Developing a Roadmap for Integrating Computational and In ...

Developing a Roadmap for Integrating Computational and In Vitro Approaches in Risk-Based Chemical Safety Decisions

Rusty ThomasDirectorNational Center for Computational Toxicology

SSCT-SweTox WorkshopOctober 14, 2015

ToxPi

PRIORITIZATION

Interactive Chemical Safety for Sustainability Web

Application

TOXCAST iCSS v0.5

Tool TipDescription of Assays (Data) or whatever is being hovered over Prioritization Mode

Desc Summary Log

80-05-7 80-05-1 80-05-2 80-05-3 80-05-5

CHEMICAL SUMMARY

CASRN ChemicalName

80-05-7 Bisphenol A80-05-1 Bisphenol B80-05-2 Bisphenol C80-05-3 Bisphenol D80-05-4 Bisphenol E80-05-5 Bisphenol F80-05-6 Bisphenol G80-05-7 Bisphenol H80-05-8 Bisphenol I80-05-9 Bisphenol J

A B C D E G HF

1 1 1 1 1 1 11

SCORING

APPLY

Stud

ies

The views expressed in this presentation are those of the author and do not necessarily reflect the views or policies of the U.S. EPA

National Center forComputational Toxicology

Multiple Destinations Necessary to Address Diverse Regulatory Needs…

• Multiple drivers shape assessment needs– Regulatory demands– Economic considerations– Multiple applications

• Chemical assessments are “fit-for-purpose”– Prioritization (e.g., EDSP, PMN, SNUR)– Screening-level assessments (e.g., CCL,

GreenChem)– Provisional assessments (e.g., PPRTVs)– Toxicity assessments (e.g., IRIS)– Risk assessments (e.g., MCLs,

pesticides)


2

Common Elements are Considered Across Many Decision Contexts

Phys-Chem Properties

HazardDose

Response

PKExposure

Uncertainty & Variability

ScreeningAssessment

PrioritizationRisk

Assessment


But, the Current Infrastructure is Antiquated and Inefficient

0

10

20

30

40

50

60

70

Perc

ent o

f Che

mic

als

Acute CancerGentox Dev ToxRepro Tox

Judson, et al EHP (2010)

... and cannot efficiently assess safety of all the existing chemicals or keep pace with those being developed

1920 1930 1940 1950 1960 1970 1980 1990 2000 2010| | | | | | | | |

Acute toxicity studies (LD50) developed to

standardized batches of pharmaceuticals

Genotoxicityassays

developed

Draize test introduced for eye

irritants

Rodent cancer bioassay

introduced

Continuous breeding studies for reproductive

toxicity

Thalidomide led to testing of

pharmaceuticals for developmental

toxicity

Current testing paradigm does not incorporate advances in technology and does not provide mechanistic data


Infrastructure Must Be Flexible and On a Scale Not Yet Employed in Tox


5

Need to Start With High Quality Chemical Foundation

Quality of Structure-CAS Mappings Chemical Structure & Property Database

• DSSTox database of over 1 million chemical structure-ID mappings

• DSSTox QC flags indicate confidence in structural associations

• Database of phys-chem property predictions and experimental measurements with QC flags (coming soon)

0% 1% 3%

10%

56%

30%DSSTox_High

DSSTox_Low

Public_High

Public_Medium

Public_Low

Public_Untrusted


6

Need to Start With High Quality Chemical Foundation

Pass = C (75%) or greaterFail = D, F, Ac, Bc, Cc

52%

6%3%

39%

Pass Fail Degrade ND

Analytical QC of Chemical Library

• EPA ToxCast chemical screening library of 3,729 unique chemicals


7

Biological Responsecell proliferation and death

cell differentiationEnzymatic activity

mitochondrial depolarizationprotein stabilization

oxidative phosphorylationreporter gene activationgene expression (qNPA)

