A chemical-genetic interaction map of small molecules using … · A chemical-genetic interaction...

A chemical-genetic interaction map of smallmolecules using high-throughput imaging incancer cellsMarco Breinig, Felix A. Klein, Wolfgang Hu-ber and Michael BoutrosAccepted for publication at Molecular Sys-tems Biology

Felix A. Klein[1em] European Molecular Biology Laboratory (EMBL),Heidelberg, [email protected]

May 1, 2018

Contents

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.1 Data avalability . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.2 Accessing the data contained in the PGPC package . . . . . . . . 3

2 Image and data processing . . . . . . . . . . . . . . . . . . . . . 52.1 Image processing on cluster . . . . . . . . . . . . . . . . . . . 5

2.2 Data extraction and conversion . . . . . . . . . . . . . . . . . . 6

2.3 Annotation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.4 Removal/Filtering of empty wells and features . . . . . . . . . . . 8

2.5 Generalized logarithm transformation . . . . . . . . . . . . . . . 9

3 Feature selection and calculation of interactions . . . . . . . . . 103.1 Quality control of features . . . . . . . . . . . . . . . . . . . . . 10

3.2 Feature selection by stability . . . . . . . . . . . . . . . . . . . 12

3.3 Calculation of interactions. . . . . . . . . . . . . . . . . . . . . 14

4 Display of phenotypes as phenoprints . . . . . . . . . . . . . . 15

PGCP, 2014

5 Display of interactions as star plots. . . . . . . . . . . . . . . . . 205.1 Scaled to the range of 0 to 1 . . . . . . . . . . . . . . . . . . . 20

5.2 Using the absolute values of an interaction. . . . . . . . . . . . . 25

6 Clustering of cell lines . . . . . . . . . . . . . . . . . . . . . . . . 306.1 Clustering cell lines based on features. . . . . . . . . . . . . . . 30

6.2 Clustering cell lines based on interaction terms . . . . . . . . . . 32

7 Compound - cell line interaction network . . . . . . . . . . . . . 347.1 Extract significant interactions for visualization. . . . . . . . . . . 34

7.1.1 Removing controls from the interactions. . . . . . . . . . . . 357.1.2 Pleiotropic degree . . . . . . . . . . . . . . . . . . . . . 377.1.3 Grouping of features into feature classes. . . . . . . . . . . . 427.1.4 Interaction map export for Cytoscape . . . . . . . . . . . . . 43

8 Heat maps of interaction profiles . . . . . . . . . . . . . . . . . . 45

9 Clustering of interaction profiles . . . . . . . . . . . . . . . . . . 489.1 Clustering of interaction profiles using the filtered data . . . . . . . 48

9.1.1 Reordered dendrogram . . . . . . . . . . . . . . . . . . . 49

9.2 Structural similarity of compounds. . . . . . . . . . . . . . . . . 509.2.1 Chemical similarity heat map ordered by interaction profile simi-

larity . . . . . . . . . . . . . . . . . . . . . . . . . . . 529.2.2 Combined cluster heat map . . . . . . . . . . . . . . . . . 53

9.3 Clustering of interaction profiles using the filtered data of a singleparental cell line . . . . . . . . . . . . . . . . . . . . . . . . . 55

9.4 Clustering of interaction profiles using the filtered cell number asfeature.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

10 Correlation between interaction profiles of shared drug targets. 6010.1 Using the target selectivity for grouping . . . . . . . . . . . . . . 60

10.2 Using the chemical similarity for grouping . . . . . . . . . . . . . 63

11 Follow-up: Drug combinations . . . . . . . . . . . . . . . . . . . 6811.1 Quality control . . . . . . . . . . . . . . . . . . . . . . . . . . 70

11.2 Interactions / Synergies . . . . . . . . . . . . . . . . . . . . . . 7211.2.1 Analysis functions . . . . . . . . . . . . . . . . . . . . . 7211.2.2 Investigating drug synergies . . . . . . . . . . . . . . . . . 7711.2.3 Investigating drug synergies, DLD cell line. . . . . . . . . . . 81

12 Follow-up: Proteasome inhibition assay . . . . . . . . . . . . . . 8512.0.1 Data processing and quality control. . . . . . . . . . . . . . 8512.0.2 Viability normalization . . . . . . . . . . . . . . . . . . . 8612.0.3 Proteasome activity normalization . . . . . . . . . . . . . . 8812.0.4 Result summary . . . . . . . . . . . . . . . . . . . . . . 89

13 Session Info . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

2

PGCP, 2014

1 Introduction

This document is associated with the R package PGPC, which contains the input data andthe R code for the statistical analysis presented in the following paper:

Systems pharmacology by integrating image-based phenotypic profiling and chemical-geneticinteraction mappingMarco Breinig, Felix A. Klein, Wolfgang Huber, and Michael Boutrosin preparation

The figures of the paper and the intermediate results are reproduced in this vignette. Inter-mediate results are stored in the folder "result" created in the current working directory.

library("PGPC")

if(!file.exists(file.path("result","data")))

dir.create(file.path("result","data"),recursive=TRUE)

if(!file.exists(file.path("result","Figures")))

dir.create(file.path("result","Figures"),recursive=TRUE)

To reduce the computational effort required, the package contains some analysis resultsalready in precomputed form. However, the corresponding code junks are contained in thisdocument, and they can be executed to recompute all results except the initial image analysis.For the inital image analysis approximately 3 TB of image data were processed. We providethe analysis of one single well to show the steps used in the initial image analysis.

1.1 Data avalability

Complementary views on this dataset are available through different repositories. The imagedata files are available from the BioStudies database at the European Bioinformatics Institute(EMBL-EBI) under the accession S-BSMS-PGPC1. An interactive front-end for explorationof the images is provided by the IDR database (http://dx.doi.org/10.17867/10000101).

The authors are hosting an interactive webpage to browse images and interaction profiles athttp://dedomena.embl.de/PGPC.

The bulk data can also be provided on hard disk drives upon request.

1.2 Accessing the data contained in the PGPC package

The final data object can be loaded by

data("interactions", package="PGPC")

A description of this object is given in the following Section together with further data objectsthat are contained within this package. Additional information on the objects can be obtainedfrom the R documentation by invoking the help page.

?interactions

The package contains the following data objects:

3
http://wwwdev.ebi.ac.uk/biostudies/studies/S-BSMS-PGPC1http://dx.doi.org/10.17867/10000101http://dedomena.embl.de/PGPC

PGCP, 2014

ftrs

data.frame with the feature data extracted from the images using imageHTS.

datamatrixTransformed

Feature data represented as a list containing array D and list anno. D is a four-dimensional array of the filtered feature data that passed quality control. The di-mensions represent:

1. drug

2. cell line

3. replicate

4. feature

To select the second feature of the first replicate of the third drug of the fourth cellline use:value=datamatrixfull$D[3,4,1,2]

To select all values of the drugs, cell lines and replicates for the first feature use:values=datamatrixfull$D[, , ,1]

anno is annotation of the dimensions of D, represented as a list containing a data.framenamed drug, a data.frame named line, a vector named repl and a vector named ftr.Use $ to access the elements (e. g. anno$drug).

selected

list of 4 elements containing the results of the feature selection. The elements are, thevector selected of selected features, the vector correlation of their correlation, thevector ratioPositive with the fraction of positive correlations for each iteration anda list correlationAll which contains the correlations of all features at each iterationstep.

interactions

list containing the list anno, array D, array res, list effect and array pVal.

res is a four-dimensional array of the interaction terms and has the same dimensionsas D. The dimensions represent:

1. drug

2. cell line

3. replicate

4. feature

effect contains the drug and cell line effect as three-dimensional array

drug and line with the following dimensions:

1. drug/cell line

2. replicate

3. feature

pVal is an array containing the p-values, adjusted p-values and correlation betweenreplicates of interactions. The dimensions represent:

1. drug

2. cell line

4

PGCP, 2014

3. p-value, adjusted p-value, correlation

4. feature

2 Image and data processing

2.1 Image processing on cluster

The images were processed using the R packages imageHTS and EBImage [1] adaptingprevious strategies [2, 3, 4]. The following code shows the processing script that was usedto segment the images and extract features. The calculations were parallelized by splittingthe wells defined by the unique identifiers unames into different processes. The object ftrscontains all the extracted features and is included in this package.

