CHiCAGO: Statistical methodology for signal detection in ......CHiCAGO: Statistical methodology for...

CHiCAGO: Statistical methodology for signal detectionin Capture Hi-C data

Jonathan Cairns

[email protected]

@jonathancairns

Fraser/Spivakov labs, Babraham Insitute

4th October 2016

Table of Contents

1 Introduction

2 The CHiCAGO model

3 Results

J. Cairns (Babraham Institute) regulatorygenomicsgroup.org/chicago 2 / 20

Table of Contents

1 Introduction

2 The CHiCAGO model

3 Results


Motivation


CHi-C: improved resolution at promoters, over Hi-C


Lieberman-Aiden et al (2009)

CHi-C: improved resolution at promoters, over Hi-C

Approx. 12-fold increase in read coverage


Schonfelder et al (2015), Mifsud et al (2015), Sahlen et al (2015)

The data

Align reads & filter out artefacts with HiCUP

Obtain counts Xij :

1 3 7 5 4 0 0 0 0

1 0 2 0 4 6 5 4 0

0 0 1 2 0 4 6 9 10 ...

0 0 0 1 1 2 5 3 4

0 0 0 0 0 1 1 5 7

... ...

22,000

823,000

baits (j)

other ends (i)


Wingett et al (2016)

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MIR625−201 (224546)

Distance from viewpoint

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PPP1CB−004,PPP1CB−006,PPP1CB−005,PPP1CB−003,PPP1CB−001,PPP1CB−009,... (340147)


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interaction

MIR625

PPP1CB

Table of Contents

1 Introduction

2 The CHiCAGO model

3 Results


CHiCAGO

CHiCAGO – Capture Hi-C Analysis of Genomic Organization.


Model

Background comes from two sources:

Brownian Technical

Source Random collisions Sequencing artefacts

Depends ondistance?

Yes (decreasing) No

Dominates Close to bait Far from bait

Under H0 (no interaction), counts are sum of the two components:

Xij = Bij + Tij


Brownian background estimation

Xij = Bij + Tij

Bij ∼ NB, with E(Bij) = f (dij) × (bait bias)j × (other end bias)i



Xij = Bij + Tij

Bij ∼ NB, with E(Bij) = f (dij)

× (bait bias)j × (other end bias)i

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2501177

53485373

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−500000 −250000 0 250000 500000Distance from bait

Exp

ecte

d co

unt



Xij = Bij + Tij

Bij ∼ NB, with E(Bij) = f (dij) × (bait bias)j

× (other end bias)i

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ecte

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ecte

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Xij = Bij + Tij


estimated close to bait(< 1.5Mb) in 20kb bins.

bin-wise estimates f (db)from geometric meanacross baits

interpolation: cubic fiton log-log scale

Distance function

10 11 12 13 14

−0.5

0.0

0.5

1.0

1.5

2.0

2.5

log(distance)

log(

f(d)

)



Xij = Bij + Tij


f (d):

estimated close to bait(< 1.5Mb) in 20kb bins.

bin-wise estimates f (db)from geometric meanacross baits

interpolation: cubic fiton log-log scale

Distance function

10 11 12 13 14

−0.5

0.0

0.5

1.0

1.5

2.0

2.5

log(distance)

log(

f(d)

)



Xij = Bij + Tij


Bait-specific bias:

Get bin-wise estimates for each bait.Take median across bins – robust to interactions

Other-end-specific bias:

Too sparse to estimate individuallyAssume trans-chromosomal reads are mostly noisePool other-ends by trans countsEstimate bias parameter, pool-wiseBait-to-bait interactions treated separately

Dispersion parameter

Established maximum likelihood methods.


Technical noise estimation

Xij = Bij + Tij


Technical noise estimation

Xij = Bij + Tij

Tij ∼ Pois(λij)

Estimated entirely from trans-chromosomal reads

Pool baits and other-ends

Pool-wise estimate: average number of reads per pair of transfragments.


Calling p-values

Xij = Bij + Tij

B is Negative Binomial, T is Poisson.

