gbs2015

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