CDSBMexico - Speaker Deck

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TIB.2018(R para todos) & Latin American R/Bioconductor Developers Workshop From learning to using to teaching to developing R Leonardo Collado Torres @fellgernon #rstats #teaching #CDSBMexico https://speakerdeck.com/lcolladotor/CDSBMexico

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@lcgunam

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@cendrinou https://www.stat.berkeley.edu/users/sandrine/ Sandrine Dudoit Fall 2007

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Who knows about ? Sandrine Dudoit: She’s one of the @Bioconductor project founders! @cendrinou

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http://www.wholebiome.com/team.html#james-bullard James Bullard January 2008 1 week intense course

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@AlexielMedyna http://liigh.unam.mx/profile/dra-alejandra-medina-rivera/ Alejandra Medina Rivera BioC2008 Developer’s day + 2 conference days Supported by @lcgunam

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@Bioconductor https://bioconductor.org/help/course-materials/2008/BioC2008/

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#lattice Deepayan Sarkar https://github.com/deepayan

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#ShortRead @mt_morgan @Bioconductor http://bioconductor.org/packages/ShortRead

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@fellgernon & Osam http://lcolladotor.github.io/courses/Courses/R/ Fall 2008

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@areyesq http://alejandroreyes.org/ Alejandro Reyes (first BioC: 2009) BioC2009 + BioC2010 + BioC2011 Developer’s day + 2 conference days + Europe Bioc 2010 http://www- huber.embl.de/biocdeveleurope2010/ With support from: @Bioconductor, @lcgunam, @WINTERGENOMICS

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@fellgernon #rstats #teaching #educollab http://lcolladotor.github.io/ 2008-2011

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@fellgernon #rstats #teaching #educollab http://lcolladotor.github.io/courses/Courses/B/ (has videos of me teaching :P, it was a pilot for OpenCourseware) TAs: Alejandro Reyes @areyesq José Víctor Moreno Mayar https://geogenetics.ku.dk/staff/?pure=en/persons/475726 José Reyes http://sysbiophd.harvard.edu/people/student-profiles/jose-reyes

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Always ask for support! • Support for traveling or registration or lodging • Support for teaching: Robert Gentleman gave me free copies of books he had in his office (authors normally get several free copies of books) • Support for community building: almost had Bioconductor’s support in 2010ish for 1 visit, we didn’t give up! #CDSBMexico • Feel free to ask for help! We all started somewhere!! Check your spam box and filters: • Almost lost a scholarship for user!2013 that way :P Check the dates for applying for support! Ask for emails and keep in touch • I asked for PhD application and career advice to Davis McCarthy @davisjmcc in 2010 • That’s how I got into my PhD Socialize! Take advantage of opportunities offered to you!

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BioC2010 First time presenting a poster about an R package (BacterialTranscription): Transcription initiation mapping and transcription unit identification in E. coli Rafael Irizarry https://rafalab.github.io/ @rafalab Ingo Ruczinski http://www.biostat.jhsph.edu/~iruczins/ Them: Have you heard about Johns Hopkins? Me: Johns???? No idea Them: come join us at @jhubiostat !!

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11 Reproducible RNA-seq analysis with Leonardo Collado-Torres @fellgernon #CDSBMexico and

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Reference genome Reads

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GTEx TCGA slide adapted from Shannon Ellis @Shannon_E_Ellis

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SRA

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Slide adapted from Ben Langmead @BenLangmead

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http://rail.bio/ Slide adapted from Ben Langmead @AbhiNellore @BenLangmead

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

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

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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

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Uses the #SummarizedExperiment @Bioconductor package

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> 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/

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slide adapted from Jeff Leek @jtleek

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Collado-Torres et al, NAR, 2017

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Fetal Infant Child Teen Adult 50+ 6 / group, N = 36 Discovery data Jaffe et al, Nat. Neuroscience, 2015 Postmortem Human Brain Samples Fetal Infant Child Teen Adult 50+ 6 / group, N = 36 Replication data @andrewejaffe

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Jaffe et al, Nat. Neuroscience, 2015 @andrewejaffe

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BrainSpan data Jaffe et al, Nat. Neuroscience, 2015 Method implemented in the #derfinder @Bioconductor package

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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 @Shannon_E_Ellis

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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 @Shannon_E_Ellis

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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 @Shannon_E_Ellis

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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$S ex “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 @Shannon_E_Ellis

