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RNAseq:
A five course meal
#DIBSI2019
Rayna M. Harris
@raynamharris
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Rayna M. Harris
Postdoctoral scholar at UC Davis
Scientist, Educator, Community Builder, Translator
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Special thanks
to the
Data Intensive Biology Lab
and the
Birds, Brain, & Banter Lab
http://calisilab.ucdavis.edu/
http://ivory.idyll.org/lab/
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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
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Who reads books?
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Read R for Data Science
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R 4 Data Science workflow
https://r4ds.had.co.nz/ 7
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Data science as a five course meal
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Data snacks prevent “hanger”
Appetizer
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Data snacks and source code
`library()`
`source()`
`data()`
Substantial, complex, noteworthy, valuable
Provides insights into what follows
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Soup & salad, tidy then transform
Salad Soup
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Data wrangling
Appetizer
Salad Soup
Wrangle
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Data wrangling
Simultaneously fun and painful
Learn from your mistakes
Write good documentation
Write tests
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The tidyverse and scientific python are
your data wrangling friends
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The DESeq2 salad
http://bioconductor.org/packages/devel/bioc/vignettes/DESeq2/inst/doc/DESeq2.html 15
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DESeq2 colData and counts
https://biojupies.cloud/notebook/AHnOIzJEq 16
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ncol(counts) == nrow(colData)
https://biojupies.cloud/notebook/AHnOIzJEq 17
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Summarized experiment soup
http://bioconductor.org/packages/devel/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#differential-expression-analysis 18
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“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
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“Communication breakdown, it’s always the same
I’m having a nervous breakdown, drive me insane.”
Led Zeppelin
Nuts
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Procrastigraphing: excessive creation of data visualizations
Communicate
Nuts
Dessert
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Modeling is challenging. So many choices.
Main course
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If you were at a banquet, would you order?
A. Lobster
B. Steak
C. Lobster and steak
D. Other
E. Lobster, steak, and other
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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
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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
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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
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BioJupies for instant
RNA-seq visualizations
in the cloud
https://biojupies.cloud/notebook/AHnOIzJEq
https://twitter.com/i/status/1062745599538282498
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https://biojupies.cloud/notebook/AHnOIzJEq
Quickly assess sample quality and variation
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https://biojupies.cloud/notebook/AHnOIzJEq
Quickly view results of gene ontology enrichment analyses
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https://biojupies.cloud/notebook/AHnOIzJEq
Quickly explore patterns of differential gene expression
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https://biojupies.cloud/notebook/AHnOIzJEq
But how many dots are red and blue?
?
?
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https://biojupies.cloud/notebook/AHnOIzJEq
But, what if I want to view many volcano plots?
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Convert .Rmd files
to GitHub pages to
communicate results
https://macmanes-lab.github.io/austinCORT/
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Open source options for programming
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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
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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
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Onward to experiments with many factors
https://github.com/macmanes-lab/DoveParentsRNAseq/
Made for a poster at the Society for Behavioral Neuroendocrinology
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Identify time-like changes in specific genes
https://github.com/macmanes-lab/DoveParentsRNAseq/
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Find meaningful principle components of variation
https://github.com/macmanes-lab/DoveParentsRNAseq/
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Create functions to run all pairwise comparisons
`contrast = c(“treatment”, “varB”, “varA”)`
https://github.com/macmanes-lab/DoveParentsRNAseq/
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The risk of unintentional p-hacking
http://www.stat.columbia.edu/~gelman/research/unpublished/p_hacking.pdf
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The risk of unintentional p-hacking
http://www.stat.columbia.edu/~gelman/research/unpublished/p_hacking.pdf
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Exploratory analyses vs. model testing
Explore
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60% 20% 20%
training set query set testing set
Should we be analyzing all the data all the time?
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“Soup to nuts: setting up a new RNAseq analysis”Titus
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“A five course meal is an excellent analogy!”Rayna
Appetizer Soup & Salad Dessert Nuts
Main course
:)
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Framework for understanding RNAseq workflows
R and/or Python or other
library()
data()
counts dds
colData
ggplot()
matplotlib
Rmarkdown
Jupyter
GitHub pages
~ condition
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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
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What questions
do you have?
@raynamharris
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