⇒ X has Delaporte distribution.

One-sided hypothesis test – Observed more than expected by chance?

Get p-value


Statistical model - p-value weighting

Simple p-value thresholding (even using Bonferroni/FDR)→ many false positives (typically, at large distances, with only oneread).




At large distances:

far fewer reproducibleinteractions

Empirical probability of reproducible interaction

log(distance)

10 11 12 13 14 15 16

−15

−10

−5

DataFit

log[empiricalprobability]




At large distances:


but vast majority of testsperformed there


log(distance)

10 11 12 13 14 15 16

−15

−10

−5

DataFit





At large distances:



So, large-distance falsepositives dominate.


log(distance)

10 11 12 13 14 15 16

−15

−10

−5

DataFit





At large distances:



So, large-distance falsepositives dominate.


log(distance)

10 11 12 13 14 15 16

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−10

−5

DataFit


Solution: p-value weighting (Genovese et al, 2009) to downweightlong-distance interactions


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MIR625−201 (224546)


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interaction

MIR625

PPP1CB

Table of Contents

1 Introduction

2 The CHiCAGO model

3 Results


Downstream analysis

CHiCAGO-derived interactions give us “Promoter-InteractingRegions” (PIRs).

Histone marks?SNPs?Other features?


Histone marks – significant enrichment at other ends0

2000

4000

6000

8000

1000

012

000

CT

CF

H3K

4me1

H3K

4me3

H3K

27ac

H3K

27me3

H3K

9me3

GM12878

020

0040

0060

0080

0010

000

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0

CT

CF

H3K

4me1

H3K

4me3

H3K

27ac

H3K

27me3

H3K

9me3

mESC

Significant interactions

Random samples

Significant interactions

Random samples

Num

ber

of o

verla

ps w

ith fe

atur

e

Num

ber

of o

verla

ps w

ith fe

atur

e


Paula Freire Pritchett

Interactions in blood cells

Javierre* / Burren* / Wilder* / Kreuzhuber* / Hill* et al. (in press)Genomic regulatory architecture links disease variants to target genes.

PCHi-C in 17 blood cell types (primary cells)

“Interactomes” found to be cell type-specific, matching lineage tree

Disease-associated SNP


Conclusions

CHiCAGO finds interactions in Capture Hi-C data:

robustlyhaving normalised for various sources of biasusing p-value weighting (to account for variable true positive rate)

Results provide biological understanding:

can detect cell type-specific interactions.can show enrichment for histone marks.can link disease-associated SNPs to their target genes.


Acknowledgements

CHiCAGO developers

Paula FreirePritchett

Steven W. Wingett

Mikhail Spivakov

Statistical Advice

Vincent Plagnol(UCL/Inivata)

Daniel Zerbino(EBI)

Additional DownstreamAnalysis

Csilla Varnai

Andrew Dimond

Data

Biola Javierre

Stefan Schonfelder

Cameron Osborne(KCL)

Peter Fraser

http://www.regulatorygenomicsgroup.org/chicago


http://www.regulatorygenomicsgroup.org/chicago

p-value weighting

We make prior “guesses” Uij . We allow Uij to depend on dij , assumingthat short-range interactions are more likely than long-range interactions,with a smooth transition between the two. The Uij are transformed intoweights Wij by dividing through by the mean value, U, ensuring that theaverage Wij value is 1. Finally, weighted p-values are obtained by dividingthe p-values by their respective weights:

Qij =pijWij

We now specify the Uij model in our particular context. (next slide)


p-value weighting


log(distance)

10 11 12 13 14 15 16

−15

−10

−5

DataFit


Bounded logistic regression model: Uij is assumed a function of both dijand a vector of parameters Θ = (α, β, γ, δ), according to

Uij = ηijUmax + (1− ηij)Umin

where

ηij = expit(α + βlog(dij))

Umin = expit(γ)