In the following code serverURL is the path to the folder which contains the example filesand images in the local installation of the PGPC package. localPath is a temporary localworking directory for the results. If you want to keep the results beyound the R session changethis path to a directory on your machine. If you want to run this example code approximatly150 MB free space are required for the 1 example wells. For the whole screen of 36864 wellsapproximately 5 TB were required. The required annotation and image files are automaticallyretrieved from the provided serverURL if they are not found in the local directory.

localPath = tempdir()

serverURL = system.file("extdata", package = "PGPC")

imageConfFile = file.path("conf", "imageconf.txt")

## circumvent memory problem on 32bit windows by segementing only spot 1.

if(.Platform$OS.type == "windows" & R.Version()$arch == "i386")

imageConfFile = file.path("conf", "imageconf_windows32.txt")

x = parseImageConf(imageConfFile, localPath=localPath, serverURL=serverURL)

unames = getUnames(x) ## select all wells for processing

unames = c("045-01-C23") ## select single well for demonstration

segmentWells(x, unames, file.path("conf", "segmentationpar.txt"))

PGPC:::extractFeaturesWithParameter(x,

unames,

file.path("conf", "featurepar.txt"))

summarizeWellsExtended(x, unames, file.path("conf", "featurepar.txt"))

## PGPC:::mergeProfiles(x) only needed if wells were processed in parallel

ftrs = readHTS(x, type="file",

filename=file.path("data", "profiles.tab"),

format="tab")

5

PGCP, 2014

Images were processed in parallel on a cluster and the corresponding session info is providedhere:

R version 3.0.1 (2013-05-16)

Platform: x86_64-unknown-linux-gnu (64-bit)

locale:

[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8

[4] LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8

[7] LC_PAPER=C LC_NAME=C LC_ADDRESS=C

[10] LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C

attached base packages:

[1] parallel grid stats graphics grDevices utils datasets methods base

other attached packages:

[1] PGPC_0.1.1 SearchTrees_0.5.2 ggplot2_0.9.3.1 imageHTS_1.10.0

[5] cellHTS2_2.24.0 locfit_1.5-9.1 hwriter_1.3 vsn_3.28.0

[9] genefilter_1.42.0 EBImage_4.2.1 LSD_2.5 ellipse_0.3-8

[13] schoolmath_0.4 colorRamps_2.3 limma_3.16.4 splots_1.26.0

[17] geneplotter_1.38.0 lattice_0.20-15 annotate_1.38.0 AnnotationDbi_1.22.5

[21] Biobase_2.20.0 BiocGenerics_0.6.0 gplots_2.11.0.1 MASS_7.3-26

[25] KernSmooth_2.23-10 caTools_1.14 gdata_2.12.0.2 gtools_2.7.1

[29] RColorBrewer_1.0-5 BiocInstaller_1.10.2

loaded via a namespace (and not attached):

[1] abind_1.4-0 affy_1.38.1 affyio_1.28.0 bitops_1.0-5

[5] Category_2.26.0 class_7.3-7 colorspace_1.2-2 DBI_0.2-7

[9] dichromat_2.0-0 digest_0.6.3 e1071_1.6-1 graph_1.38.0

[13] GSEABase_1.22.0 gtable_0.1.2 IRanges_1.18.1 jpeg_0.1-4

[17] labeling_0.1 munsell_0.4 plyr_1.8 png_0.1-4

[21] prada_1.36.0 preprocessCore_1.22.0 proto_0.3-10 RBGL_1.36.2

[25] reshape2_1.2.2 robustbase_0.9-7 rrcov_1.3-3 RSQLite_0.11.4

[29] scales_0.2.3 splines_3.0.1 stats4_3.0.1 stringr_0.6.2

[33] survival_2.37-4 tiff_0.1-4 tools_3.0.1 XML_3.96-1.1

[37] xtable_1.7-1

2.2 Data extraction and conversion

The ftrs object is included in this vignette and the following code junks can be executed togenerate the results described in the following.

The extracted feature data are represented in data.frame where each row corresponds to awell in the screen defined by the unique identifier uname in the first column. All remainingcolumns represent the extracted features. There are 36864 wells (12 cell lines * 2 replicates* 4 plates * 384 wells).

data("ftrs", package="PGPC")

dim(ftrs)

ftrnames = colnames(ftrs[,-1])

For further processing the data are transformed into an array.

6

PGCP, 2014

makeArray

PGCP, 2014

line

PGCP, 2014

levels = apply(D, 4, function(x) length(unique(c(x))))

D = D[,,,!(levels < 2000)]

anno$ftr = anno$ftr[-which(levels < 2000)]

datamatrixWithAnnotation = list(D=D, anno=anno)

save(datamatrixWithAnnotation,

file=file.path("result","data","datamatrixWithAnnotation.rda"),

compress="xz")

2.5 Generalized logarithm transformation

The data is transformed to a logarithmic scale using a so-called generalized logarithm trans-formation [5]:

f(x; c) = log

(x+x2 + c2

2

). 1

This avoids singularities for values equal to or smaller than 0. We use the 5% quantile asparameter c. In the cases where the 5% quantile is zero we use 0.05 times the maximum asparameter c.

glog

PGCP, 2014

1372 chemical compounds 12 cell line 2 biological replicate 385 phenotypic features

A precomputed version of the datamatrixTransformed is available from the package and canbe loaded bydata("datamatrixTransformed", package="PGPC").

3 Feature selection and calculation of interactions

3.1 Quality control of features

To assess the reproducibility of a feature, we calculate the correlation of each feature fromthe replicates.

getCorrelation

PGCP, 2014

0 100 200 300 400

0.0

0.4

0.8

feature

corr

elat

ion

coef

ficie

nt

nnseg.dna.m.eccentricity.qt.0.01nseg.0.m.cx.mean

Figure 1: Correlation of featuresThis figure is the basis for Figure 1B in the paper.

for (i in seq_along(selection)){

plot(c(datamatrixTransformed$D[,,1,match(selection[i],

datamatrixTransformed$anno$ftr)]),

c(datamatrixTransformed$D[,,2,match(selection[i],

datamatrixTransformed$anno$ftr)]),

xlab="replicate 1",

ylab="replicate 2",

main = selection[i],

pch=20,

col="#00000033",

asp=1)

}

10 12 14 16

11.0

12.0

13.0

14.0

n

replicate 1

repl

icat

e 2

2.5 2.0 1.5

2.

2

2.0

1.

8

1.6

nseg.dna.m.eccentricity.qt.0.01

replicate 1

repl

icat

e 2

9.5 10.0 10.5

9.6

9.7

9.8

9.9

10.1

nseg.0.m.cx.mean

replicate 1

repl

icat

e 2

Figure 2: Correlation of feature n, nseg.dna.m.eccentricity.qt.0.01 and nseg.0.m.cx.mean

Features with a correlation lower than 0.7 were removed from further analysis.

## remove columns with low correlation or few levels

remove = correlation$name[correlation$cor < 0.7]

D = datamatrixTransformed$D[,,, -match(remove, datamatrixTransformed$anno$ftr)]

anno = datamatrixTransformed$anno

anno$ftr = anno$ftr[-match(remove, anno$ftr)]

datamatrixFiltered = list(D=D, anno=anno)

save(datamatrixFiltered,

file=file.path("result","data","datamatrixFiltered.rda"),

compress="xz")

rm(datamatrixTransformed)

The data are now represented in the 4-dimensional array D with dimensions

11

PGCP, 2014


3.2 Feature selection by stability

The data contains features that are partially redundant. Therefore we aim to select thefeatures that contain the least redundant information. As first step we center and scale thefeatures, using the median as a measure of location and the median absolute deviation as ameasure of spread. The features are selected as described by Laufer et al. [4] starting withcell number as first selected feature.

D = apply(datamatrixFiltered$D,

2:4,

function(x) { (x - median(x,na.rm=TRUE)) / mad(x, na.rm=TRUE) } )

D[!is.finite(D)] = 0

dim(D) = c(prod(dim(D)[1:2]),2,dim(D)[4])

forStabilitySelection = list(D=D,

Sample = 1:prod(dim(D)[1:2]),

phenotype = datamatrixFiltered$anno$ftr)

preselect=c("n")

selected

PGCP, 2014

col = ifelse(ratioPositive > 0.5,

brewer.pal(3,"Pastel1")[2], brewer.pal(3,"Pastel1")[1]),

ylab = "information gain")

Cel

l num

ber

Cel

l maj

or a

xis

Act

in H

aral

ick

text

ure

(1)

Nuc

lear

maj

or a

xis

Nuc

lear

A

ctin

ecc

entr

icity

SD

Act

in H

aral

ick

text

ure

SD

(1)

Nuc

lear

Har

alic

k te

xtur

e (1

)N

ucle

ar e

ccen

tric

ity5%

qua

ntile

of c

ell r

adiu

s5%

qua

ntile

of N

ucle

ar

Act

in in

tens

ityA

ctin

ecc

entr

icity

Nuc

lear

ecc

entr

icity

SD

Nuc

lear

Har

alic

k te

xtur

e S

D (

1)N

ucle

ar

Act

in H

aral

ick

text

ure

(1)

Act

in H

aral

ick

text

ure

(2)

Nuc

lear

A

ctin

Har

alic

k te

xtur

e S

D (

1)N

ucle

ar H

aral

ick

text

ure

SD

(2)

Nuc

lear

A

ctin

inte

nsity

MA

DA

ctin

Har

alic

k te

xtur

e S

D (

2)5%

qua

ntile

of N

ucle

ar r

adiu

s99

% q

uant

ile o

f Nuc

lear

rad

ius

Loca

l cel

l den

sity

SD

99%

qua

ntile

of c

ell m

ajor

axi

sN

ucle

ar H

aral

ick

text

ure

SD

(3)

Nuc

lear

are

aN

ucle

ar

Act

in m

ajor

axi

sA

ctin

Har

alic

k te

xtur

e S

D (

3)N

ucle

ar H

aral

ick

text

ure

(2)

1% q

uant

ile o

f Nuc

lear

A

ctin

inte

nsity

Nuc

lear

A

ctin

Har

alic

k te

xtur

e (2

)95

% q

uant

ile o

f nuc

lear

are

a95

% q

uant

ile o

f Nuc

lear

A

ctin

maj

or a

xis

Nuc

lear

A

ctin

Har

alic

k te

xtur

e S

D (

2)N

ucle

ar

Act

in H

aral

ick

text

ure

SD

(3)