receptor bindingreceptor activitysteroidogenesis

Target Familyresponse Element

transportercytokineskinases

nuclear receptorCYP450 / ADMEcholinesterasephosphatases

proteasesXME metabolism

GPCRsion channels

Assay Designviability reporter

morphology reporterconformation reporter

enzyme reportermembrane potential reporter

binding reporterinducible reporter

Cell Formatcell free cell lines

primary cellscomplex cultures

free embryos

High-Throughput Bioactivity Screening as an Indicator of Hazard

ToxCast

Concentration

Res

pons

e

~700 Cell & biochemical

assays

~2,000 Chemicals Hazard


Lots of Possible Research to Highlight

Promiscuity

Estrogen Receptor

Thyroid Peroxidase


9

Most Environmental Chemicals are Nonselective for Biological Targets

ToxCast

Concentration

Res

pons

e


assays

~2,000 Chemicals

0.01 0.1 1 100.0

0.2

0.4

0.6

0.8

1.0

Concentration to Activate First AssayConcentration to Activate 10% of Assays

Cum

ulat

ive

Frac

tion

of C

hem

ical

s~80% with < 3-fold ratio

Nonselective

Selective


In Vitro Assay Selectivity as a Starting Point for AOPs

Selectively Activated In Vitro Assays

Selective Chemical

DefineMode-of-Action

Confirm Human Relevance and Derive

Point-of-Departure

Key Events

0.01 0.1 1 100.0

0.2

0.4

0.6

0.8

1.0


Cum

ulat

ive

Frac

tion

of C

hem

ical

s

~80% with < 3-fold ratio

Nonselective

Selective


Computational Modeling to Integrate Upstream Events in ER AOP

ER Receptor Binding(Agonist)

Dimerization

CofactorRecruitment

DNA Binding

RNA Transcription

Protein Production

ER-inducedProliferation

R3

R1

R5

R7

R8

R6

N1

N2

N3

N4

N5

N6

A1

A2

A3

A4

A5

A6

A7

A8

A9

A10

A12

A13

A14

A15

A16

ε3

A11

Receptor (Direct Molecular Interaction)

Intermediate Process

Assay

ER agonist pathway

Interference pathway

Noise Process

ER antagonist pathway

R2

N7

ER Receptor Binding

(Antagonist)

A17

A18

Dimerization

N8

N9DNA Binding

CofactorRecruitment

N10AntagonistTranscriptionSuppression

R4

R9

• 18 ToxCast/Tox21 in vitro assays measure ER-related activity

• Screened ~2,000 total chemicals• 38 in vitro reference chemicals• 39 in vivo reference chemicals

Judson et al., Tox Sci. In Press


Computational Modeling to Integrate Upstream Events in ER AOP

ER Receptor Binding(Agonist)

Dimerization

CofactorRecruitment

DNA Binding

RNA Transcription

Protein Production

ER-inducedProliferation

R3

R1

R5

R7

R8

R6

N1

N2

N3

N4

N5

N6

A1

A2

A3

A4

A5

A6

A7

A8

A9

A10

A12

A13

A14

A15

A16

ε3

A11

Receptor (Direct Molecular Interaction)

Intermediate Process

Assay

ER agonist pathway

Interference pathway

Noise Process

ER antagonist pathway

R2

N7

ER Receptor Binding

(Antagonist)

A17

A18

Dimerization

N8

N9DNA Binding

CofactorRecruitment

N10AntagonistTranscriptionSuppression

R4

R9

18 ToxCast/Tox21 In Vitro Assays Measure ER-Related Activity True Positive 26 (25)

True Negative 11 (11)

False Positive 1 (0)

False Negative 2 (2)

Accuracy 0.93 (0.95)

Sensitivity 0.93 (0.93)

Specificity 0.92 (1.0)

True Positive 29 (29)

True Negative 8 (8)





Specificity 0.67 (0.89)

In Vitro Reference Chemicals*

In Vivo Reference Chemicals*

Judson et al., Tox Sci. In PressBrowne et al., ES&T. 2015

*Values in parentheses exclude inconclusive chemicals


AOP Network for Thyroid Hormone Disruption

Paul et al., In Review

Presenter

Presentation Notes

Inorganic iodine enters the body primarily as iodide, I−. After entering the thyroid follicle (or thyroid follicular cell) via a Na+/I− symporter (NIS) on the basolateral side, iodide is shuttled across the apical membrane into the colloid via pendrin, after which thyroid peroxidase oxidizes iodide to atomic iodine (I) or iodinium (I+). The "organification of iodine," the incorporation of iodine into thyroglobulin for the production of thyroid hormone, is nonspecific; that is, there is no TPO-bound intermediate, but iodination occurs via reactive iodine species released from TPO.[4] The chemical reactions catalyzed by thyroid peroxidase occur on the outer apical membrane surface and are mediated by hydrogen peroxide


Identification of Selective TPO Inhibitors

Paul et al., In Review


What About the Non-Selective Chemicals?

?????????

