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ICSA 2017

ICSA 2017

Reproducible RNA-seq analysis with recount2

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Leonardo Collado-Torres

June 27, 2017
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  1. 11 Reproducible RNA-seq analysis with Leonardo Collado-Torres @fellgernon #ICSA2017

  2. Reference genome Reads

  3. None
  4. GTEx TCGA slide adapted from Shannon Ellis

  5. SRA

  6. Slide adapted from Ben Langmead

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

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

  9. Obstacle: our research moves (spot) markets Spike in market price

    due to preprocessing job flows slide adapted from Jeff Leek
  10. Obstacle: our research moves (spot) markets Weekday market volatility Weekend

    EC2 inactivity slide adapted from Jeff Leek
  11. https://jhubiostatistics.shinyapps.io/recount/

  12. exon 1 exon 2 exon 3

  13. disjoint exon 1 disjoint exon 2 disjoint exon 3

  14. None
  15. None
  16. None
  17. 5 10 15 0 1 2 3 4 5 Genome

    Coverage 3 3 5 4 4 2 2 3 1 3 3 1 4 4 2 1 AUC = area under coverage = 45
  18. None
  19. > 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/
  20. slide adapted from Jeff Leek

  21. >library('recount') > download_study('SRP029880', type='rse-gene') > download_study('SRP059039', type='rse-gene') > load(file.path('SRP029880 ',

    'rse_gene.Rdata')) > load(file.path('SRP059039', 'rse_gene.Rdata')) > mdat <- do.call(cbind, dat) https://github.com/leekgroup/recount-analyses/
  22. Collado Torres et al. Nat. Biotech 2017

  23. None
  24. None
  25. None
  26. 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
  27. Collado-Torres et al, NAR, 2017

  28. 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
  29. Jaffe et al, Nat. Neuroscience, 2015

  30. DERs outside of “known genes” Jaffe et al, Nat. Neuroscience,

    2015
  31. CBC: 28 MD: 24 STR: 28 AMY: 31 HIP: 32

    DFC: 34 Total N samples: 487 BrainSpan data Coverage Data from BrainSpan: http://download.alleninstitute.org/brainspan/MRF_BigWig_Gencode_v10/ VFC: 30 MFC: 32 OFC: 30 M1C: 25 S1C: 26 IPC: 33 A1C: 30 STC: 35 ITC: 33 V1C: 33
  32. BrainSpan data Jaffe et al, Nat. Neuroscience, 2015

  33. Percent Expressed Mean reads across GTEx

  34. > library('recount') > regions_list <- bplapply(chrs, function(chr) { regs <-

    expressed_regions('SRP012682', chr, cutoff = 5L) return(regs) }, BPPARAM = bp) > names(regions_list) <- chrs > regions <- unlist(GRangesList(regions_list)) https://github.com/leekgroup/recount-analyses/
  35. > library('recount') > covMat <- bplapply(chrs, function(chr) { coverageMatrix <-

    coverage_matrix('SRP012682’', chr, regions_list[[chr]]) return(coverageMatrix) }, BPPARAM = bp) > covMat <- do.call(rbind, covMat) https://github.com/leekgroup/recount-analyses/
  36. 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
  37. 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
  38. 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
  39. 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
  40. SRA phenotype information is far from complete SubjectID Sex Tissue

    Race Age 6620 NA female liver NA NA 6621 NA female liver NA NA 6622 NA female liver NA NA 6623 NA female liver NA NA 6624 NA female liver NA NA 6625 NA male liver NA NA 6626 NA male liver NA NA 6627 NA male liver NA NA 6628 NA male liver NA NA 6629 NA male liver NA NA 6630 NA male liver NA NA 6631 NA NA blood NA NA 6632 NA NA blood NA NA 6633 NA NA blood NA NA 6634 NA NA blood NA NA 6635 NA NA blood NA NA 6636 NA NA blood NA NA z z z z slide adapted from Shannon Ellis
  41. 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
  42. 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 accuracy of predictor test set TCGA The Cancer Genome Atlas N=11,284 slide adapted from Shannon Ellis
  43. 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 accuracy of predictor predict phenotypes across samples in TCGA test set TCGA The Cancer Genome Atlas N=11,284 slide adapted from Shannon Ellis
  44. 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
  45. select_regions() Output: Coverage matrix (data.frame) Region information (GRanges) slide adapted

    from Shannon Ellis
  46. 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
  47. 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
  48. http://www.rna-seqblog.com/ Can we use expression data to predict tissue? slide

    adapted from Shannon Ellis
  49. 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
  50. 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
  51. > 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/
  52. 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
  53. None
  54. bioconductor.org/packages/derfinder bioconductor.org/packages/recount > biocLite(“derfinder”) > biocLite(“recount”) http://rail.bio $ ./install-rail-rna-V

  55. https://github.com/leekgroup/recount-contributions

  56. 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