99%

qua

ntile

of N

ucle

ar

Act

in e

ccen

tric

ity99

% q

uant

ile o

f cel

l rad

ius

1% q

uant

ile o

f nuc

lear

inte

nsity

Act

in e

ccen

tric

ity S

DN

ucle

ar

Act

in H

aral

ick

text

ure

SD

(4)

1% q

uant

ile o

f cel

l maj

or a

xis

info

rmat

ion

gain

0.0

0.2

0.4

0.6

0.8

Figure 3: Correlation of selected feature after subtraction of the information containted in the previ-ously selected features

par(mfrow=c(2,1))

barplot(ratioPositive-0.5,

names.arg=PGPC:::hrNames(selectedFtrs),

las=2,

col = ifelse(ratioPositive > 0.5,

brewer.pal(3,"Pastel1")[2], brewer.pal(3,"Pastel1")[1]),

offset=0.5,

ylab = "fraction positive correlated")

#abline(a=0.5,0)

selectedFtrs=selectedFtrs[ratioPositive > 0.5]

D = datamatrixFiltered$D[,,,match(selectedFtrs, datamatrixFiltered$anno$ftr)]

anno = datamatrixFiltered$anno

anno$ftr = anno$ftr[match(selectedFtrs, anno$ftr)]

datamatrixSelected = list(D=D, anno=anno)

save(datamatrixSelected,

file=file.path("result","data","datamatrixSelected.rda"),

compress="xz")

Cel

l num

ber

Cel

l maj

or a

xis

Act

in H

aral

ick

text

ure

(1)

Nuc

lear

maj

or a

xis

Nuc

lear

A

ctin

ecc

entr

icity

SD

Act

in H

aral

ick

text

ure

SD

(1)

Nuc

lear

Har

alic

k te

xtur

e (1

)N

ucle

ar e

ccen

tric

ity5%

qua

ntile

of c

ell r

adiu

s5%

qua

ntile

of N

ucle

ar

Act

in in

tens

ityA

ctin

ecc

entr

icity

Nuc

lear

ecc

entr

icity

SD

Nuc

lear

Har

alic

k te

xtur

e S

D (

1)N

ucle

ar

Act

in H

aral

ick

text

ure

(1)

Act

in H

aral

ick

text

ure

(2)

Nuc

lear

A

ctin

Har

alic

k te

xtur

e S

D (

1)N

ucle

ar H

aral

ick

text

ure

SD

(2)

Nuc

lear

A

ctin

inte

nsity

MA

DA

ctin

Har

alic

k te

xtur

e S

D (

2)5%

qua

ntile

of N

ucle

ar r

adiu

s99

% q

uant

ile o

f Nuc

lear

rad

ius

Loca

l cel

l den

sity

SD

99%

qua

ntile

of c

ell m

ajor

axi

sN

ucle

ar H

aral

ick

text

ure

SD

(3)

Nuc

lear

are

aN

ucle

ar

Act

in m

ajor

axi

sA

ctin

Har

alic

k te

xtur

e S

D (

3)N

ucle

ar H

aral

ick

text

ure

(2)

1% q

uant

ile o

f Nuc

lear

A

ctin

inte

nsity

Nuc

lear

A

ctin

Har

alic

k te

xtur

e (2

)95

% q

uant

ile o

f nuc

lear

are

a95

% q

uant

ile o

f Nuc

lear

A

ctin

maj

or a

xis

Nuc

lear

A

ctin

Har

alic

k te

xtur

e S

D (

2)N

ucle

ar

Act

in H

aral

ick

text

ure

SD

(3)

99%

qua

ntile

of N

ucle

ar

Act

in e

ccen

tric

ity99

% q

uant

ile o

f cel

l rad

ius

1% q

uant

ile o

f nuc

lear

inte

nsity

Act

in e

ccen

tric

ity S

DN

ucle

ar

Act

in H

aral

ick

text

ure

SD

(4)

1% q

uant

ile o

f cel

l maj

or a

xisfrac

tion

posi

tive

corr

elat

ed

0.2

0.4

0.6

0.8

1.0

Figure 4: Fraction of positive correlationsThis figure is the basis for Figure 1C and Appendix Figure S2A in the paper.

The feature selection algorithm selected 20 features which are kept for further analysis.

The final data are now represented in the 4-dimensional array D with dimensions

13

PGCP, 2014


3.3 Calculation of interactions

Te cell number is scaled to the dynamic range between positive (Paclitaxel) and negativecontrols (DMSO) for each cell line and replicate, to account for the different proliferationrates of the cell lines. For this purpose the median of the control wells is calculated and usedfor the scaling of cell number.

neg = grepl("neg", datamatrixSelected$anno$drug$GeneID)

pos = grepl("Paclitaxel", datamatrixSelected$anno$drug$GeneID)

negMedians = apply(datamatrixSelected$D[neg,,,1], c(2,3), median)

posMedians = apply(datamatrixSelected$D[pos,,,1], c(2,3), median)

Nnorm = (datamatrixSelected$D[,,,1] - rep(posMedians,

each=dim(datamatrixSelected$D)[1])) /

(rep(negMedians, each=dim(datamatrixSelected$D)[1]) -

rep(posMedians, each=dim(datamatrixSelected$D)[1]))

datamatrixSelected$D[,,,1]

PGCP, 2014

We use an additive model on glog-transformed data to calculate interactions following pre-vious approaches [6, 7, 3, 4]. The fit is implemented using the medpolish function, whichfits an additive model using Tukeys median polish procedure. The residuals from this fitrepresent the interactions.

interactions = getInteractions(datamatrixSelected,

datamatrixSelected$anno$ftr,

eps = 1e-4,

maxiter = 100)

save(interactions,

file=file.path("result","data", "interactions.rda"), compress="xz")

## check consistency

PGPC:::checkConsistency("interactions")

4 Display of phenotypes as phenoprints

To visualize the phenotypes represented by the extracted features, we display the features asradar plots called phenoprints. For this representation, each feature is scaled by the medianabsolut deviation observed and then linearly transformed to the interval 0 to 1.

The phenoprints for several drugs are shown for the two parental cell lines and the CTNNB1wt, cell line. Also the phenoprint for one DMSO control well is shown for all cell lines.

if(!exists("interactions")) data(interactions, package="PGPC")

D = interactions$D

D2 = D

dim(D2) = c(prod(dim(D2)[1:2]),dim(D2)[3],dim(D2)[4])

SD = apply(D2, 3, function(m) apply(m, 2, mad, na.rm=TRUE))

MSD = apply(SD, 2, function(x) { median(x,na.rm=TRUE) } )

pAdjusted = interactions$pVal[,,,2]

bin

PGCP, 2014

"Etoposide", "Amsacrine", "Colchicine",

"BIX"),

GeneID = c("neg ctr DMSO", "79902", "80101",

"80082", "79817", "79926",

"79294", "79028", "79184",

"80002"),

stringsAsFactors=FALSE)

drugPheno$annotationName =

interactions$anno$drug$Name[match(drugPheno$GeneID,

interactions$anno$drug$GeneID)]

drugPositions nuclear shape

#12 nseg.dna.m.eccentricity.sd --> nuclear shape

#7 nseg.dna.h.var.s2.mean --> nuclear texture

#13 nseg.dna.h.idm.s1.sd --> nuclear texture

#17 nseg.dna.h.cor.s2.sd --> nuclear texture

#1 n --> cell number

#6 cseg.act.h.f12.s2.sd --> cellular texture

#15 cseg.act.h.asm.s2.mean --> cellular texture

#18 cseg.dnaact.b.mad.mean --> cellular texture

#16 cseg.dnaact.h.den.s2.sd --> cellular texture

#10 cseg.dnaact.b.mean.qt.0.05 --> cellular texture

#3 cseg.act.h.cor.s1.mean --> cellular texture

#19 cseg.act.h.idm.s2.sd --> cellular texture

#14 cseg.dnaact.h.f13.s1.mean --> cellular texture

#9 cseg.0.s.radius.min.qt.0.05 --> cellular shape

#5 cseg.dnaact.m.eccentricity.sd --> cellular shape

#11 cseg.act.m.eccentricity.mean --> cellular shape

#2 cseg.act.m.majoraxis.mean --> cellular shape

#20 nseg.0.s.radius.max.qt.0.05 --> nuclear shape

#8 nseg.0.m.eccentricity.mean --> nuclear shape

orderFtr

PGCP, 2014

main = "Phenotypes of HCT116 P1",

draw.segments = FALSE,

scale=FALSE)

par007

PGCP, 2014

Phenotypes of HCT116 P1

DMSO PD98 Vinblastin

Vincristine Ouabain Rottlerin

Etoposide Amsacrine Colchicine

BIX

Nuclear major axis

Nuclear eccentricity SD

Nuclear Haralick texture (1)

Nuclear Haralick texture SD (1)Nuclear Haralick texture SD (2)Cell numberActin Haralick texture SD (1)

Actin Haralick texture (2)

NuclearActin intensity MAD

NuclearActin Haralick texture SD (1)

5% quantile of NuclearActin intensity


NuclearActin Haralick texture (1)

Actin Haralick texture SD (2)5% quantile of cell radiusNuclearActin eccentricity SDActin eccentricity

Cell major axis

5% quantile of Nuclear radius

Nuclear eccentricity

Phenotypes of HCT116 P2




BIX

Nuclear major axis











Cell major axis



Phenotypes of CTNNB1 wt




BIX

Nuclear major axis











Cell major axis



Figure 5: Phenoprints of the two parental cell lines and the CTNNB1 WT backgroundThis figure is the basis for Figure 1E-K, Figure 2A and Appendix Figure S2B in the paper.

