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Not Just a Black Box: Interpretable Deep Learning for Genomics AvanA Shrikumar 1 , Peyton Greenside 2 , Anshul Kundaje 1,3 1 Stanford Computer Science, 2 Stanford Dept. of Biomedical InformaAcs, 3 Stanford GeneAcs • Novel algorithm (DeepLIFT) for explaining predicAons of a given deep learning model for parAcular input examples • Novel algorithm (MoDISco) for extracAng recurring pamerns (moAf discovery) using a deep learning model Our contribuCons Method: DeepLIFT (Deep Learning Important Features) 1. Alipanahi, B., Delong, A., Weirauch, M., & Frey, B. (2015). PredicAng the sequence specificiAes of DNA- and RNA-binding proteins by deep learning. Nat Biotechnol 2. Zhou J, Troyanskaya O. PredicAng effects of noncoding variants with deep learning–based sequence model. Nature Methods. 2015 3. Kelley, D., Snoek, J., & Rinn, J. (2015). Basset: Learning the regulatory code of the accessible genome with deep convoluAonal neural networks. doi:10.1101/028399 4. Heinz S, e. (2016). Simple combinaAons of lineage-determining transcripAon factors prime cis-regulatory elements required for macrophage and B cell idenAAes. 5. Lim LS, e. (2016). The pluripotency regulator Zic3 is a direct acAvator of the Nanog promoter in ESCs. 6. Gagliardi A, e. (2016). A direct physical interacAon between Nanog and Sox2 regulates embryonic stem cell self-renewal. - PubMed - NCBI . Ncbi.nlm.nih.gov. Retrieved 30 January 2016, from hmps://www.ncbi.nlm.nih.gov/ pubmed/23892456 7. Kheradpour, P., & Kellis, M. (2014). SystemaAc discovery and characterizaAon of regulatory moAfs in ENCODE TF binding experiments. Nucleic acids research Results of logisCc regression model trained to predict Nanog binding using the top 3 moCf hits, per moCf, per region Visualizing individual paQern- detectors: DeepBind (Alipanahi et al.) Superior moCf discovery for Nanog PosiCve set: 5,473 reproducible Nanog peaks in H1-ESC from ENCODE NegaCve set: 258,987 H1ESC DNase-seq peaks Method: MoDISco (MoCf Discovery from Importance Scores) i 1 = 0 i 2 = 0 h 1 = max(0, i 1 + 2i 2 + 1) = 1 h 2 = max(0, i 1 + 2i 2 - 1) = 0 y=h 1 +h 2 = 1 i 1 = -1 i 2 = -1 h 1 = max(0, i 1 + 2i 2 + 1) = 0 h 2 = max(0, i 1 + 2i 2 - 1) = 0 y=h 1 +h 2 = 0 Compute behaviour under “reference” Use difference from reference to find importance scores Gradients assign importance of 0 to both inputs in laQer case, as gradient of h 1 and h 2 are 0. Using difference-from- reference, we see h 1 is -1 below its reference value; DeepLIFT assigns an importance of -1/3 to i 1 and -2/3 to i 2 Gata (Rev. Comp.) Gata SPI1 Gata (Rev. Comp.) B-cells Gata1 ChIP-seq peak SPI1 ChIP-seq peak No SPI1 peak No Gata1 ChIP-seq peak Erythroid Reveal context-specific use of regulatory sequence Results (DeepLIFT) Model architecture overview C G A T A A C C G A T A T Learned paQern detectors Input: DNA sequence represented as ones and zeros Later layers build on paQerns of previous layer Accessible in Erythroid Accessible in B-cells Output: Accessible (+1) vs not accessible (0) “Fully connected” layers incorporate all info together A C G T 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 Computer vision All 5 ENCODE Nanog moCfs Canonical HOMER matches to 4 MoDISCo moCfs All 32 de-novo HOMER moCfs Top 4 de-novo HOMER moCfs All 4 MoDISco moCfs LogisCc Regression auROC 0.0 1.0 (a) Obtain per-base importance scores using DeepLIFT (b) Segment to “seqlets” of high importance (c) “Autocomplete” seqlets using DeepLIFT informaCon (d) Compute distances between pairs of seqlets via cross-correlaCon (e) Cluster seqlets using pairwise distances (f) Aggregate clusters • Regulatory sequence involves complex hierarchical pamerns that are difficult for exisAng computaAonal methods to model • Deep Learning techniques show great promise in this area [1-3] but are considered uninterpretable “Black Boxes”, limiAng their usefulness for making biological discoveries MoCvaCon • Goal: learn key regulatory sequences governing hematopoesis • Approach: (1) Experimentally idenAfy biochemically acAve regions in different cell-lines during the hematopoiesis lineage (2) Train deep learning model to predict acAvity from seq. (3) Interpret the model to learn key regulatory sequences Example problem Peyton Greenside 4/5 ENCODE Nanog moCfs Corresponding MoDISco moCf Zic3 Sox2 Oct4-Sox2-Nanog Nanog Results (MoDISco) 4 MoCf clusters idenCfied by MoDISco: ++ and -- orientaAon +- and -+ orientaAon Zic3 and Nanog separaAon: Oct-Sox-Nanog and Nanog separaAon: Shuffled Zic3 and Nanog separaAon: Co-binding between Zic3 and Nanog? Fusion moCf from subclustering: Individual examples: Protein-protein interacCon: original scores: 8 scores: 3 masked, 8->3 scores: 6 masked, 8->6 |grad| (simonyan) Guided Backprop gradient* input integrated grads-10 DeepLIFT- RevealCancel Proof-of-concept: morphing an “8” to a 3 or a 6 Deep learning model is trained to recognize handwriQen digits from the MNIST database. Pixels are ranked by difference of importance for original class (eg: 8) and target class (eg: 3 or 6) by different methods. Up to 20% of pixels more important to original class than target class erased. i 1 i 2 y i 1 i 2 <i 1 i 1 – (i 1 -i 2 )=i 2 i 1 i 2 >i 1 i 1 –0=i 1 i 1 i 2 y=i 1 –h 2 h 1 =i 1 -i 2 1 -1 1 -1 y = min(i 1 ,i 2 ) à gradient 0 for either i 1 or i 2 h 2 = max(0, h 1 ) y=i 1 – max(0, i 1 –i 2 ) = min(i 1 ,i 2 ) -6 y=i 1 - max(0, i 1 –i 2 ) = 10 – max(0, 4) = 6 Standard breakdown: 4= (10 from i 1 ) + (-6 from i 2 ) max(0, i 1 -i 2 ) i 1 -i 2 i 1 =10 i 2 =6 +10 Other possible breakdown: 4= (4 from i 1 ) + (0 from i 2 ) max(0, i 1 -i 2 ) i 1 -i 2 i 1 =10 i 2 =6 4 0 Standard breakdown: y = (10 from i 1 ) –[(10 from i 1 ) – (6 from i 2 )]= 6 from i 2 Average over both orders: y = (10 from i 1 ) –[(7 from i 1 ) + (-3 from i 2 )] = (3 from i 1 ) + (3 from i 2 ) Average: 4= (7 from i 1 ) + (-3 from i 2 ) i 1 -i 2 Consider i 1 = 10, i 2 =6 By considering different orders for posiCve and negaCve terms, can also improve assignment of importance scores: “AND”/min operaCon:

