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

motivating problem: identify and validate regions of the genome that change expression during brain development

RNA-seq reads Genome (DNA) RNA transcripts (many possible variants) Measuring gene expression: RNA-seq Adapted from @jtleek

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

Annotation variation Frazee et al, Biostatistics, 2014

DER finder approach •  Find contiguous base pairs with Differential Expression signal à DE Regions or DERs •  Find nearest annotated feature

coverage vector 2 6 0 11 6 Genome (DNA) Read coverage Adapted from @jtleek

Jaffe et al, Nat. Neuroscience, 2015

Single-base F-statistics •  Null model •  Alternative Model •  F-statistic i: base-pair j: sample Collado-Torres et al, bioRxiv, 2015

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

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

Input data n samples → ~348 million nt 11.24% coverage Rows with at least 1 sample with coverage > 5 Adapted from @jtleek

Finding DERs by expressed-regions

Simulation similar in power, yet allows new discoveries

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

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

Jaffe et al, Nat. Neuroscience, 2015

Widespread differential expression of novel transcriptional activity Jaffe et al, Nat. Neuroscience, 2015

DERs validate: Cytosolic vs total mRNA fractions Jaffe et al, Nat. Neuroscience, 2015

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

Age-associated DERs lack regional specificity in the human brain BrainSpan data Jaffe et al, Nat. Neuroscience, 2015

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

LIBD Human DLPFC Development •  UCSC “Track Hub” Jaffe et al, Nat. Neuroscience, 2015

• 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

•  221246 ERs – 160817 strictly exonic (73%) – 26740 exonic + intronic (12%) – 22375 strictly intronic (10%) •  Can strictly intronic ERs differentiate tissues? Presence of intronic ERs

PCs differentiate tissues

PCs differentiate tissues

Differential intronic ERs adjusting for exonic ERs

Differential intronic ERs | exonic ERs

Differential intronic ERs | exonic ERs

Collado-Torres et al, F1000Research, 2015 regionReport

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

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

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