18

PGCP, 2014

Phenotypes of DMSO control for all cell lines

AKT1/2 MEK2 AKT1

CTNNB1 wt HCT116 P2 P53

PTEN PI3KCA wt KRAS wt

BAX MEK1 HCT116 P1

Figure 6: Phenoprints of the all cell lines for a DMSO controlThis figure is the basis for Expanded View Figure EV2 in the paper.

19

PGCP, 2014

5 Display of interactions as star plots.

Similar to the phenotypes, the interaction profiles are displayed as star plots for the differentfeatures.

5.1 Scaled to the range of 0 to 1

The interactions are scaled by the median absolute deviation and linearly transformed to theinterval 0 to 1 for this display. A ring scale is used to represent the features.

#### plot interactions for 1 drug as radar plot of all celllines

#### including ftrs as segments...

if(!exists("interactions")) data(interactions, package="PGPC")

int = interactions$res

dim(int) = c(prod(dim(int)[1:2]),dim(int)[3],dim(int)[4])

dim(int)

## [1] 16464 2 20

SD = apply(int, 3, function(m) apply(m, 2, mad, na.rm=TRUE))



bin

PGCP, 2014

"nseg.0.m.majoraxis.mean",

"nseg.0.m.eccentricity.mean",

"nseg.0.s.radius.max.qt.0.05",

"cseg.act.m.majoraxis.mean" ,

"cseg.act.m.eccentricity.mean",

"cseg.dnaact.m.eccentricity.sd",

"cseg.0.s.radius.min.qt.0.05",

"cseg.act.h.idm.s2.sd",

"cseg.dnaact.h.f13.s1.mean",

"cseg.act.h.cor.s1.mean",

"cseg.dnaact.b.mean.qt.0.05",

"cseg.dnaact.h.den.s2.sd",

"cseg.dnaact.b.mad.mean",

"cseg.act.h.asm.s2.mean",

"cseg.act.h.f12.s2.sd" )

## define background colors for phenogroups

backgroundColors = c("black",

rep("grey60", 3),

rep("grey40", 4),

rep("grey20", 4),

rep("grey80", 8))

## order of mutations for plot

mutationOrder = c("HCT116 P1", "HCT116 P2", "PI3KCA wt",

"AKT1", "AKT1/2", "PTEN", "KRAS wt",

"MEK1", "MEK2", "CTNNB1 wt", "P53", "BAX")

### plot radar for each drug showing all cell lines

for(i in seq_len(nrow(drugPheno))){

drugPosition

PGCP, 2014

pAdjustedThresh = 0.01

## order features and cell lines

featureDf$feature = factor(featureDf$feature,

levels=ftrLevels,

ordered=TRUE)

featureDf$line 2) {

colors = c("red", "black")

featureDf

PGCP, 2014

stat="identity") +

coord_polar(start=-pi/nlevels(featureDf$feature)) +

ylim(c(0,maxInt*1.2)) +

geom_bar(aes(feature, value),

data = featureDf[featureDf$pAdjusted < pAdjustedThresh,],

fill="red",

stat="identity") +

geom_point(aes(feature, maxInt*1.1),

data = featureDf[featureDf$pAdjusted < pAdjustedThresh,],

pch=8,

col=2) +

theme_new + labs(title = paste0("Interactions of ", drugPheno$name[i]))

print(starplot)

}

CTNNB1 wt P53 BAX

KRAS wt MEK1 MEK2

AKT1 AKT1/2 PTEN

HCT116 P1 HCT116 P2 PI3KCA wt

0.000.250.500.75

0.000.250.500.75

0.000.250.500.75

0.000.250.500.75

feature

max

Int *

1.2

Interactions of Bendamustine

(a) Interaction profile of Bendamustine.This figure is the basis for Figure 4A in thepaper.

CTNNB1 wt P53 BAX

KRAS wt MEK1 MEK2

AKT1 AKT1/2 PTEN


0.000.250.500.75

0.000.250.500.75

0.000.250.500.75

0.000.250.500.75

feature

max

Int *

1.2

Interactions of Disulfiram

(b) Interaction profile of Disulfiram. Thisfigure is the basis for Figure 4C in the pa-per.

CTNNB1 mut CTNNB1 wt

0.0

0.3

0.6

0.9

feature

max

Int *

1.2

Interactions of Colchicine

(c) Interaction profile of Colchicine in theparental cell line and the CTNNB1 WTbackground. This figure is the basis forFigure 2B in the paper.


0.00

0.25

0.50

0.75

1.00

feature

max

Int *

1.2

Interactions of BIX01294

(d) Interaction profile of BIX01294 in theparental cell line and the CTNNB1 WTbackground. This figure is the basis forFigure 2B in the paper.

Figure 7: Interaction profiles

Here we plot the scaled profiles for all compounds with significant interactions. This wasprovided as Appendix Figure 6 and the code is not executed for the generation of this vignette.



featureDf

PGCP, 2014

function(i){

tmp=data.frame(int[i,,])

tmp$GeneID = dimnames(int)[[1]][i]

tmp$line

PGCP, 2014

#theme_new$axis.text$size = rel(0.2)

theme_new$axis.text.x = element_blank()

barColor = "lightblue"

allplots

PGCP, 2014



int = apply(interactions$res, c(1, 2, 4), mean)

for (i in 1:dim(int)[3]) {

int[,,i] = int[,,i] / MSD[i]

}

## use abs value and replace values larger than 10 by 10

direction

PGCP, 2014


## order features and cell lines

featureDf$feature = factor(featureDf$feature,

levels=ftrLevels,

ordered=TRUE)

featureDf$line 2) {

colors = c("red", "black")

featureDf

PGCP, 2014

CTNNB1 wt P53 BAX

KRAS wt MEK1 MEK2

AKT1 AKT1/2 PTEN


0102030

0102030

0102030

0102030

feature

max

Int *

1.2 direction

negative

positive

Interactions of Bendamustine

(a) Interaction profile of Bendamustine.This figure is the basis for Appendix FigureS7A in the paper.

CTNNB1 wt P53 BAX

KRAS wt MEK1 MEK2

AKT1 AKT1/2 PTEN


05

1015

05

1015

05

1015

05

1015

featurem

axIn

t * 1

.2 direction

negative

positive

Interactions of Disulfiram

(b) Interaction profile of Disulfiram. Thisfigure is the basis for Appendix Figure S7Bin the paper.


0

10

20

30

40

feature

max

Int *

1.2 direction

negative

positive

Interactions of Colchicine

(c) Interaction profile of Colchicine in theparental cell line and the CTNNB1 WTbackground. This figure is the basis forAppendix Figure S3 in the paper.


0

20

40

60

feature

max

Int *

1.2 direction

negative

positive

Interactions of BIX01294

(d) Interaction profile of BIX01294 in theparental cell line and the CTNNB1 WTbackground. This figure is the basis forAppendix Figure S3 in the paper.

Figure 8: Interaction profiles

Here we plot the profiles for all compounds with significant interactions using absolute valuesand color coding the direction of the interactions. This was provided as Appendix Figure 7and the code is not executed for the generation of this vignette.



featureDf

PGCP, 2014

directionDf

PGCP, 2014

subset=subset(featureDf, GeneID %in% id)

subset$maxInt = ifelse(max(subset$value) < maxValue,

maxValue,

max(subset$value))

starplot

PGCP, 2014

if(!exists("interactions"))


drugAnno = interactions$anno$drug

filterFDR = function(d, pAdjusted, pAdjustedThresh = 0.1){

select = pAdjusted

PGCP, 2014

col=colorRampPalette(c("darkblue", "white"))(64),

breaks = c(seq(0,0.5999,length.out=64),0.6),

margin=c(9,9))

tmp = par(mar=c(5, 4, 4, 10) + 0.1)

plot(as.dendrogram(hc), horiz=TRUE)

par(tmp)

save(celllineDist, file=file.path("result", "celllineDist.rda"))

AK

T1/

2

CT

NN

B1

wt

KR

AS

wt

P53

PT

EN

ME

K1

PI3

KC

A w

t

ME

K2

BA

X

AK

T1

HC

T11

6 P

2

HC

T11

6 P

1

AKT1/2

CTNNB1 wt

KRAS wt

P53

PTEN

MEK1

PI3KCA wt

MEK2

BAX

AKT1

HCT116 P2

HCT116 P1

0 0.1 0.3 0.5

Value

05

1015

20

Color Keyand Histogram

Cou

nt

0.30 0.25 0.20 0.15 0.10 0.05 0.00

AKT1/2

CTNNB1 wt

KRAS wt

P53

PTEN

MEK1

PI3KCA wt

MEK2

BAX

AKT1

HCT116 P2

HCT116 P1

Figure 9: Clustering of cell lines based on the raw values of the selected featuresThis figure is the basis for Appendix Figure S5B in the paper.

