11 Reproducible Research and Bioinformatics Leonardo Collado-Torres @fellgernon http://lcolladotor.github.io/

Reproducible research 2 What is reproducible research? • Have you heard about it? • How would you definite it? • Is it the same as replicability? • Is it important?

Reproducible research 3 Research • Science moves forward then discoveries are replicated and reproduced Implementing Reproducible Research by Stodden, Leish, Peng

https://www.nature.com/news/1-500-scientists-lift-the-lid-on-reproducibility-1.19970

https://www.nature.com/news/1- 500-scientists-lift-the-lid-on- reproducibility-1.19970

Open Science Collaboration, Science, 2015 35/97 (36%) replications P < 0.05 in same direction

https://simplystatistics.org/2017/03/02/rr-glossy/

https://github.com/jtleek/replication_paper/blob/gh-pages/in_the_media.md

Patil, Peng & Leek, Perspectives on Psychological Science, 2016

Researchers need a new definition for replication that acknowledges variation in both the original study and in the replication study. Specifically, a study replicates if the data collected from the replication are drawn from the same distribution as the data from the original experiment. Multiple independent replications of the same study will be needed to definitively evaluate replication. Patil, Peng & Leek, Perspectives on Psychological Science, 2016 Reproducible research Key conclusions:

Reproducible research 11 Replication • Replication, the practice of independently implementing scientific experiments to validate specific findings, is the cornerstone of discovering scientific truth. Implementing Reproducible Research by Stodden, Leish, Peng

Kim et al, biorXiv, 2017

Kim et al, biorXiv, 2017

Reproducible research 14 Reproducibility: from back in 2006 • However, because of the time, expense, and opportunism of many current epidemiologic studies, it is often impossible to fully replicate their findings. An attainable minimum standard is "reproducibility," which calls for data sets and software to be made available for verifying published findings and conducting alternative analyses. Peng et al, Reproducible epidemiologic research, Am J Epidemiol., 2006

Reproducible research 15 Reproducibility • Reproducibility can be thought of as a different standard of validity from replication because it forgoes independent data collection and uses the methods and data collected by the original investigator. Implementing Reproducible Research by Stodden, Leish, Peng

Reproducible research 16 A bit more practical • The sharing of analytic data and computer codes uses to map those data into computational results is central to any comprehensive definition of reproducibility. Implementing Reproducible Research by Stodden, Leish, Peng

Reproducible research 17 Why is it important? • Except for the simplest of analyses, the computer code used to analyze a dataset is the only record that permits others to fully understand what a researcher has done. Implementing Reproducible Research by Stodden, Leish, Peng

Reproducible research 18 Drawing the line

Reproducible research 19 Together • Reproducibility is the ability to take the code and data from a previous publication, rerun the code and get the same results. Replicability is the ability to rerun an experiment and get “consistent” results with the original study using new data. Results that are not reproducible are hard to verify and results that do not replicate in new studies are harder to trust. https://simplystatistics.org/2017/03/02/rr-glossy/

Reproducible research 20 Visually Patil, Peng & Leek, biorXiv, 2016 http://biorxiv.org/content/early/2016/07/29/066803

http://science.sciencemag.org/content/354/6317/1240

http://rpubs.com/lcollado/4080

Reproducible research 23 Reproducible documents • Have you ever had your code in one file, your description of the results in another file? • Ever made copy-paste mistakes? • What if you were asked to change some models or revise the document? • Was it easy to maintain?

Reproducible research 24 Reproducible documents • What would be a reproducible document for you?

Reproducible research 25 Reproducible documents in R • R Markdown is the easiest • It's based on Markdown: simple human readable syntax • You maintain a single file! It has the • code, • figures, • description of results. • It then creates a file in the format you want to share with others.

Reproducible research 26 R Markdown http://rmarkdown.rstudio.com/

Reproducible research 27 R Markdown http://rmarkdown.rstudio.com/

https://github.com/leekgroup/polyester_code/blob/master/polyester_manuscript.Rmd Complex example

http://htmlpreview.github.io/?https://github.com/alyssafrazee/polyester_code/blob/master/polyester_manuscript.html

Reproducible research 30 Reproducible research can still be wrong! • Unfortunately, the mere reproducibility of computational results is insufficient to address the replication crisis because even a reproducible analysis can suffer from many problems—confounding from omitted variables, poor study design, missing data—that threaten the validity and useful interpretation of the results. Although improving the reproducibility of research may increase the rate at which flawed analyses are uncovered […] it does not change the fact that problematic research is conducted in the first place. Leek and Peng, PNAS, 2015

https://www.nature.com/ news/1-500-scientists- lift-the-lid-on- reproducibility-1.19970

Bioinformatics 32 Dictionary definition https://www.merriam-webster.com/dictionary/bioinformatics

Bioinformatics http://hyperphysics.phy-astr.gsu.edu/hbase/Organic/dogma.html

Bioinformatics http://www.batcallid.com/canBCIDfeatures.html

Bioinformatics https://www.evogeneao.com/learn/tree-of-life

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0 3000 6000 9000 1970 1980 1990 2000 2010 Year Yearly PDB sequences over time Bioinformatics http://www.rcsb.org/pdb/statistics/contentGrowthChart.do?content=total&seqid=100

