Reproducibility 2017

Reproducible research and bioinformatics

Transcript

1. 11 Reproducible Research and Bioinformatics Leonardo Collado-Torres @fellgernon http://lcolladotor.github.io/
2. 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?
3. Reproducible research 3 Research • Science moves forward then discoveries are replicated and reproduced Implementing Reproducible Research by Stodden, Leish, Peng
4. https://www.nature.com/news/1-500-scientists-lift-the-lid-on-reproducibility-1.19970
5. https://www.nature.com/news/1- 500-scientists-lift-the-lid-on- reproducibility-1.19970
6. Open Science Collaboration, Science, 2015 35/97 (36%) replications P < 0.05 in same direction
7. https://simplystatistics.org/2017/03/02/rr-glossy/
8. https://github.com/jtleek/replication_paper/blob/gh-pages/in_the_media.md
9. Patil, Peng & Leek, Perspectives on Psychological Science, 2016
10. 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:
11. 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
12. Kim et al, biorXiv, 2017
13. Kim et al, biorXiv, 2017
14. 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
15. 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
16. 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
17. 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
18. Reproducible research 18 Drawing the line
19. 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/
20. Reproducible research 20 Visually Patil, Peng & Leek, biorXiv, 2016 http://biorxiv.org/content/early/2016/07/29/066803
21. http://science.sciencemag.org/content/354/6317/1240
22. http://rpubs.com/lcollado/4080
23. 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?
24. Reproducible research 24 Reproducible documents • What would be a reproducible document for you?
25. 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.
26. Reproducible research 26 R Markdown http://rmarkdown.rstudio.com/
27. Reproducible research 27 R Markdown http://rmarkdown.rstudio.com/
28. https://github.com/leekgroup/polyester_code/blob/master/polyester_manuscript.Rmd Complex example
29. http://htmlpreview.github.io/?https://github.com/alyssafrazee/polyester_code/blob/master/polyester_manuscript.html
30. 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
31. https://www.nature.com/ news/1-500-scientists- lift-the-lid-on- reproducibility-1.19970
32. Bioinformatics 32 Dictionary definition https://www.merriam-webster.com/dictionary/bioinformatics
33. Bioinformatics http://hyperphysics.phy-astr.gsu.edu/hbase/Organic/dogma.html
34. Bioinformatics http://www.batcallid.com/canBCIDfeatures.html
35. Bioinformatics https://www.evogeneao.com/learn/tree-of-life
36. • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • 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
37. • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • 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
38. Bioinformatics https://www.ncbi.nlm.nih.gov/genbank/statistics/
39. Bioinformatics 39 Luscombe et al., Methods Inf Med., 2011
40. Bioinformatics http://hyperphysics.phy-astr.gsu.edu/hbase/Organic/dogma.html
41. Genome Transcripts
42. Bioinformatics http://www.sequence-alignment.com/ https://commons.wikimedia.org/wiki/File:Sequence_alignment_dendrotoxins.jpg
43. Bioinformatics https://blast.ncbi.nlm.nih.gov/Blast.cgi
44. Bioinformatics Wilks et al., biorXiv, 2017
45. Bioinformatics
46. Bioinformatics Wilks et al., biorXiv, 2017
47. Bioinformatics
48. Bioinformatics Wilks et al., biorXiv, 2017
49. 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
50. Bioinformatics 50 Welch et al., PLoS Comp Bio, 2014 Recommended curriculum
51. http://www.nature.com/news/don-t-let-useful-data-go-to-waste-1.21555
52. AUCAGUCGAUCACCGAU transcription RNA translation protein ACTGACCTAGATCAGTCGATCGATCGTATACGATTACAAAATCATCGGCAT DNA M M M slide adapted from Alyssa Frazee
53. AUCAGUCGAUCACCGAU transcription RNA translation protein ACTGACCTAGATCAGTCGATCGATCGTATACGATTACAAAATCATCGGCAT DNA M M M Bisulfite RNA ChIP Genome slide adapted from Alyssa Frazee
54. AUCAGUCGAUCACCGAU transcription RNA translation protein ACTGACCTAGATCAGTCGATCGATCGTATACGATTACAAAATCATCGGCAT DNA M M M RNA slide adapted from Alyssa Frazee
55. Genome Transcripts Reads
56. @22:16362385-16362561W:ENST00000440999:2:177:-40:244:S/2 CCAGCCCACCTGAGGCTTCTTTTTCCTTCCCAAGCCACATCACCATCCTGGTGGAACTCTCCTGTGAGGACAGCCA + GGFF<BB=>GBGIIIIIIIIIIIIIIEGEHGHHIIIIIIIIHFHBB2/:=??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=;<HHHHGIIHHEC-;;3? 3 gb
57. GTEx TCGA slide adapted from Shannon Ellis
58. SRA
59. Slide adapted from Ben Langmead
60. Genome
61. http://rail.bio/ Slide adapted from Ben Langmead
62. http://blogs.citrix.com/2012/10/17/announcing-general-availability-of-sharefile-with-storagezones/
63. Obstacle: our research moves (spot) markets Spike in market price due to preprocessing job flows slide adapted from Jeff Leek
64. Obstacle: our research moves (spot) markets Weekday market volatility Weekend EC2 inactivity slide adapted from Jeff Leek
65. https://jhubiostatistics.shinyapps.io/recount/
66. 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
67. > 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/
68. 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
69. 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
70. 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
71. 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
72. 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
73. 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
74. 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
75. 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
76. bioconductor.org/packages/derfinder bioconductor.org/packages/recount > biocLite(“derfinder”) > biocLite(“recount”) http://rail.bio $ ./install-rail-rna-V
77. 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
78. 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/
79. Bioinformatics 79 Staying Current in Bioinformatics & Genomics • Twitter • Blogs • Some websites • Pre-prints • Journal articles http://www.gettinggeneticsdone.com/
80. 11 Reproducible Research and Bioinformatics Leonardo Collado-Torres @fellgernon http://lcolladotor.github.io/