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SRA phenotype information is far from complete SubjectID Sex Tissue Race Age 662 0 NA female liver NA NA 662 1 NA female liver NA NA 662 2 NA female liver NA NA 662 3 NA female liver NA NA 662 4 NA female liver NA NA 662 5 NA male liver NA NA 662 6 NA male liver NA NA 662 7 NA male liver NA NA 662 8 NA male liver NA NA z z z z slide adapted from Shannon Ellis @Shannon_E_Ellis

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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 TCGA The Cancer Genome Atlas N=11,284 GTEx Genotype Tissue Expression Project N=9,662 slide adapted from Shannon Ellis @Shannon_E_Ellis

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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 test accurac y of predicto r test set TCGA The Cancer Genome Atlas N=11,284 slide adapted from Shannon Ellis @Shannon_E_Ellis

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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 test accurac y of predicto r predict phenotypes across samples in TCGA test set TCGA The Cancer Genome Atlas N=11,284 slide adapted from Shannon Ellis @Shannon_E_Ellis

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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 accurac y of predicto r predict phenotypes across samples in TCGA test set TCGA The Cancer Genome Atlas N=11,284 slide adapted from Shannon Ellis @Shannon_E_Ellis

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select_regions() Output: Coverage matrix (data.frame) Region information (GRanges) slide adapted from Shannon Ellis @Shannon_E_Ellis

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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 @Shannon_E_Ellis

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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 @Shannon_E_Ellis

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http://www.rna-seqblog.com/ Can we use expression data to predict tissue? slide adapted from Shannon Ellis @Shannon_E_Ellis

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Number of Regions 589 589 589 589 Number of Samples (N) 4,769 4,769 7,193 8,951 97.3 % 96.5 % 71.9 % 50.6 % Tissue prediction is accurate across data sets slide adapted from Shannon Ellis @Shannon_E_Ellis

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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 @Shannon_E_Ellis

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

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slide adapted from Kai Kammers Can combine with genotype data to identify eQTLs @KaiKammers

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biorxiv.org/content/early/2018/01/12/247346 @JFuBiostats @biorxivpreprint

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Sex Female Male Age/Development Fetus Child Adolescent Adult Race/Ethnicity Asian Black Hispanic White Tissue Site 1 Cerebral cortex Hippocampus Brainstem Cerebellum Tissue Site 2 Frontal lobe Temporal lobe Midbrain Basal ganglia Tissue Site 3 Dorsolateral prefrontal cortex Superior temporal gyrus Substantia nigra Caudate Hemisphere Left Right Brodmann Area 1-52 Disease Status Disease Neurological control Disease Brain tumor Alzheimer’s disease Parkinson’s disease Bipolar disorder Tumor Type Glioblastoma Astrocytoma Oligodendroglioma Ependymoma Clinical Stage 1 Grade I Grade II Grade III Grade IV Clinical Stage 2 Primary Secondary Recurrent Viability Postmortem Biopsy Preparation Frozen Thawed

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Ashkaun Razmara, in prep. @ashkaun_razmara

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Code Example: research.libd.org/recount-brain/example_PMI/example_PMI.html research.libd.org/recount-brain/example_PMI/example_PMI.Rmd Replicates part of the GTEx PMI paper by Ferreira et al. doi.org/10.1038/s41467-017-02772-x Ashkaun Razmara, in prep. http://research.libd.org/recount-brain/ @ashkaun_razmara

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The recount2 team Hopkins Kai Kammers Shannon Ellis Margaret Taub Kasper Hansen Jeff Leek Ben Langmead OHSU Abhinav Nellore LIBD Leonardo Collado-Torres Andrew Jaffe recount-brain Ashkaun Razmara Funding and hosting NIH R01 GM105705 NIH 1R21MH109956 CONACyT 351535 AWS in Education Seven Bridges IDIES SciServer

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There are many communities you can join! Ask for help / support / #rstats love ^_^

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#Rladies @RLadiesGlobal

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Check #runconf18 @rOpenSci

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This is where it starts for you and us: #CDSBMexico @CDSBMexico It’s your home now! Help us build it and maintain it! Submit your blog posts too!

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expression data for ~70,000 human samples (Multiple) Postdoc positions available to - develop methods to process and analyze data from recount2 - use recount2 to address specific biological questions This project involves the Hansen, Leek, Langmead and Battle labs at JHU Contact: Kasper D. Hansen (khansen@jhsph.edu | www.hansenlab.org) @KasperDHansen @jtleek @BenLangmead @alexisjbattle