6.2 Clustering cell lines based on interaction terms

Here we do the same analysis as above, just the interaction scores are used this time.

PI = interactions$res

PI2 = PI ##aperm(PI, c(1,3,2,4,5))

dim(PI2) = c(prod(dim(PI2)[1:2]),dim(PI2)[3],dim(PI2)[4])

SD = apply(PI2, 3, function(m) apply(m, 2, mad, na.rm=TRUE))


## normalize by mean SD

PI = apply(interactions$res, c(1, 2, 4), mean)

for (i in 1:dim(PI)[3]) {

PI[,,i] = PI[,,i] / MSD[i]

}

dimnames(PI) = list(template = interactions$anno$drug$GeneID,

query = interactions$anno$line$mutation,

phenotype = interactions$anno$ftr)

PIfilter = filterFDR(PI, pAdjusted, pAdjustedThresh)

32

PGCP, 2014

## combine controls

PIfilter = apply(PIfilter, c(2,3),

function(x) tapply(x, dimnames(PIfilter)$template, mean))

PIfilter = PIfilter[!grepl("ctr", dimnames(PIfilter)[[1]]) |

dimnames(PIfilter)[[1]] %in% ctrlToKeep,,]

celllineCorrelation = PGPC:::getCorr(aperm(PIfilter, c(2, 1, 3)),

drugAnno)

celllineDist = PGPC:::trsf(celllineCorrelation)

hccelllineDist

PGCP, 2014

7 Compound - cell line interaction network

7.1 Extract significant interactions for visualization.

In this section we calculate some statistics from the obtained interaction data and generatea table of drug-cell line interactions for visualizing them graph network in Cytoscype [8].

We start by extracting all interactions with an adjusted p-value < 0.01.

library(PGPC)

data(interactions, package="PGPC")


d = interactions


dimnames(pAdjusted) = list(template = paste(interactions$anno$drug$GeneID),




result = NULL

for (ftr in seq_along(interactions$anno$ftr)){

pAdjusted = d$pVal[,,ftr,2]

top = pAdjusted

PGCP, 2014

selTreatment[selTreatment == 0] = dim(top)[1]

drug = d$anno$drug[selTreatment,]

line = d$anno$line[which(top) %/% dim(top)[1]+1,]

topHits = data.frame(ftr = d$anno$ftr[ftr],

uname = gsub("-", "-01-", uname),

GeneID = drug$GeneID,

r1,

r2,

rMean,

pAdjusted = pAdjusted[which(top)],

stringsAsFactors=FALSE)

topHits = cbind(topHits, line)

topHits = cbind(topHits,

drugAnno[match(topHits$GeneID, drugAnno$GeneID),

-match("GeneID",names(drugAnno))])

topHits = topHits[order(topHits$pAdjusted),]

rownames(topHits) = 1:nrow(topHits)

result=rbind(result, topHits)

}

## add controls to names

result$Name[grep("ctr", result$GeneID)]

PGCP, 2014

mutationOrder = c("HCT116 P1", "HCT116 P2", "PI3KCA wt",

"AKT1", "AKT1/2", "PTEN", "KRAS wt",

"MEK1", "MEK2", "CTNNB1 wt", "P53", "BAX")

## number of total interactions per cell line

noIntPerLine = table(result$mutation)

noIntPerLine = noIntPerLine[mutationOrder]

tmp = par(mar=c(6, 4, 4, 2) + 0.1)

mp

PGCP, 2014

inte

ract

ing

drug

s pe

r ce

ll lin

e

020

4060

8010

012

0

HCT1

16 P

1

HCT1

16 P

2

PI3K

CA w

tAK

T1

AKT1

/2

PTEN

KRAS

wt

MEK

1

MEK

2

CTNN

B1 w

tP5

3BA

X

Figure 12: Number of drugs with at least one specific interaction per cell line

par(tmp)

## percentage of all possible interactions (removing controls)

nrow(result) /

((dim(interactions$res)[1] -

sum(grepl("ctr", interactions$anno$drug$GeneID))) *prod(dim(interactions$res)[c(2,4)]))

## [1] 0.007703109

## percentage of drugs showing an interactions (removing controls)

length(unique(result$GeneID)) /

(dim(interactions$res)[1] -

sum(grepl("ctr", interactions$anno$drug$GeneID)))

## [1] 0.1512539

7.1.2 Pleiotropic degree

Here we define and calculate the pleiotropic degree. It is the number of cell lines that interactwith a given drug.

## pleiotropy degree

pleiotropicDegree = sapply(unique(result$GeneID),

function(drug)

length(unique(subset(result, GeneID==drug)$name)))

mp

PGCP, 2014

pleiotropy degree

num

ber

of d

rugs

020

4060

80

1 2 3 4 5 6 7 8 9 10 11 12

Figure 13: Pleiotropic degree per cell lineThis figure is the basis for Figure 2D in the paper.

## 90 17 14 7 7 2 6 12 9 17 4 8

For some features the pleiotropic degree shows a correlatin with the drug main effect. Thisis especially the case for cell number.

## pleiotropic degree vs. drug effect

drugEffect = apply(interactions$effect$drug, c(1,3), mean)

dimnames(drugEffect) = list(interactions$anno$drug$GeneID,

interactions$anno$ftr)

for(ftr in colnames(drugEffect)){

plot(pleiotropicDegree,

drugEffect[match(names(pleiotropicDegree), rownames(drugEffect)), ftr],

main=ftr,

ylab ="drug effect")

}

38

PGCP, 2014

2 4 6 8 10 12

1.

0

0.8

0.

6

0.4

0.

20.

0

n

pleiotropicDegree

drug

effe

ct

2 4 6 8 10 12

0.

4

0.2

0.0

0.2

0.4

0.6

cseg.act.m.majoraxis.mean

pleiotropicDegree

drug

effe

ct

2 4 6 8 10 12

1.

2

0.8

0.

40.

0

cseg.act.h.cor.s1.mean

pleiotropicDegree

drug

effe

ct

2 4 6 8 10 12

0.

2

0.1

0.0

0.1

0.2

nseg.0.m.majoraxis.mean

pleiotropicDegree

drug

effe

ct

2 4 6 8 10 12

0.

6

0.5

0.

4

0.3

0.

2

0.1

0.0

cseg.dnaact.m.eccentricity.sd

pleiotropicDegree

drug

effe

ct

2 4 6 8 10 12

0.

20.

00.

20.

40.

60.

81.

0

cseg.act.h.f12.s2.sd

pleiotropicDegree

drug

effe

ct

39

PGCP, 2014

2 4 6 8 10 12

1.

00.

00.

51.

01.

52.

02.

5

nseg.dna.h.var.s2.mean

pleiotropicDegree

drug

effe

ct

2 4 6 8 10 12

0.

050.

000.

050.

10

nseg.0.m.eccentricity.mean

pleiotropicDegree

drug

effe

ct

2 4 6 8 10 12

1.

5

1.0

0.

50.

0

cseg.0.s.radius.min.qt.0.05

pleiotropicDegree

drug

effe

ct

2 4 6 8 10 12

2

1

01

23

4

cseg.dnaact.b.mean.qt.0.05

pleiotropicDegree

drug

effe

ct

2 4 6 8 10 12

0.

15

0.05

0.00

0.05

0.10

cseg.act.m.eccentricity.mean

pleiotropicDegree

drug

effe

ct

2 4 6 8 10 12

0.

050.

000.

050.

100.

15

nseg.dna.m.eccentricity.sd

pleiotropicDegree

drug

effe

ct

40

PGCP, 2014

2 4 6 8 10 12

0.

3

0.2

0.

10.

00.

10.

20.

3

nseg.dna.h.idm.s1.sd

pleiotropicDegree

drug

effe

ct

2 4 6 8 10 12

0.

30

0.20

0.

100.

00

cseg.dnaact.h.f13.s1.mean

pleiotropicDegree

drug

effe

ct

2 4 6 8 10 12

01

23

cseg.act.h.asm.s2.mean

pleiotropicDegree

drug

effe

ct

2 4 6 8 10 12

0.

4

0.2

0.0

0.2

cseg.dnaact.h.den.s2.sd

pleiotropicDegree

drug

effe

ct

2 4 6 8 10 12

0.

10.

00.

10.

2

nseg.dna.h.cor.s2.sd

pleiotropicDegree

drug

effe

ct

41

PGCP, 2014

2 4 6 8 10 12

01

23

4

cseg.dnaact.b.mad.mean

pleiotropicDegree

drug

effe

ct

2 4 6 8 10 12

0.

8

0.6

0.

4

0.2

0.0

0.2

0.4

cseg.act.h.idm.s2.sd

pleiotropicDegree

drug

effe

ct

2 4 6 8 10 12

0.

15

0.05

0.05

0.15

nseg.0.s.radius.max.qt.0.05

pleiotropicDegree

drug

effe

ct

7.1.3 Grouping of features into feature classes.

First the number of interactions for each feature is calculated. Then the features are groupedinto feature classes and the number of interactions for each class is calculated. Finally theoverlap between feature classes is computed and represented as a venn diagram and barplot.

