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Annotation-agnostic differential expression analysis

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Annotation-agnostic differential expression analysis

  1. 1. Annotation-agnostic differential expression analysis Leonardo Collado-Torres @fellgernon #ENAR2016 www.slideshare.net/lcolladotor
  2. 2. Mo#va#ng problem: iden#fy and validate regions of the genome that change expression when analyzing #ssues with poten#ally incomplete transcriptome annota#on
  3. 3. RNA-seq reads Genome (DNA) RNA transcripts (many possible variants) Measuring gene expression: RNA-seq Adapted from @jtleek
  4. 4. Genome (DNA) Mapped reads Adapted from @jtleek Common analysis pipelines: • Feature coun#ng (gene or exon level) • Transcript assembly
  5. 5. Challenges in counting hBp://www-huber.embl.de/users/anders/HTSeq/doc/count.html
  6. 6. Annotation variation Frazee et al, Biostatistics, 2014
  7. 7. DER finder approach •  Find con#guous base pairs with Differen#al Expression signal à DE Regions or DERs •  Find nearest annotated feature Frazee et al, Biostatistics, 2014
  8. 8. coverage vector 2 6 0 11 6 Genome (DNA) Read coverage Adapted from @jtleek
  9. 9. Jaffe et al, Nat. Neuroscience, 2015
  10. 10. Finding DERs by expressed-regions
  11. 11. CBC: 28 MD: 24 STR: 28 AMY: 31 HIP: 32 DFC: 34 Total N samples: 487 BrainSpan data Coverage Data from BrainSpan: hBp://download.allenins#tute.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
  12. 12. • Data: 3 #ssues (liver, tes#s, heart), 8 samples each • Align with • Iden#fy expressed regions with derfinder – Adjust coverage (40 million) – Find expressed regions (cutoff 5) – Discard ERs < 9 bp GTEx: DERs via expressed-regions
  13. 13. Presence of intronic ERs Can strictly intronic ERs differen#ate #ssues?
  14. 14. PCs differentiate tissues
  15. 15. Differential intronic ERs | exonic ERs
  16. 16. Differential intronic ERs | exonic ERs
  17. 17. Differential intronic ERs | exonic ERs
  18. 18. Simulation setup 3 replicates: 2 groups, each with 5 samples ~2 million paired-end reads for chr17 1/6 high, 1/6 low in group 2 vs group 1 Annota#on: complete missing 20% of transcripts (8.28% exons) Reference set: 3868 exons that overlap only 1 transcript
  19. 19. Simulation results •  Similar power to methods that have complete annota#on •  Methods with incorrect annota#on lose a lot of power •  Higher empirical FDR/FPR
  20. 20. Collado-Torres et al, F1000Research, 2015 regionReport
  21. 21. Mo#va#ng problem: iden#fy and validate regions of the genome that change expression when analyzing #ssues with poten#ally incomplete transcriptome annota#on derfinder permits discovery of novel expressed regions 1. we iden#fied expressed intronic regions that differen#ate #ssues independently of the nearest exonic region 2. we have developed tools for reproducible/ shareable repor#ng
  22. 22. Acknowledgements Hopkins Jeffrey Leek Alyssa Frazee Abhinav Nellore Chris Wilks Ben Langmead LIBD Andrew Jaffe Jooheon Shin Nikolay Ivanov Amy Deep Ran Tao Yankai Jia Thomas Hyde Joel Kleinman Daniel Weinberger Harvard Rafael Irizarry Michael Love Funding NIH LIBD CONACyT México
  23. 23. References + software + code •  Collado-Torres, et al. bioRxiv (2015) doi:10.1101/015370 –  http://bioconductor.org/packages/derfinder –  http://lcolladotor.github.io/derSupplement/ •  Collado-Torres, et al. F1000Research (2015) doi:10.12688/f1000research.6379.1 -  http://www.bioconductor.org/packages/regionReport -  http://lcolladotor.github.io/regionReportSupp/ •  Nellore, Collado-Torres, et al. bioRxiv (2015) doi:10.1101/019067 - rail.bio • Nellore, …, Collado-Torres, et al. bioRxiv (2016) doi:10.1101/038224 - intropolis.rail.bio •  Jaffe, Shin, Collado-Torres, et al. Nat. Neurosci. (2015) doi:10.1038/nn.3898 –  https://github.com/lcolladotor/libd_n36 –  https://github.com/lcolladotor/enrichedRanges •  Frazee, et al. Biostatistics. (2014) doi:10.1093/biostatistics/kxt053 –  https://github.com/leekgroup/derfinder

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