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Not Just a Black Box: Interpretable Deep Learning for Genomics AvanAShrikumar1,PeytonGreenside2,AnshulKundaje1,3

1StanfordComputerScience,2StanfordDept.ofBiomedicalInformaAcs,3StanfordGeneAcs

•  Novelalgorithm(DeepLIFT)forexplainingpredicAonsofagivendeeplearningmodelforparAcularinputexamples

•  Novelalgorithm(MoDISco)forextracAngrecurringpamerns(moAfdiscovery)usingadeeplearningmodel

OurcontribuCons

Method:DeepLIFT(DeepLearningImportantFeatures)

1.  Alipanahi,B.,Delong,A.,Weirauch,M.,&Frey,B.(2015).PredicAngthesequencespecificiAesofDNA-andRNA-bindingproteinsbydeeplearning.NatBiotechnol2.  ZhouJ,TroyanskayaO.PredicAngeffectsofnoncodingvariantswithdeeplearning–basedsequencemodel.NatureMethods.20153.  Kelley,D.,Snoek,J.,&Rinn,J.(2015).Basset:LearningtheregulatorycodeoftheaccessiblegenomewithdeepconvoluAonalneuralnetworks.doi:10.1101/0283994.  HeinzS,e.(2016).SimplecombinaAonsoflineage-determiningtranscripAonfactorsprimecis-regulatoryelementsrequiredformacrophageandBcellidenAAes.

5.LimLS,e.(2016).ThepluripotencyregulatorZic3isadirectacAvatoroftheNanogpromoterinESCs.6.GagliardiA,e.(2016).AdirectphysicalinteracAonbetweenNanogandSox2regulatesembryonicstemcellself-renewal.-PubMed-NCBI.Ncbi.nlm.nih.gov.Retrieved30January2016,fromhmps://www.ncbi.nlm.nih.gov/pubmed/238924567.Kheradpour,P.,&Kellis,M.(2014).SystemaAcdiscoveryandcharacterizaAonofregulatorymoAfsinENCODETFbindingexperiments.Nucleicacidsresearch

ResultsoflogisCcregressionmodeltrainedtopredictNanogbindingusingthetop3moCfhits,permoCf,perregion

VisualizingindividualpaQern-detectors:DeepBind(Alipanahietal.)