## no of interactions per feature

noIntPerFtr = table(result$ftr)[interactions$anno$ftr]

tmp = par(mar=c(9, 4, 4, 2) + 0.1)

mp

PGCP, 2014

significant = lapply(unique(result$ftrClass),

function(selectedFtr)

unique(subset(result, ftrClass==selectedFtr)$uname))

names(significant) = unique(result$ftrClass)

## merging the interactions for each category

plot(venn(significant))

overlap = sapply(names(significant),

function(class1){

sapply(names(significant), function(class2){

length(intersect(significant[[class1]],

significant[[class2]]))

})

})

barplot(overlap,

beside=TRUE,

legend=names(significant),

args.legend=list(x="topleft"))

tota

l int

erac

tions

per

feat

ure

050

150

250

n

cseg

.act.

m.m

ajora

xis.m

ean

cseg

.act.

h.co

r.s1.

mea

n

nseg

.0.m

.majo

raxis

.mea

n

cseg

.dna

act.m

.ecc

entri

city.s

d

cseg

.act.

h.f1

2.s2

.sd

nseg

.dna

.h.va

r.s2.

mea

n

nseg

.0.m

.ecc

entri

city.m

ean

cseg

.0.s.

radiu

s.min.

qt.0

.05

cseg

.dna

act.b

.mea

n.qt

.0.0

5

cseg

.act.

m.e

ccen

tricit

y.mea

n

nseg

.dna

.m.e

ccen

tricit

y.sd

nseg

.dna

.h.id

m.s1

.sd

cseg

.dna

act.h

.f13.

s1.m

ean

cseg

.act.

h.as

m.s2

.mea

n

cseg

.dna

act.h

.den

.s2.sd

nseg

.dna

.h.co

r.s2.

sd

cseg

.dna

act.b

.mad

.mea

n

cseg

.act.

h.idm

.s2.sd

nseg

.0.s.

radiu

s.max

.qt.0

.05

(a) Total interactions per feature.

cell number

cell shape

cellular texture

nuclear shapenuclear texture

0

12

79

20448

0

2

10

1715

6

74

36

66

0

0

0

01

1

186

17

980

0

0

5

58

6

(b) Overlap of interactions in the 5 phe-notypic classes. This figure is the basis forFigure 2C in the paper.

cell number cell shape cellular texture nuclear shape nuclear texture

cell numbercell shapecellular texturenuclear shapenuclear texture

010

020

030

040

050

0

(c) Overlap of interactions in the 5 pheno-typic classes shown as bar plots.

Figure 14: Distributions and overlap of interactions per feature

7.1.4 Interaction map export for Cytoscape

To obtain a interaction map for display in Cytoscape we remove all drugs that only show aninteraction for one feature in a given cell line. Also ambigous drugs showing interactions with3 or more cell lines are removed.

## remove low confidence interactions

result 1])

subset(tmp, GeneID %in% selectedDrug)

}))

43

PGCP, 2014

## remove ambiguous interactions

noLinesPerDrug = sapply(unique(result$GeneID),

function(drug)

length(unique(subset(result, GeneID==drug)$name)))

unambigDrugs = names(noLinesPerDrug[noLinesPerDrug i))

The results are saved and used as input to Cytoscape for visualization. The interactions fordifferent features are combined into Phenogroups and also merged completely. In the lattercase number of feature classes is calculated. We used the results with phenogroups as inputto Cytoscape.

write.table(result, file=file.path("result", "cytoscapeExportFiltered.txt"),

sep="\t",

row.names=FALSE)

############################

## transform to pheno groups

44

PGCP, 2014

############################

result$ftr = PGPC:::hrClass(result$ftr)

columnsToRemove = c("r1", "r2", "rMean", "pAdjusted")

result = unique(result[,-match(columnsToRemove, names(result))])

write.table(result,

file=file.path("result", "cytoscapeExportFilteredPhenoGroups.txt"),

sep="\t",

row.names=FALSE)

#########################

## combine features

#########################

result = do.call(rbind,

lapply(unique(result$uname),

function(u){

tmp = subset(result, uname==u)

ftr = tmp$ftr

tmp = unique(tmp[,-match(c("ftr", "ftrClass"),

names(tmp))])

tmp$cellnumber =

ifelse("cell number" %in% ftr, 1, 0)

tmp$cellshape =

ifelse("cell shape" %in% ftr, 1, 0)

tmp$celltexture =

ifelse("cellular texture" %in% ftr, 1, 0)

tmp$nuclearshape =

ifelse("nuclear shape" %in% ftr, 1, 0)

tmp$nucleartexture =

ifelse("nuclear texture" %in% ftr, 1, 0)

tmp$noFtrClasses = length(ftr)

tmp

}))

write.table(result, file=file.path("result",

"cytoscapeExportFilteredFtrsCombined.txt"),

sep="\t",

row.names=FALSE)

8 Heat maps of interaction profiles

In this section we display the interaction profiles as heatmaps. In order to visualize theinteractions of all features in one plot the interaction terms are scaled by the median andmedian absolute deviation for each feature.

if(!exists("interactions")){


}

PI = interactions$res

45

PGCP, 2014

PI2 = PI ##aperm(PI, c(1,3,2,4,5))

dim(PI2) = c(prod(dim(PI2)[1:2]),dim(PI2)[3],dim(PI2)[4])

SD = apply(PI2, 3, function(m) apply(m, 2, mad, na.rm=TRUE))


## normalize by mean SD

PI = apply(interactions$res, c(1, 2, 4), mean)

for (i in 1:dim(PI)[3]) {

PI[,,i] = PI[,,i] / MSD[i]

}

dimnames(PI) = list(template = interactions$anno$drug$GeneID,



myColors = c(`Blue`="cornflowerblue",

`Black`="#000000",

`Yellow`="yellow")

colBY = colorRampPalette(myColors)(513)

cuts = c(-Inf,

seq(-6, -2, length.out=(length(colBY)-3)/2),

0.0,

seq(2, 6, length.out=(length(colBY)-3)/2),

+Inf)

ppiw = .25

ppih = 1.4

fac = 2.2

d = dim(PI)

ordTempl = PGPC:::orderDim(PI, 1)

ordQuery = PGPC:::orderDim(PI, 2)

ordFeat = PGPC:::orderDim(PI, 3)

PGPC:::myHeatmap(PI[ordTempl, ordQuery, ordFeat],

cuts=cuts,

fontsize=10,

col=colBY)

Next we focus on the interactions with a FDR below 0.01. Also all controls are removed,except Paclitaxel, U0126 and Vinblastin. The mean values accross the control wells arecalculated for the selected controls.

filterFDR = function(d, pAdjusted, pAdjustedThresh = 0.1){

select = pAdjusted

PGCP, 2014

cse

g.0.

s.ra

dius

.min

.qt.0

.05

nse

g.dn

a.h.

idm

.s1.

sd

cse

g.dn

aact

.m.e

ccen

tric

ity.s

d

cse

g.ac

t.m.e

ccen

tric

ity.m

ean

nse

g.0.

m.e

ccen

tric

ity.m

ean

nse

g.dn

a.h.

cor.s

2.sd

cse

g.ac

t.h.c

or.s

1.m

ean

cse

g.ac

t.h.id

m.s

2.sd

nse

g.0.

s.ra

dius

.max

.qt.0

.05

cse

g.ac

t.m.m

ajor

axis

.mea

n

nse

g.0.

m.m

ajor

axis

.mea

n

cse

g.dn

aact

.b.m

ean.

qt.0

.05

cse

g.dn

aact

.h.f1

3.s1

.mea

n

nse

g.dn

a.m

.ecc

entr

icity

.sd

nse

g.dn

a.h.

var.s

2.m

ean

cse

g.dn

aact

.b.m

ad.m

ean

n cse

g.dn

aact

.h.d

en.s

2.sd

cse

g.ac

t.h.f1

2.s2

.sd

cse

g.ac

t.h.a

sm.s

2.m

ean

Figure 15: Heatmap of interaction profiles for all drugs

pAdjustedThreshold = 0.01


PIfilter = filterFDR(PI,

pAdjusted,

pAdjustedThresh = pAdjustedThreshold)

PIfilter = apply(PIfilter, c(2,3),

function(x) tapply(x, dimnames(PIfilter)$template, mean))

ctrlToKeep = c("ctrl Paclitaxel", "ctrl U0126", "ctrl Vinblastin")

### some other contrls:

# ctrlToKeep = c("ctrl Paclitaxel", "ctrl U0126", "ctrl Vinblastin", "ctrl IWP", "ctrl DAPT")

PIfilter = PIfilter[!grepl("ctr", dimnames(PIfilter)[[1]]) |

dimnames(PIfilter)[[1]] %in% ctrlToKeep,,]

ordTempl = PGPC:::orderDim(PIfilter, 1)

ordQuery = PGPC:::orderDim(PIfilter, 2)

ordFeat = PGPC:::orderDim(PIfilter, 3)

PGPC:::myHeatmap(PIfilter[ordTempl,ordQuery,ordFeat],

cuts=cuts,

fontsize=10,

col=colBY)


subset = drugAnno[drugAnno$compoundID %in% dimnames(PIfilter)[[1]] &

!grepl("ctr", drugAnno$GeneID),]

write.table(subset[, c("Name", "GeneID", "Selectivity", "Selectivity_updated")],

file=file.path("result", "annotation_selected_compounds.txt"),

sep="\t",

47

PGCP, 2014

nse

g.dn

a.h.