Nonselective Chemical

Define Point-of-DepartureBMR

BMDBMDL

0.01 0.1 1 100.0

0.2

0.4

0.6

0.8

1.0


Cum

ulat

ive

Frac

tion

of C

hem

ical

s

~80% with < 3-fold ratio

Nonselective

Selective

Most sensitive response generally protective on a

dose level

Specific adverse outcome not reliably

predicted

Absence of activity difficult to interpret


16

Efforts to Ensure HTS Data Quality and Increase Transparency

• Public release of Tox21 and ToxCast data on PubChem and EPA web site (raw and processed data)

• ToxCast data analysis pipeline has been completely revamped

• More statistically rigorous and less prone to outliers

• Data quality flags to indicate concerns with chemical purity and identity, noisy data, systematic assay errors, and activity in range of cytotoxicity

• Available for download as an R package

• Release of ToxCast “Owner’s Manual”• Chemical Procurement and QC

• Data Analysis

• Assay Characteristics and Performance

• External audit on ToxCast data and data analysis pipeline

• Continued offering of webinars and workshops to educate stakeholders on high-throughput screening data analysis and interpretation

FLAGS:Only one conc above baseline, activeBorderline active


17

Efforts to Address Metabolism Challenge

ToxCast

Concentration

Res

pons

e


assays

~2,000 Chemicals

Limited Xenobiotic

Metabolism


18

Efforts to Address Metabolism Challenge

In Development

P450 Glo IPA Assay


19

Efforts to Address Limited Biological Coverage

ToxCast

Concentration

Res

pons

e


assays

~2,000 Chemicals

In DevelopmentGene Coverage

Pathway Coverage*

ToxCast

Not in ToxCast

*At least one gene from pathway represented

Multiple cell lines/types

~2,000 Chemicals

High-Throughput Transcriptomics

Lamb et al. Science (2006)

Concentration

Res

pons

e

• Low-cost• Whole genome• 384-well• Automatable


20

Efforts to Address Limited Biological Coverage

Illumina RNA AccessL1000 Illumina Low Coverage WT

Log2

FC

L10

00

Log2 FC Affymetrix Log2 FC Affymetrix Log2 FC Affymetrix

Log1

0 FC

RN

A Ac

cess

Log1

0 FC

Low

Cov

erag

e W

T

r2 = 0.03 r2 = 0.60 r2 = 0.63


21

Developing a Broad Hazard Screening Platform

Tier 1

Select In VitroHTS Assays

Non-Selective Interacting Chemicals

Tier 0High-Throughput Transcriptomic

Assay

Selective Interacting Chemicals

Tier 2Organotypic Assays and Virtual Tissue

Modeling

Estimate Point-of-Departure Based on AOP

Estimate Point-of-Departure Based on Biological Activity

ConfirmationScreen

Discriminate Perturbation from Adversity and

Estimate Inter-Individual Variability


Integrating High-Throughput Pharmacokinetic Approach

Rotroff et al., Tox Sci., 2010Wetmore et al., Tox Sci., 2012

Reverse Dosimetry

Oral Exposure

Plasma Concentration

ToxCast AC50 Value

Oral Dose Required to Achieve Steady State

Plasma Concentrations Equivalent to In Vitro

Bioactivity

Human Liver Metabolism

Human Plasma Protein Binding

Population-Based IVIVE Model

Upper 95th Percentile CssAmong 100 Healthy

Individuals of Both Sexes from 20 to 50 Yrs Old

309 EPA ToxCastPhase I Chemicals

PK


Integrating High-Throughput Pharmacokinetic Approach


Reverse Dosimetry

Oral Exposure


ToxCast AC50 Value



Bioactivity

~700 In Vitro ToxCast Assays

Least Sensitive Assay

MostSensitive

Assay








Comparing with Exposure for Risk Context


Reverse Dosimetry

Oral Exposure


ToxCast AC50 Value



Bioactivity

~700 In Vitro ToxCast Assays

Least Sensitive Assay

Ora

l Equ

ival

ent D

ose

(mg/

kg/d

ay)

What are humans exposed to?

?

?

?

