Annotation-agnostic differential expression analysis

Annotation-agnostic differential expression analysis

• 1.
  Annotation-agnostic differential expression analysis LeonardoCollado-Torres @fellgernon #ENAR2016 www.slideshare.net/lcolladotor
• 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.
  RNA-seq reads Genome (DNA) RNA transcripts (many possible variants) Measuringgene expression: RNA-seq Adapted from @jtleek
• 4.
  Genome (DNA) Mapped reads Adapted from@jtleek Common analysis pipelines: • Feature coun#ng (gene or exon level) • Transcript assembly
• 5.
  Challenges in counting hBp://www-huber.embl.de/users/anders/HTSeq/doc/count.html
• 6.
  Annotation variation Frazee etal, Biostatistics, 2014
• 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.
  coverage vector 2 6 011 6 Genome (DNA) Read coverage Adapted from @jtleek
• 9.
  Jaffe et al,Nat. Neuroscience, 2015
• 10.
  Finding DERs byexpressed-regions
• 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
• 14.
  • 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 viaexpressed-regions
• 15.
  Presence of intronicERs Can strictly intronic ERs differen#ate #ssues?
• 16.
  PCs differentiate tissues
• 17.
  Differential intronic ERs| exonic ERs
• 18.
  Differential intronic ERs| exonic ERs
• 19.
  Differential intronic ERs| exonic ERs
• 20.
  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
• 21.
  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
• 22.
  Collado-Torres et al,F1000Research, 2015 regionReport
• 23.
  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
• 24.
  Acknowledgements Hopkins Jeffrey Leek Alyssa Frazee AbhinavNellore 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
• 25.
  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