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Beyond The Bench Workshops
- 1. #BeyondTheBench #BECareer2013 #CurrentExchange ORGANIZERS: SPONSORS:
- 3. ur yo ing ce lish sen il tab pre oukhal Es ne b er t A Ro b nli o
- 6. Why LinkedIn? • Online CV + networking • Recruiters use LinkedIn • Find jobs posted on LinkedIn • Apply to jobs
- 11. #3: T witte r
- 17. Step 3: Have an awesome portfolio
- 18. Now what ?
- 21. A language all scientists should know How R helped me look at billions of genotypes and how it can help you too Mitchell Bekritsky WSBS Graduate Student
- 22. What is R? • Language for statistical analysis, data manipulation and graphics • Open source • Flexible language • Powerful built-in functions • Strong user community • Publication quality graphs • Free! Graphic from h=p://blenditbayes.blogspot.com/2013/06/visualising-‐crime-‐hotspots-‐in-‐england_25.html
- 24. Who uses R? Source: h=p://www.revoluKonanalyKcs.com/what-‐is-‐open-‐source-‐r/companies-‐using-‐r.php
- 25. What is R used for? • Movie recommendations • Clinical drug development • Credit risk analysis • News graphics • Tailoring online advertising • Modeling oil spills • Predicting economic activity • Predicting election outcomes Graphic from h=p://www.nyKmes.com/interacKve/2009/06/25/arts/0625-‐jackson-‐graphic.html
- 26. But I’m a biologist…
- 27. How R helped me see my data • First time looking at microsatellite genotypes • How many microsatellites differ from reference genome? • By how much? Problems: – Lots of data (4.7 million genotypes) – Complex information – Too big for Excel – No good graphics in Excel either
- 28. One of my first graphs in R Lessons learned about my data • Lots of microsatellites differ from reference by a little bit • Thousands differ by ± 20 bp • 8.27% of all microsatellites differ from reference (~400k) Lessons learned about my graph • This is a terrible graph
- 29. A bad R graph is better than no R graph Bad graphs helped me • Understand my data better • Improve my analyses • Improve how I communicate my data • R has incredible flexibility for graphing—if you can dream it, you can probably build it
- 30. A bad R graph is better than no R graph Bad graphs helped me • Understand my data better • Improve my analyses • Improve how I communicate my data • R has incredible flexibility for graphing—if you can dream it, you can probably build it My best R graphs make one point clearly without clutter
- 31. For example…
- 32. How R saved my thesis • Processing lots of sequencing data in hundreds of people • Too many people and processes to monitor all steps of pipeline by eye while data was being processed Sanity check • After data processing did data look bi-allelic?
- 33. How R saved my thesis • Processing lots of sequencing data in hundreds of people • Too many people and processes to monitor all steps of pipeline by eye while data was being processed Sanity check • After data processing did data look bi-allelic? No!!
- 34. Troubleshooting using R • People don’t actually have massive deletions and amplifications • My pipeline was deleting files because of a bug, which would remove large chunks of chromosomes • Thanks to R, I found people where this had happened, tracked down the bug, and didn’t report massive CNVs in autistic children Side note • If it looks too good to be true, it probably is
- 35. R helped me build a better genotyper • Some non-reference alleles aren’t covered well • Leads to incorrect genotype calls Problem • How do I develop a smarter genotyper and know that it works?
- 36. R helped me build a better genotyper • Some non-reference alleles chr19:54772760 A repeat, reference length 8 aren’t covered well Genotypes 100 • Leads to incorrect genotype works? 60 40 20 0 genotyper and know that it 10 bp allele coverage • How do I develop a smarter 80 calls Problem 10|-1 10|10 8|-1 8|10 8|8 0 20 40 60 8 bp allele coverage 80 100
- 37. Modeling genotypes in R • Built a model for biased genotypes in R • Model helped me build a more accurate genotyper • When applied to real data, clear improvements
- 38. R finds de novo mutations for me • >300 million genotypes • How do I find de novo mutations in all that data? R to the rescue!
- 39. What R has done for me Data mining • Finding de novo mutations • Quality control for my data Data manipulation • Converting raw read counts to genotypes Data simulation and modeling • Finding ways to improve my genotyper Data visualization
- 40. R has extensive support for biologists Bioconductor is an incredible resource for biological analyses in R • Microarrays • Differential expression (DESeq, edgeR, cummeRbund) • Gene models • Flow cytometry (flowCore, flowStats, flowViz) • Interacting with Ensembl, Cosmic, Gramene, etc. (biomaRt)
- 41. Installing R • R can be downloaded from rproject.org • R runs on PCs, Macs and Linux computers • The R project website has an R manual to get you started
- 42. Working in R Native R interface can be hard to work with • Lots of windows • Difficult to keep things organized
- 43. RStudio interface • All your variables, help pages, script windows and consoles in one place • Highlights R code for easier programming • Tabbed windows for multiple scripts • History saves all previous commands, plot history saves all previous plots • Find it at rstudio.com
- 44. Learning R Many online tutorials • R has its own introduction • Statistics Using R with Biological Examples Take interesting data, use it to explore R • Plot, graph, use statistical tests Ask someone who knows R • Getting started is pretty easy • Learn what you need when you need it
- 45. Thanks!!
- 47. The Bioscience Entreprise Club is dedicated to helping CSHL’s science research professionals and alumni cultivate and leverage their cross-disciplinary skill sets and expertise to transition into diverse careers.
- 48. Current Exchange is CSHL’s very own student-run magazine. We feature articles about science aimed at a general audience. Check out our inaugural issue at issuu.com/ currentexchange Send your articles to raboukha@cshl.edu by November 5, 2013