SuperiormoCfdiscoveryforNanogPosiCveset:5,473reproducibleNanogpeaksinH1-ESCfromENCODENegaCveset:258,987H1ESCDNase-seqpeaks

Method:MoDISco(MoCfDiscoveryfromImportanceScores)

i1=0 i2=0

h1=max(0,i1+2i2+1)=1

h2=max(0,i1+2i2-1)=0

y=h1+h2=1

i1=-1 i2=-1

h1=max(0,i1+2i2+1)=0

h2=max(0,i1+2i2-1)=0

y=h1+h2=0

Computebehaviourunder“reference” Usedifferencefromreferencetofindimportancescores

Gradientsassignimportanceof0tobothinputsinlaQercase,asgradientofh1andh2are0.Usingdifference-from-reference,weseeh1is-1belowitsreferencevalue;DeepLIFTassignsanimportanceof-1/3toi1and-2/3toi2

Gata(Rev.Comp.)Gata SPI1Gata(Rev.Comp.)

B-cells

Gata1ChIP-seqpeak SPI1ChIP-seqpeak

NoSPI1peakNoGata1ChIP-seqpeak

Erythroid

Revealcontext-specificuseofregulatorysequence

Results(DeepLIFT)

Modelarchitectureoverview

C G A T A A C C G A T A T

LearnedpaQerndetectors

Input:DNAsequencerepresentedasonesandzeros

LaterlayersbuildonpaQernsofpreviouslayer

AccessibleinErythroid

AccessibleinB-cells

Output:Accessible(+1)vsnotaccessible(0)

“Fullyconnected”layersincorporateallinfotogether

ACGT

0100

0010

1000

0001

1000

1000

0100

0100

0010

1000

0001

1000

0001

Computervision

All5ENCODENanogmoCfs

CanonicalHOMERmatchesto4

MoDISComoCfs

All32de-novoHOMERmoCfs

Top4de-novoHOMERmoCfs

All4MoDIScomoCfs

LogisCcRe

gression

auR

OC

0.0

1.0

(a)Obtainper-baseimportancescoresusing

DeepLIFT

(b)Segmentto“seqlets”ofhigh

importance

(c)“Autocomplete”seqletsusingDeepLIFT

informaCon

(d)Computedistancesbetweenpairsofseqletsviacross-correlaCon

(e)Clusterseqletsusingpairwisedistances

(f)Aggregateclusters

•  RegulatorysequenceinvolvescomplexhierarchicalpamernsthataredifficultforexisAngcomputaAonalmethodstomodel

•  DeepLearningtechniquesshowgreatpromiseinthisarea[1-3]butareconsidereduninterpretable“BlackBoxes”,limiAngtheirusefulnessformakingbiologicaldiscoveries

MoCvaCon

•  Goal:learnkeyregulatorysequencesgoverninghematopoesis•  Approach:

(1)ExperimentallyidenAfybiochemicallyacAveregionsindifferentcell-linesduringthehematopoiesislineage(2)TraindeeplearningmodeltopredictacAvityfromseq.(3)Interpretthemodeltolearnkeyregulatorysequences

Exampleproblem

PeytonGreenside

4/5ENCODENanogmoCfs

CorrespondingMoDIScomoCf

Zic3

Sox2 Oct4-Sox2-Nanog

Nanog

Results(MoDISco)

4MoCfclustersidenCfiedbyMoDISco:

++and--orientaAon

+-and-+orientaAon

Zic3andNanogseparaAon:

Oct-Sox-NanogandNanogseparaAon:

ShuffledZic3andNanogseparaAon:

Co-bindingbetweenZic3andNanog? FusionmoCffromsubclustering:

Individualexamples:

Protein-proteininteracCon:

original scores:8 scores:3 masked,8->3 scores:6 masked,8->6

|grad|

(sim

onyan)

Guide

dBa

ckprop

grad

ient*

inpu

tintegrated

grad

s-10

De

epLIFT-

RevealCa

ncel

Proof-of-concept:morphingan“8”toa3ora6DeeplearningmodelistrainedtorecognizehandwriQendigitsfromtheMNISTdatabase.Pixelsarerankedbydifferenceofimportancefororiginalclass(eg:8)andtargetclass(eg:3or6)bydifferentmethods.Upto20%ofpixelsmoreimportanttooriginalclassthantargetclasserased.

i1 i2 y

i1 i2<i1 i1–(i1-i2)=i2

i1 i2>i1 i1–0=i1

i1 i2

y=i1–h2

h1=i1-i21

-1

1 -1

y=min(i1,i2)àgradient0foreitheri1ori2

h2=max(0,h1)

y=i1–max(0,i1–i2)=min(i1,i2)

-6

y=i1-max(0,i1–i2)=10–max(0,4)=6

Standardbreakdown:4=(10fromi1)+(-6fromi2)

max(0,i1-i2)

i1-i2i1=10

i2=6

+10

Otherpossiblebreakdown:4=(4fromi1)+(0fromi2)

max(0,i1-i2)

i1-i2

i1=10

i2=6

4

0

Standardbreakdown:y=(10fromi1)–[(10fromi1)–(6fromi2)]=6fromi2Averageoverbothorders:y=(10fromi1)–[(7fromi1)+(-3fromi2)]=(3fromi1)+(3fromi2)

Average:4=(7fromi1)+(-3fromi2)

i1-i2

Consideri1=10,i2=6

ByconsideringdifferentordersforposiCveandnegaCveterms,canalsoimproveassignmentofimportancescores:

“AND”/minoperaCon:

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