gbs2015

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1. Annotation-agnostic differential expression analysis Leonardo Collado-Torres @fellgernon

2. motivating problem: identify and validate regions of the genome that

   change expression during brain development

3. RNA-seq reads Genome (DNA) RNA transcripts (many possible variants) Measuring

   gene expression: RNA-seq Adapted from @jtleek

4. Challenges in counting h"p://www-huber.embl.de/users/anders/HTSeq/doc/count.html

5. Annotation variation Frazee et al, Biostatistics, 2014

6. DER finder approach •  Find contiguous base pairs with Differential

   Expression signal à DE Regions or DERs •  Find nearest annotated feature

7. coverage vector 2 6 0 11 6 Genome (DNA) Read

   coverage Adapted from @jtleek

8. Jaffe et al, Nat. Neuroscience, 2015

9. Single-base F-statistics •  Null model •  Alternative Model •  F-statistic

   i: base-pair j: sample Collado-Torres et al, bioRxiv, 2015

10. Single-base F-statistics Collado-Torres et al, bioRxiv, 2015 BrainSpan data

11. Compare DERs vs annotation Collado-Torres et al, bioRxiv, 2015 BrainSpan

    data

12. Input data n samples → ~348 million nt 11.24% coverage

    Rows with at least 1 sample with coverage > 5 Adapted from @jtleek

13. Finding DERs by expressed-regions

14. Simulation similar in power, yet allows new discoveries

15. Identifying brain development DERs Fetal Infant Child Teen Adult 50+

    6 / group, N = 36 Discovery data Null: Alt: Models Cutoff Details •  Rank DERs by area •  1000 permutations •  Control FWER (≤ 5%) by max area per permutation Results 63,135 DERs 20.509 Corresponds to p-value 10-08 Jaffe et al, Nat. Neuroscience, 2015

16. Replicating DERs Fetal Infant Child Teen Adult 50+ 6 /

    group, N = 36 Replication data Null: Alt: Models Cutoff Details Per sample and per DER calculate average expression Results 50,650 DERs replicated Single F-statistic per DER p-value < 0.05 Jaffe et al, Nat. Neuroscience, 2015

17. Jaffe et al, Nat. Neuroscience, 2015

18. Widespread differential expression of novel transcriptional activity Jaffe et al,

    Nat. Neuroscience, 2015

19. DERs validate: Cytosolic vs total mRNA fractions Jaffe et al,

    Nat. Neuroscience, 2015

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

    DFC: 34 Total N samples: 487 BrainSpan data Coverage Data from BrainSpan: h"p://download.alleninsUtute.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

21. Age-associated DERs lack regional specificity in the human brain BrainSpan

    data Jaffe et al, Nat. Neuroscience, 2015

22. ProporUon of Cells Expression changes across development may represent a

    changing neuronal phenotype Jaffe et al, Nat. Neuroscience, 2015 Estimation method: Houseman et al, BMC Bioinformatics, 2012

23. LIBD Human DLPFC Development •  UCSC “Track Hub” Jaffe et

    al, Nat. Neuroscience, 2015

24. • Data: 3 tissues, 12 samples each • Align with • Identify expressed

    regions with derfinder – Adjust coverage (40 mi) – Find expressed regions (cutoff 5) – Discard ERs < 9 bp GTEX: expressed regions

25. •  221246 ERs – 160817 strictly exonic (73%) – 26740 exonic +

    intronic (12%) – 22375 strictly intronic (10%) •  Can strictly intronic ERs differentiate tissues? Presence of intronic ERs

26. PCs differentiate tissues

27. PCs differentiate tissues

28. Differential intronic ERs adjusting for exonic ERs

29. Differential intronic ERs | exonic ERs

30. Differential intronic ERs | exonic ERs

31. Collado-Torres et al, F1000Research, 2015 regionReport

32. motivating problem: identify and validate regions of the genome that

    change expression during brain development 1. derfinder permits discovery of novel expressed regions 2. we identified & validated gene expression changes in the developing brain 3. we have developed tools for reproducible/shareable reporting

33. Acknowledgements Hopkins Jeffrey Leek Alyssa Frazee Abhinav Nellore 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

34. References + software + code •  Collado-Torres L, et al.

    bioRxiv (2015) doi:10.1101/015370 –  http://bioconductor.org/packages/derfinder •  Collado-Torres L, et al. F1000Research (2015) doi:10.12688/f1000research.6379.1 -  http://www.bioconductor.org/packages/regionReport -  http://lcolladotor.github.io/regionReportSupp/ •  Nellore, et al. bioRxiv (2015) doi:10.1101/019067 - rail.bio •  Jaffe AE, et al. Nat. Neurosci. (2015) doi:10.1038/nn.3898 –  https://github.com/lcolladotor/libd_n36 –  https://github.com/lcolladotor/enrichedRanges •  Frazee AC, et al. Biostatistics. (2014) doi:10.1093/biostatistics/kxt053 –  https://github.com/leekgroup/derfinder