Chemical

In VitroBioactivity

MostSensitive

Assay







Exposure


Comparing with Exposure for Risk Context

Wetmore et al., Tox Sci., 2012

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e

Ethoxyquin

2,4,5-

Trichloro

phenol

Diisobutyl

adipate

2,4,6-

Trichloro

phenol

Carbofuran

Benzo

phenone

p,p'-DDD

Fluoranthen

e

4,4'-O

xydian

iline

Proges

terone

Diphenhyd

ramine h

ydro

chlorid

e

methyl

1H-ben

zimidaz

ol-2-yl

car

2,6-D

i-tert-

butylphen

ol

Fluconaz

ole

2-(2-B

utoxyeth

oxy)et

hanol

2-meth

oxy-5-

nitroan

iline

2-Chloro

-2'-deo

xyad

enosin

e

1,5-D

iaminonap

hthalene

0.00001

0.0001

0.001

0.01

0.1

1

10

100

1000

10000 Gen. USMHE

Ora

l Equ

ival

ent D

ose

or E

stim

ated

Exp

osur

e(m

g/kg

/day

)

N-[(3R

)-1-az

abicy

clo[2.

2.2]oct

(2S,3S

)-N-[2

-meth

oxy-5-

(triflu

o-Tolidine

1,2-D

initroben

zene

Nitroben

zene

Predniso

ne

4-(Butan

-2-yl)

phenol

4-Meth

ylphen

ol

Phosmet

Quinoline

Volinan

serin

Cyclopam

ine

Methyl

octanoate

Butylpara

ben

4-tert

-Butyl

phenol

2,6-D

initrotoluen

e

N-Nitr

osodi-N

-butylam

ine

1,5,9-

Cyclododec

atrien

e

3,3,4,

4,5,5,

6,6,7,

7,8,8,

8-Trid

Methyle

ugenol

Methylp

araben

N-Phen

yl-1,4

-benze

nediam

ine

3,3'-D

imeth

oxyben

zidine)

2,6-D

imeth

ylphen

ol

(-)-C

otinine

4-Nitr

otoluene

Oxytet

racyc

line h

ydro

chlorid

e

Eugenol

2,4-D

i-tert-

butylphen

ol

N,N'-M

ethyle

nebis(

acryl

amide)

Candoxa

tril

p-Cres

idine

Propylp

araben

Dibutyl hex

aned

ioate

Ethylpara

ben

Tannic

acid

Resorci

nol

Dimeth

yl glutar

ate

N,N-D

imeth

ylocty

lamine

Erythro

mycin

Triethyle

ne Glyc

ol Diac

etate

Triethyl

citrat

e

Diallyl

phthalate

Dimeth

yl su

ccinate

0.00001

0.0001

0.001

0.01

0.1

1

10

100

1000

10000Gen. USMHE

Ora

l Equ

ival

ent D

ose

or E

stim

ated

Exp

osur

e(m

g/kg

/day

)

1-Hyd

roxy

pyrene

Azelai

c acid

di(2-et

hylhex

yl)

Tridem

orph

Anthracen

e

Triamcin

olone

Acetam

inophen

Octhilin

one

2-Ben

zylid

eneo

ctanal

Tri(eth

ylene g

lycol) b

is(2-e

th

N-Nitr

osodiphen

ylamine

Aldrin

Phorate

2,6-D

iethyla

niline

Vernolat

e

Genist

ein

5-Hep

tyldihyd

ro-2(

3H)-f

uranone

Terbuthyla

zin

Mepan

ipyrim

2-Anisi

dine

Tributyl

phosphate

Pyrene

3-Hyd

roxy

fluoren

e

Pyrimeth

amine

2-Nap

hthylamine

Bisphen

ol B

Rifampici

n

Di(2-et

hylhex

yl)ad

ipate

Simva

statin

2-tert

-Butyl

-4-meth

oxy-phen

ol

Monuron

N,N,4-

Trimeth

ylanilin

e

1,3-D

iphenylg

uanidine

Flutamide

Heptac

hlor

Benzid

ine

Caffein

e

Haloperi

dol

Lovasta

tin

Carbam

azep

ine

PK 1119

5

Chlorthal-

dimeth

yl

Butam

8-Hyd

roxy

quinoline

3-Nitr

otoluene

2-Meth

ylphen

ol

4,4'-M

ethyle

ne bis(

2-meth

ylani

Androste

nedione

Diisopro

pyl meth

ylphosp

honate

Phenolphthale

in0.00001

0.0001

0.001

0.01

0.1

1

10

100

1000

10000Gen. USMHE

Ora

l Equ

ival

ent D

ose

or E

stim

ated

Exp

osur

e(m

g/kg

/day

)