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0e+00 5e+04 1e+05 1970 1980 1990 2000 2010 Year Total PDB sequences over time Bioinformatics http://www.rcsb.org/pdb/statistics/contentGrowthChart.do?content=total&seqid=100

Bioinformatics https://www.ncbi.nlm.nih.gov/genbank/statistics/

Bioinformatics 39 Luscombe et al., Methods Inf Med., 2011

Bioinformatics http://hyperphysics.phy-astr.gsu.edu/hbase/Organic/dogma.html

Genome Transcripts

Bioinformatics http://www.sequence-alignment.com/ https://commons.wikimedia.org/wiki/File:Sequence_alignment_dendrotoxins.jpg

Bioinformatics https://blast.ncbi.nlm.nih.gov/Blast.cgi

Bioinformatics Wilks et al., biorXiv, 2017

Bioinformatics

Bioinformatics Wilks et al., biorXiv, 2017

Bioinformatics

Bioinformatics Wilks et al., biorXiv, 2017

Bioinformatics 49 Aims • Organize data, allow new entries, data curation • Develop tools and resources that aid in the analysis of data • Use these tools to analyze the data and interpret the results in a biologically meaningful manner Luscombe et al., Methods Inf Med., 2011

Bioinformatics 50 Welch et al., PLoS Comp Bio, 2014 Recommended curriculum

http://www.nature.com/news/don-t-let-useful-data-go-to-waste-1.21555

AUCAGUCGAUCACCGAU transcription RNA translation protein ACTGACCTAGATCAGTCGATCGATCGTATACGATTACAAAATCATCGGCAT DNA M M M slide adapted from Alyssa Frazee

AUCAGUCGAUCACCGAU transcription RNA translation protein ACTGACCTAGATCAGTCGATCGATCGTATACGATTACAAAATCATCGGCAT DNA M M M Bisulfite RNA ChIP Genome slide adapted from Alyssa Frazee

AUCAGUCGAUCACCGAU transcription RNA translation protein ACTGACCTAGATCAGTCGATCGATCGTATACGATTACAAAATCATCGGCAT DNA M M M RNA slide adapted from Alyssa Frazee

Genome Transcripts Reads

@22:16362385-16362561W:ENST00000440999:2:177:-40:244:S/2 CCAGCCCACCTGAGGCTTCTTTTTCCTTCCCAAGCCACATCACCATCCTGGTGGAACTCTCCTGTGAGGACAGCCA + GGFFGBGIIIIIIIIIIIIIIEGEHGHHIIIIIIIIHFHBB2/:=??EGGGEGFHHIHHEDBD?@@DDHHD @22:16362385-16362561W:ENST00000440999:3:177:-56:294:S/2 GCGTGAGCCACAGGGCCCAGCCCACCTGAGGCTTCTTTTTCCTTCCCAAGCCACATCACCATCCTGGTGGAACTCT + @=ABBBBIIIIIIIIHHGGGGIIDBDIIIIIIGIIIIHIIIIHFDD@BBDBGGFIDEE8DCC/29>BGFCGHHHGF @22:16362385-16362561W:ENST00000440999:4:177:137:254:S/1 TCACCATCCTGGTGGAACTCTCCTGTGAGGACAGCCAAGGCCTGAACTACCTGCaGTGGGGAGCACCTCAGGGTTT + DDGBBCGGGIGGGBDDDHIIGGDGD77=BDIIIIIIIIFHHHHIIIHEFFHGGDD8A>DEGHHIFDDHH8@BEDDI @22:16362385-16362561W:ENST00000440999:5:177:68:251:S/2 AGGGTTTGCCCAGGCAACCAGCCAGCCCTGGTCCAAGGCATCCTGGAGCGAGTTGTGGATGGCAAAAAGACNCGCC + HIGHIHFHEGE4111:.;8@?@HDIIIIIIIEGGIHHHIIGA?=:FIIIDD8.02506A8=AC############# @22:16362385-16362561W:ENST00000440999:6:177:348:453:S/1 AAGGCCTGAACTACCTGCGGTGGGGAGCACCTCAGGGTTTGCCCAGGCAACCAGCCAGCCCTGGTCCAAGGCATCC + B9?@8=42:E@GDEDIIIIIGGHIIIFBEEAGIIDIIDHHGGHIIEGEIIIIIHIHFHFFEEFGGGGGB88>:DGH @22:51205934-51222090C:ENST00000464740:132:612:223:359:S/2 GGAAGTATGATGCTGATGACAACGTGAAGATCATCTGCCTGGGAGACAGCGCAGTGGGCAAATCCAAACTCATGGA + IIEHHHHHIIIIIIIHGGDGHHEDDG8=;?==19;<<>D@@GGGIIHIIHGGDDHGBA=ABEG@@DFCCAA<:=>8 @22:51205934-51222090C:ENST00000464740:125:612:-1:185:S/1 TGGAGTGCGCTGCGGCGCGAGCTGGGCCGGCGGGCGTGGTTCGAGAGCGCGCAGAGTCCAGACTGGCGGCAGGGCC + HHIIIHIDGG@;=@GIIIIIDDGBBBEDB@8>5554,/':9B@@C?==@1:2@?=GG=;