var.s

2.m

ean

cse

g.dn

aact

.b.m

ad.m

ean

nse

g.0.

m.e

ccen

tric

ity.m

ean

nse

g.dn

a.h.

cor.s

2.sd

cse

g.0.

s.ra

dius

.min

.qt.0

.05

cse

g.dn

aact

.b.m

ean.

qt.0

.05

nse

g.dn

a.m

.ecc

entr

icity

.sd

nse

g.dn

a.h.

idm

.s1.

sd

cse

g.dn

aact

.h.d

en.s

2.sd

cse

g.ac

t.h.f1

2.s2

.sd

cse

g.ac

t.h.a

sm.s

2.m

ean

n cse

g.dn

aact

.h.f1

3.s1

.mea

n

cse

g.dn

aact

.m.e

ccen

tric

ity.s

d

cse

g.ac

t.m.e

ccen

tric

ity.m

ean

cse

g.ac

t.h.c

or.s

1.m

ean

cse

g.ac

t.h.id

m.s

2.sd

nse

g.0.

s.ra

dius

.max

.qt.0

.05

cse

g.ac

t.m.m

ajor

axis

.mea

n

nse

g.0.

m.m

ajor

axis

.mea

n

Figure 16: Heatmap of interaction profiles for drugs that show at least one specific interaction

quote=FALSE,

row.names=FALSE)

9 Clustering of interaction profiles

9.1 Clustering of interaction profiles using the filtered data

To investigate drug similarity and cluster drugs with similar function or targets, we use theinteractions profiles to calculate a distance between the drugs. The metric that we use is1-cor(x,y), where x and y represent the interaction profiles for two drugs.

PIdist = PGPC:::getDist(PIfilter, drugAnno=drugAnno)