Bioactivity from Wetmore et al., Tox Sci., 2015


27

Developing High-Throughput Exposure Models

(Bio) Monitoring

Dataset 1

Dataset 2…

e.g., CDC NHANES study

Wambaugh et al., Environ Sci Technol., 2014

Predicted Exposures

…

Use

Production Volume

Inferred Exposures

Pharmacokinetic Models

Estimate Uncertainty

Calibrate models

Infe

rred

Exp

osur

e

Predicted Exposure


28

High-Throughput Exposure Heuristics

Wambaugh et al., Environ Sci Technol., 2014

Heuristic DescriptionACToR “Consumer use &

Chemical/Industrial Process use”Chemical substances in consumer products (e.g., toys, personal care products, clothes, furniture, and home-care products) that are also used in industrial manufacturing processes. Does not include food or pharmaceuticals.

ACToR “Chemical/Industrial Process use with no Consumer use” Chemical substances and products in industrial manufacturing processes that are not

used in consumer products. Does not include food or pharmaceuticals

ACToR UseDB “Pesticide Inert use”Secondary (i.e., non-active) ingredients in a pesticide which serve a purpose other than repelling pests. Pesticide use of these ingredients is known due to more stringent reporting standards for pesticide ingredients, but many of these chemicals appear to be also used in consumer products

ACToR “Pesticide Active use” Active ingredients in products designed to prevent, destroy, repel, or reduce pests (e.g., insect repellants, weed killers, and disinfectants).

TSCA IUR 2006 Total Production Volume Sum total (kg/year) of production of the chemical from all sites that produced the

chemical in quantities of 25,000 pounds or more per year. If information for a chemical is not available, it is assumed to be produced at <25,000 pounds per year.


29

Comparing Bioactivity with Exposure Predictions for Risk Context

Wetmore et al., Tox Sci., 2015

Chemicals


30

Work In Progress to Address Exposure Data Gaps

ExpoCast

Estimate Uncertainty

EDSP Chemicals

QSARs and HTE Data

BiomonitoringData

Infe

rred

(Rev

erse

) Exp

osur

e

Model 1

Model 2…

Calibrate models

Apply calibration and uncertainty to other chemicals

Evaluate Model Performanceand Refine Models

Forward Predictions

Exposure Inference

Dataset 1

Dataset 2…

• Limited domain of applicability of existing biomonitoring data

• Limited knowledge of qualitative and quantitative composition of chemical ingredients and emissivity of consumer products and home goods

• Limited experimental measurements of physical chemical properties


31

Non-Targeted Analysis of Consumer Products (Baby Products Pilot)

Brand #1 Brand #2 Brand #3 Brand #4 Brand #5

Category Number Peak Area Number Peak Area Number Peak Area Number Peak Area Number Peak AreaReported peaks with reviewed library matches 98 430366619 106 123796416 114 1439691153 67 189430603 56 88784398

Reported unknowns (>500,000) 11 138064492 40 67604884 27 473487828 0 0 1 961833Confirmed Hydrocarbons (n-alkanes) 7 4549995 5 85130984 20 14500452 20 28668397 21 30176895Unconfirmed Hydrocarbons C10-C16 171 96701978 245 992103366 141 116097791 117 50621285 109 83257076Unconfirmed Hydrocarbons C17-C32 -- -- -- -- 181 107558101 261 155810354 243 142323751Unresolved C17-C32 2457 116556867334 1934 67979297371 -- -- -- -- -- --

Excluded unknowns (<500,000) 37 5917014 120 21963344 66 10205424 52 7493668 48 6471715

Excluded non-specific (<500,000) 1 391747 0 0 11 425378 17 1038180 14 1169202Excluded trace (<100,000) and similarity < 850 36 2259514 32 1795899 118 7218428 116 7015017 123 6698879

Excluded artifacts 1 393278 4 2172274 34 162097507 16 10001825 7 11311153Total 2819 117235511971 2486 69273864537 712 2331282062 666 450079329 622 371154902

• Solvent extracted followed by GCXGC-TOFMS• A total of 306 unique compounds identified across all toys, including 102 Tox21

chemicals. • Bisphenol A found in “BPA free product”


32

Pulling the Pieces Together for Regulatory Application


HazardDose

Response

PKExposure

Uncertainty & Variability


Hazard

Dose Response PK

Exposure Uncertainty & Variability


33

Application to Endocrine Disruptor Screening Program

• ER, AR, and exposure modeling approaches underwent FIFRA SAP review

• FR Notice published for using ER assays and models to replace subset of Tier 1 assays