GTEx TCGA slide adapted from Shannon Ellis

SRA

Slide adapted from Ben Langmead

Genome

http://rail.bio/ Slide adapted from Ben Langmead

http://blogs.citrix.com/2012/10/17/announcing-general-availability-of-sharefile-with-storagezones/

Obstacle: our research moves (spot) markets Spike in market price due to preprocessing job flows slide adapted from Jeff Leek

Obstacle: our research moves (spot) markets Weekday market volatility Weekend EC2 inactivity slide adapted from Jeff Leek

https://jhubiostatistics.shinyapps.io/recount/

jx 1 jx 2 jx 3 jx 4 jx 5 jx 6 Coverage Reads Gene Isoform 1 Isoform 2 Potential isoform 3 exon 1 exon 2 exon 3 exon 4 Expressed region 1: potential exon 5

> library('recount') > download_study( 'ERP001942', type='rse-gene') > load(file.path('ERP001942 ', 'rse_gene.Rdata')) > rse <- scale_counts(rse_gene) https://github.com/leekgroup/recount-analyses/

expression data for ~70,000 human samples GTEx N=9,962 TCGA N=11,284 SRA N=49,848 samples expression estimates gene exon junctions ERs slide adapted from Shannon Ellis

expression data for ~70,000 human samples Answer meaningful questions about human biology and expression GTEx N=9,962 TCGA N=11,284 SRA N=49,848 samples expression estimates gene exon junctions ERs slide adapted from Shannon Ellis

expression data for ~70,000 human samples samples phenotypes ? GTEx N=9,962 TCGA N=11,284 SRA N=49,848 samples expression estimates gene exon junctions ERs Answer meaningful questions about human biology and expression slide adapted from Shannon Ellis

Category Frequency F 95 female 2036 Female 51 M 77 male 1240 Male 141 Total 3640 Even when information is provided, it’s not always clear… sra_meta$Se x “1 Male, 2 Female”, “2 Male, 1 Female”, “3 Female”, “DK”, “male and female” “Male (note: ….)”, “missing”, “mixed”, “mixture”, “N/A”, “Not available”, “not applicable”, “not collected”, “not determined”, “pooled male and female”, “U”, “unknown”, “Unknown” slide adapted from Shannon Ellis

Goal : to accurately predict critical phenotype information for all samples in recount gene, exon, exon-exon junction and expressed region RNA-Seq data SRA Sequence Read Archive N=49,848 GTEx Genotype Tissue Expression Project N=9,662 divide samples build and optimize phenotype predictor training set predict phenotypes across SRA samples test accuracy of predictor predict phenotypes across samples in TCGA test set TCGA The Cancer Genome Atlas N=11,284 slide adapted from Shannon Ellis

Sex prediction is accurate across data sets Number of Regions 20 20 20 20 Number of Samples (N) 4,769 4,769 11,245 3,640 99.8% 99.6% 99.4% 88.5% slide adapted from Shannon Ellis

Number of Regions 589 589 589 589 589 Number of Samples (N) 4,769 4,769 613 6,579 8,951 97.3% 96.5% 91.0% 70.2% Prediction is more accurate in healthy tissue 50.6% slide adapted from Shannon Ellis

expression data for ~70,000 human samples samples phenotypes ? GTEx N=9,962 TCGA N=11,284 SRA N=49,848 samples expression estimates gene exon junctions ERs Answer meaningful questions about human biology and expression sex tissue M Blood F Heart F Liver slide adapted from Shannon Ellis

bioconductor.org/packages/derfinder bioconductor.org/packages/recount > biocLite(“derfinder”) > biocLite(“recount”) http://rail.bio $ ./install-rail-rna-V

Collaborators The Leek Group Jeff Leek Shannon Ellis Hopkins Ben Langmead Chris Wilks Kai Kammers Kasper Hansen Margaret Taub OHSU Abhinav Nellore LIBD Andrew Jaffe Emily Burke Stephen Semick Carrie Wright Amanda Price Nina Rajpurohit Funding NIH R01 GM105705 NIH 1R21MH109956 CONACyT 351535 AWS in Education Seven Bridges IDIES SciServer

Bioinformatics 78 Staying Current in Bioinformatics & Genomics • “focused on applied methodology and study design rather than any particular phenotype, model system, disease, or specific method” • “a software implementation that’s well documented, actively supported, and performs well in fair benchmarks” http://www.gettinggeneticsdone.com/

Bioinformatics 79 Staying Current in Bioinformatics & Genomics • Twitter • Blogs • Some websites • Pre-prints • Journal articles http://www.gettinggeneticsdone.com/

11 Reproducible Research and Bioinformatics Leonardo Collado-Torres @fellgernon http://lcolladotor.github.io/