanes-lab/DoveParentsRNAseq/ 40
41. The risk of unintentional p-hacking http://www.stat.columbia.edu/~gelman/research/unpublished/p_hacking.pdf 41
42. The risk of unintentional p-hacking http://www.stat.columbia.edu/~gelman/research/unpublished/p_hacking.pdf 42
43. Exploratory analyses vs. model testing Explore 43
44. 60% 20% 20% training set query set testing set Should we be analyzing all the data all the time? 44
45. “Soup to nuts: setting up a new RNAseq analysis”Titus 45
46. “A five course meal is an excellent analogy!”Rayna Appetizer Soup & Salad Dessert Nuts Main course :) 46
47. Framework for understanding RNAseq workflows R and/or Python or other library() data() counts dds colData ggplot() matplotlib Rmarkdown Jupyter GitHub pages ~ condition 47
48. Strategies for creating healthy RNAseq workflows R and/or Python or other library() data() counts dds colData ggplot() matplotlib Rmarkdown Jupyter GitHub pages ~ condition Communicate Wrangle Explore Model 48
49. What questions do you have? @raynamharris 49