• Work continues on AR, steroidogenesis, and thyroid

Phys-Chem Properties Hazard

Dose Response PK



34

Application to Risk AssessmentPhys-Chem Properties Hazard

Dose Response PK


• Semi-automated assessment workflow

• Dashboard interface

• Two case studies


35

• Dealing with the “V” word• Defining a fit-for-purpose framework(s) that is time and resource efficient (e.g.,

Judson et al., ALTEX 30(1):51-6, 2013)

• Role of in vivo rodent studies• Accepting and incorporating the inherent uncertainty of the in vivo studies

Challenges


36

Owens and Koëter, Environ Health Perspect, 2003 Kleinstreuer et al., EHP In Press

Dealing with the “V” WordOriginal OECD TG 440 Validation Concordance of In Vivo Uterotrophic Studies

Active

Inactive

Predictive Performance of In Vitro Assays for In Vivo Uterotrophic Studies

True Positive 29 (29)

True Negative 8 (8)





Specificity 0.67 (0.89)Browne et al., ES&T. 2015


37

• Dealing with the “V” word• Defining a fit-for-purpose framework(s) that is time and resource efficient (e.g.,

Judson et al., ALTEX 30(1):51-6, 2013)

• Role of in vivo rodent studies• Accepting and incorporating the inherent uncertainty of the in vivo studies

• Moving from an apical to a molecular paradigm and defining adversity

• Legal defensibility of new methods and assessment products

• Predicting human safety vs. toxicity

• Systematically integrating multiple data streams from the new approaches in a risk-based, weight of evidence assessment

• Ensuring a comprehensive screening and testing paradigm

• Quantifying and incorporating uncertainty and variability

• Application to cumulative risk/mixtures

Challenges


38

Acknowledgements

Tox21 Colleagues:NTP CrewFDA CollaboratorsNCATS Collaborators

ORD Colleagues:NERLNHEERLNCEA

Alice Yau - SWRI

EPA’s National Center for Computational Toxicology


39

New Dashboards Provide Improved Access to the Data

iCSS Dashboard

http://actor.epa.gov/dashboard2/

EDSP21 Dashboard

http://actor.epa.gov/edsp21/


Extra Slides


Eigenvalues from Principle Component Analysis

Data Matrix

Cell Lines

Gen

es

Data Matrix

Cell Lines

Gen

es

OR


All Genes(20,960)

Druggable 1(3845)

Druggable 5(237)

NCI60(59)

1755%

1757%

2166%

Cancer Atlas(1036)

15466%

16571%

20298%

Primary Cells(303)

3983%

4083%

4686%

Combined(1398)

17272%

18777%

21199%

Dimensionality and Fraction of Variance Explained

# genes# cell lines #

eigenvalues ≥ 1

• Dimensionality is relatively constant within cell lines

• Dimensionality increases (slightly) as # genes decrease


As Many Genes ‘On’ as Possible

Toy Example: Call a gene ‘on’ if its expression is in the upper 20th %-ile of its distribution across cell lines.

cell_linesgenes A B C D E F

a 0.250 -0.960 -0.570 0.60 -0.25 -1.300b 3.300 -0.380 -0.980 -0.59 1.90 1.100c -0.042 2.200 0.130 1.80 -0.68 2.900d -1.100 0.200 0.240 -0.18 0.77 0.013e -0.310 -1.100 1.000 -0.45 -0.58 -0.490f 0.600 0.520 -0.011 0.83 1.70 0.270g -0.610 -2.400 -0.290 0.45 -0.33 -0.130h 1.000 -0.011 0.990 -0.82 -2.90 0.420

cell_linesgenes A B C D E F

a 0 0 0 1 0 0b 1 0 0 0 0 0c 0 0 0 0 0 1d 0 0 0 0 1 0e 0 0 1 0 0 0f 0 0 0 0 1 0g 0 0 0 1 0 0h 1 0 0 0 0 0

cell_linesgenes B C D E F

a 0 0 1 0 0c 0 0 0 0 1d 0 0 0 1 0e 0 1 0 0 0f 0 0 0 1 0g 0 0 1 0 0

cell_linesgenes B C F

c 0 0 1e 0 1 0

cell_linesgenes B C E F

c 0 0 0 1d 0 0 1 0e 0 1 0 0f 0 0 1 0

Result:Cell Lines A, D, and E cover 6 of 8 genes.


Fraction of Genes ‘On’ in At Least One Cell Line

Developing a Roadmap for Integrating Computational and In ...

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