Umax = expit(δ)

using the expit function

expit(x) =ex

1 + ex


Numbers of called interactions

# interactions per sample: 130,000− 190,000

# interactions per captured promoter:

05

1015

2025

30

Naive_

B

Total

_B

al_thy

mus

Activate

d

CD4_MF

nActiv

ated

aive_

CD4

aive_

CD8

Total

_CD8

hage

s_M2

hage

s_M0

hage

s_M1

precu

rsors

karyo

cytes

throb

lasts

Monoc

ytes

Neutro

phils

Ave

rag

e o

f in

tera

ctio

ns

per

C

aptu

red

Pro

mo

ter

0 10

20

30

Naïve B Total B Fetal Thymus

Naïve CD4+ Naïve CD8+ Total CD8+ Macrophages M2 Macrophages M0 Macrophages M1 Endothelial Precursors Megakaryocytes Erythroblasts Monocytes Neutrophils

Total CD4+ Unstimulated Total CD4+

Stimulated Total CD4+


2. PCHiC: sequencing, HICUP & CHiCAGO

Cell Type Processed Reads Capture Unique Valid Reads Significant Interactions

Megakaryocytes 2,696,317,863 653,848,788 150,203

Erythroblasts 2,338,677,291 588,786,672 144,771

Neutrophils 2,241,977,639 736,055,569 131,609

Monocytes 1,942,858,536 572,357,387 151,389

Macrophages M0 2,125,716,849 668,675,248 163,791

Macrophages M1 2,067,485,594 497,683,496 163,399

Macrophages M2 2,055,090,022 523,561,551 173,449

Naïve B 2,127,262,739 629,928,642 171,439

Total B 1,874,130,921 702,533,922 183,119

Fetal Thymus 2,728,388,103 776,491,344 145,577

Naïve CD4+ 2,797,861,611 844,697,853 192,048

Total CD4+ 2,227,386,686 836,974,777 166,668

Unstimulated Total CD4+ 2,034,344,692 721,030,702 177,371

Stimulated Total CD4+ 1,971,143,855 749,720,649 188,714

Naïve CD8+ 1,910,881,702 747,834,572 187,399

Total CD8+ 1,849,225,803 628,771,947 183,964

Endothelial Precursors 2,308,749,174 420,536,621 141,382

37,297,499,080 11,299,489,740 2,816,292

Paula Freire-Pritchett Steven Wingett

* HICUP *CHiCAGO

Clustering of BR according the score of 10K random interactions (cis

1Mb)

NeutrophilsTotal B

Naïve B

Total CD8+

NeutrophilsNeutrophils

MonocytesMonocytes

Erythroblasts

ErythroblastsErythroblasts

MegakaryocytesMegakaryocytes

MegakaryocytesMegakaryocytes

Endothelial Precursors

Endothelial PrecursorsEndothelial Precursors

Macrophages M1Macrophages M1

Macrophages M0

Macrophages M1


Fetal ThymusNaïve CD4+Naïve CD4+

Total CD8+Total CD8+

Stimulated Total CD4+Stimulated Total CD4+Stimulated Total CD4+

Naïve CD8+

UnstimulatedTotal CD4+UnstimulatedTotal CD4+UnstimulatedTotal CD4+

Total CD4+

Naïve CD8+Naïve CD8+

Naïve BNaïve B

Total BTotal B

Fetal ThymusFetal Thymus

Naïve CD4+Total CD4+Naïve CD4+

Total CD4+

Monocytes

Macrophages M2


Distance

400 500 600 700

Sven Sewitz

−1e+06 −5e+05 0e+00 5e+05 1e+06

050

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012

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−1e+06 −5e+05 0e+00 5e+05 1e+06

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N

B1_Monocyte_D1_step2_chicago2

Megakaryocyte_D5_6_step2_chicago2 789407 − AP1S2

789407 − AP1S2

410124 − CD93−002,...

−1e+06 −5e+05 0e+00 5e+05 1e+06

050

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B1_Monocyte_D1_step2_chicago2

−1e+06 −5e+05 0e+00 5e+05 1e+06

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410124 − CD93−002,...Megakaryocyte_D5_6_step2_chicago2

CHiCAGO: Statistical methodology for signal detection in ......CHiCAGO: Statistical methodology for...

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