hcInt

PGCP, 2014

7999

0 P

KC

7935

6 P

KC

7942

5 F

FA1/

GP

R40

7919

1 C

a2+

8013

6 C

a2+

7998

2 N

a+/K

+ P

ump

7947

1 S

RC

7921

5 To

po I

7965

3 D

NA

sy

nthe

sis

7927

3 R

OS

7961

3 E

stro

gen

7989

1 C

asei

nKin

ase

7997

3 C

a2+

8007

6 N

orep

inep

hrin

e

7992

2 S

TAT

3

7905

7 IK

B

alph

a

7989

2 IK

B

alph

a

7990

7 N

OS

7924

1 eN

OS

7998

6 m

itoch

ondr

ial e

lect

ron

tran

spor

t

7903

8 G

olgi

app

arat

us

7922

9 N

a+/K

+ P

ump

7981

7 N

a+/K

+ P

ump

7940

5 C

YP

1A1

/ Top

oII

7984

4 M

AO

7950

3 C

dc25

7933

4 tr

ansl

atio

n

7916

5 C

DK

7947

4 D

NA

Met

abol

ism

8008

7 H

ista

min

e re

cept

or

7959

1 ap

opto

sis

7974

0 P

roto

noph

ore

7992

6 P

KC

7941

3 G

2M

che

ckpo

int

7904

9 H

SV

1

7908

6 T

an

alog

7955

9 ra

c1

7987

2 N

a+

chan

nel

7990

6 P

KC

7952

0 do

pam

ine

rece

ptor

7959

8 se

roto

nin

rece

ptor

7959

9

7959

5 ac

etyl

chol

ine

rece

ptor

7960

3 C

B

7960

4 C

alpa

in

7952

4 B

TK

7910

6 H

ista

min

e re

cept

or

7895

9 iN

OS

7919

4 iN

OS

7888

4 Le

ucin

e am

inop

eptid

ase

7896

3 N

MD

A

7896

4 K

+ c

hann

el

7887

6 M

AO

7887

5 Ty

rosi

ne h

ydro

xyla

se

7895

5 A

A

anal

og

7974

5 P

P1B

/ S

HP

TP

1

7974

6 N

MD

A

7982

5 ad

reno

cept

or

7975

1 Li

poxy

gena

se

7982

7 gu

anyl

yl c

ycla

se

7919

8 ad

enos

ine

rece

ptor

7974

7 B

utyr

ylch

olin

este

rase

7983

5 do

pam

ine

rece

ptor

7901

4 C

asei

nKin

ase

7922

5 C

asei

nKin

ase

7992

3 IR

AK

7959

6 do

pam

ine

rece

ptor

7959

7 A

lcoh

ol D

ehyd

roge

nase

7911

0 E

GF

R

7904

7 p3

8/M

PK

KK

7927

5 p3

8/M

PK

KK

7950

9 A

DK

7920

0

7951

2 se

roto

nin

rece

ptor

7960

0 se

roto

nin

rece

ptor

7935

5 G

SK

3

7928

3

7942

9 do

pam

ine

rece

ptor

7901

6 D

NA

Met

abol

ism

7919

0 D

NA

Met

abol

ism

7981

2 C

DK

7946

2 do

pam

ine

rece

ptor

7902

8 To

po II

7929

4 To

po II

7889

4 fo

late

7890

8 D

ihyd

rofo

late

red

ucta

se

7890

9 D

NA

met

hyltr

ansf

eras

e

7985

9 M

etal

lopr

otea

se

7894

9 do

pam

ine

rece

ptor

7904

5 do

pam

ine

rece

ptor

7920

7 R

OC

K

7980

0 se

roto

nin

rece

ptor

7992

0

8008

9 R

OS

7891

9 N

a+/H

+ A

ntip

orte

r

7897

8 N

a+/H

+ A

ntip

orte

r

7966

4 se

roto

nin

rece

ptor

7980

4 do

pam

ine

rece

ptor

7909

9 ad

reno

cept

or

7958

2 C

a2+

ch

anne

l

7955

8 do

pam

ine

7922

1 ad

reno

cept

or

7908

9 ad

reno

cept

or

7962

6 ta

chyk

inin

rec

epto

r

7960

7 Tu

bulin

7961

5 Tu

bulin

ctrl

Vin

blas

tin T

ubul

in

8007

5 Tu

bulin

ctrl

Pac

litax

el T

ubul

in

8008

2 Tu

bulin

8010

1 Tu

bulin

7918

4 Tu

bulin

7980

2 Tu

bulin

7968

9 H

NE

7907

4 D

NA

_int

erca

latio

n

7910

4 In

terc

alat

or

7911

1 T

RPA

1

7981

9 S

erot

onin

8004

4 p5

3

7944

4 al

kyla

ting

7949

7 D

NA

_alk

ylat

ion

8012

8 P

DG

FR

7979

9 N

MD

A

7956

7 ac

etyl

chol

ine

rece

ptor

7963

9 ac

etyl

chol

ine

rece

ptor

7946

7 M

AO

8011

5 C

aspa

se 3

7900

3 do

pam

ine

rece

ptor

7909

0 do

pam

ine

rece

ptor

7996

8 To

po II

8002

1 E

GF

R

7903

3 G

luco

cort

icoi

d

7908

7 G

luco

cort

icoi

d

7972

5 H

ista

min

e re

cept

or

7953

5 N

MD

A

7906

8 ac

etyl

chol

ine

rece

ptor

7913

4 H

ista

min

e re

cept

or

7959

4

7977

3 H

ista

min

e re

cept

or

8003

0 se

roto

nin

7943

6

7922

0 do

pam

ine

rece

ptor

7926

6 do

pam

ine

rece

ptor

7930

1 do

pam

ine

rece

ptor

7964

7 G

SK

3

7912

2 P

P2A

7919

2 P

P2A

7983

7 M

MP

8010

4 G

uany

lyl c

ycla

se

7962

8 R

as, R

ho

8008

3 ta

chyk

inin

rec

epto

r

7930

4 P

IPLC

7924

7

8000

2 hi

ston

e m

ethy

l tra

nsfe

rase

7938

6 C

arbo

xype

ptid

ase

B

7906

4 P

LK

7914

3 N

FkB

8003

2 E

GF

R

7916

4 C

hym

otry

psin

NF

KB

8003

8 A

lcoh

ol D

ehyd

roge

nase

7994

3 do

pam

ine

rece

ptor

7932

4 Ic

k

7902

0 C

DK

7911

6 C

alci

neur

in p

hosp

hata

se

7947

7 H

istin

ol D

ehyd

roge

nase

7941

0 D

NA

sy

nthe

sis

7941

1 D

NA

sy

nthe

sis

7939

4 Ty

rosi

ne k

inas

e

8003

9 P

DE

7899

2 ad

enos

ine

rece

ptor

7954

9 ad

enos

ine

rece

ptor

7991

0

7916

8 ad

enos

ine

rece

ptor

7922

7 P

2 re

cept

or

7934

4 ad

enos

ine

rece

ptor

7936

1 A

deny

late

cyc

lase

8012

1 A

DK

7966

7 D

NA

_int

erca

latio

n

8010

2 A

Ch

stor

age

7914

4 A

lani

ne a

min

otra

nsfe

rase

7915

4 D

NA

st

rand

bre

ak

7987

9 N

a+

chan

nel

7890

1 P

urin

e sy

nthe

sis

7899

0 A

dren

ocep

tor

7904

8 C

ortis

ol

7972

1 P

KC

7990

2 M

EK

8009

1 M

EK

ctrl

U01

26 M

EK

8012

2 P

I3K

7921

2 K

+

chan

nel

8013

0 M

NK

1

7996

3 do

pam

ine

rece

ptor

7998

4 V

EG

FR

PT

K

7993

7 IM

P d

ehyd

roge

nase

8008

6 C

DK

79990 DLStearoylcarnitine chloride

79356 Staurosporine aglycone

79425 GW9508

79191 Calcimycin

80136 Thapsigargin

79982 Sanguinarine chloride

79471 MNS

79215 (S)(+)Camptothecin

79653 Mitoxantrone

79273 2,3Dimethoxy1,4naphthoquinone

79613 2methoxyestradiol

79891 IC 261

79973 SKF 96365

80076 Tomoxetine

79922 Stattic

79057 Bay 117085

79892 Bay 117082

79907 Ammonium pyrrolidinedithiocarbamate

79241 Diphenyleneiodonium chloride

79986 Rotenone

79038 Brefeldin A from Penicillium brefeldianum

79229 Dihydroouabain

79817 Ouabain

79405 Ellipticine

79844 Quinacrine dihydrochloride

79503 NSC 95397

79334 Emetine dihydrochloride hydrate

79165 CGP74514A hydrochloride

79474 Idarubicin

80087 Terfenadine

79591 betaLapachone

79740 Niclosamide

79926 Rottlerin

79413 Ganciclovir

79049 (E)5(2Bromovinyl)2'deoxyuridine

79086 5Bromo2'deoxyuridine

79559 Aurothioglucose

79872 Phenamil methanesulfonate

79906 Phorbol 12myristate 13acetate

79520 JL18

79598 pMPPI hydrochloride

79599 Molsidomine

79595 TMPH hydrochloride

79603 GW405833 hydrochloride

79604 MDL 28170

79524 LFMA13

79106 (+)Brompheniramine maleate

78959 (+)AMT hydrochloride

79194 LCanavanine sulfate

78884 Actinonin

78963 cisAzetidine2,4dicarboxylic acid

78964 2,3Butanedione monoxime

78876 6Methoxy1,2,3,4tetrahydro9Hpyrido[3,4b] indole

78875 DLalphaMethylptyrosine

78955 NAcetylLCysteine

79745 Me3,4dephostatin

79746 (+)MK801 hydrogen maleate

79825 Bisoprolol hemifumarate salt

79751 Nordihydroguaiaretic acid from Larrea divaricata (creosote bush)

79827 ODQ

79198 CGS15943

79747 Ethopropazine hydrochloride

79835 Promazine hydrochloride

79014 TBBz

79225 CK2 Inhibitor 2

79923 IRAK1/4 Inhibitor I

79596 L750,667 trihydrochloride

79597 4Methylpyrazole hydrochloride

79110 DAPH

79047 SB 202190

79275 PD 169316

79509 ABT702 dihydrochloride

79200 Debrisoquin sulfate

79512 R(+)8HydroxyDPAT hydrobromide

79600 Metergoline

79355 SB 415286

79283 Anisotropine methyl bromide

79429 Fluphenazine dihydrochloride

79016 Ancitabine hydrochloride

79190 Cytosine1betaDarabinofuranoside hydrochloride

79812 NU2058

79462 BNTX maleate salt hydrate

79028 Amsacrine hydrochloride

79294 Etoposide

78894 Methotrexate hydrate

78908 Aminopterin

78909 5azacytidine

79859 1,10Phenanthroline monohydrate

78949 (+)Butaclamol hydrochloride

79045 (+)Bromocriptine methanesulfonate

79207 Y27632 dihydrochloride

79800 LP44

79920 Auranofin

80089 U74389G maleate

78919 5(NEthylNisopropyl)amiloride

78978 5(N,Nhexamethylene)amiloride

79664 GR 127935 hydrochloride hydrate

79804 Perphenazine

79099 Benoxathian hydrochloride

79582 Loperamide hydrochloride

79558 Indatraline hydrochloride

79221 Carvedilol

79089 Bromoacetyl alprenolol menthane

79626 L703,606 oxalate salt hydrate

79607 Nocodazole

79615 CHM1 hydrate

ctrl Vinblastin

80075 Taxol

ctrl Paclitaxel

80082 Vincristine sulfate

80101 Vinblastine sulfate salt

79184 Colchicine

79802 Podophyllotoxin

79689 Sivelestat sodium salt hydrate

79074 CB 1954

79104 Carboplatin

79111 Supercinnamaldehyde

79819 Parthenolide

80044 Pifithrinmu

79444 Iodoacetamide

79497 Bendamustine hydrochloride

80128 Tyrphostin A9

79799 Pentamidine isethionate

79567 Ivermectin

79639 MG 624

79467 Hydralazine hydrochloride

80115 PAC1

79003 A77636 hydrochloride

79090 R(+)6BromoAPB hydrobromide

79968 Sobuzoxane

80021 Tyrphostin AG 494

79033 Beclomethasone

79087 Betamethasone

79725 Methapyrilene hydrochloride

79535 Ifenprodil tartrate

79068 Benztropine mesylate

79134 Clemastine fumarate

79594 Loxapine succinate

79773 Promethazine hydrochloride

80030 Trimipramine maleate

79436 Paliperidone

79220 Droperidol

79266 (+)ChloroAPB hydrobromide

79301 Domperidone

79647 BIO

79122 Cantharidin

79192 Cantharidic Acid

79837 ARP 101

80104 YC1

79628 Mevastatin

80083 WIN 62,577

79304 ET18OCH3

79247 Capsazepine

80002 BIX 01294 trihydrochloride hydrate

79386 EGTA

79064 BTO1

79143 Caffeic acid phenethyl ester

80032 Tyrphostin AG 555

79164 ZLPhe chloromethyl ketone

80038 Tetraethylthiuram disulfide

79943 Spiperone hydrochloride

79324 7Cyclopentyl5(4phenoxy)phenyl7Hpyrrolo[2,3d]pyrimidin4ylamine

79020 Indirubin3'oxime

79116 Cyclosporin A

79477 4Imidazolemethanol hydrochloride

79410 5Fluorouracil

79411 5fluoro5'deoxyuridine

79394 Genistein

80039 Trequinsin hydrochloride

78992 N62(4Aminophenyl)ethyladenosine

79549 IBMECA

79910 Enalaprilat dihydrate

79168 2Chloroadenosine

79227 2Chloroadenosine triphosphate tetrasodium

79344 5'NEthylcarboxamidoadenosine

79361 Forskolin

80121 A134974 dihydrochloride hydrate

79667 Melphalan

80102 (+)Vesamicol hydrochloride

79144 betaChloroLalanine hydrochloride

79154 Pyrocatechol

79879 Prilocaine hydrochloride

78901 Azathioprine

78990 Amoxapine

79048 Budesonide

79721 Gossypol

79902 PD 98,059

80091 U0126

ctrl U0126

80122 Wortmannin from Penicillium funiculosum

79212 Dequalinium chloride hydrate

80130 CGP 57380

79963 ()Sulpiride

79984 SU 5416

79937 Ribavirin

80086 NU6027

0 0.1 0.2 0.3 0.4 0.5 0.6Value

010

000

2000

030

000

Color Keyand Histogram

Cou

nt

Figure 17: Clustering of interaction profiles for drugs that show at least one interaction

9.1.1 Reordered dendrogram

Due to the ambiguity of the cluster tree and for visualization purposes we rearange twoclusters. Clusters are colored by cutting the cluster tree at a height of 0.6. For visability weonly color clusters that contain at least 3 drugs.

## reorder dendrogram

wts = rep(0, dim(PIdist)[1])

## reorder bio cluster

inbetween = c(146, 187, 66, 170, 73, 121, 180)

wts[inbetween] = 1000

drugIds = sapply(strsplit(rownames(PIdist), " "), "[", 1)

## reorder Etoposide cluster

wts[match("79462", drugIds)] = 10

## reorder calcimycin cluster



hcInt = reorder(hcInt, wts)

cluster = cutree(as.hclust(hcInt), h=0.6)

49

PGCP, 2014

## make color table

inCl

PGCP, 2014

## read structure file

sdfset

PGCP, 2014

## annotate distance matrix with GeneIDs and drugnames

dimnames(distmat)

PGCP, 2014

heatmap.2(1-distmat,

Rowv=hcInt,

Colv=hcInt,

col=colorRampPalette(c( "white","antiquewhite", "darkorange2"))(64),

density.info="none",

trace="none",

main="Structural similarity orderd by interaction similarity")

JL

18p

MP

PI h

ydro

chlo

ride

Mol

sido

min

eT

MP

H h

ydro

chlo

ride

GW

4058

33 h

ydro

chlo

ride

MD

L 28

170

LFM

A

13(+

)B

rom

phen

iram

ine

mal

eate

(+

)A

MT

hyd

roch

lorid

eL

Can

avan

ine

sulfa

teA

ctin

onin

cis

Aze

tidin

e2,

4di

carb

oxyl

ic a

cid

2,3

But

aned

ione

mon

oxim

e6

Met

hoxy

1,

2,3,

4te

trah

ydro

9H

py

rido[

3,4b

] ind

ole

DL

alph

aM

ethy

lp

tyro

sine

N

Ace

tyl

LC

yste

ine

Me

3,4

deph

osta

tin(+

)M

K

801

hydr

ogen

mal

eate

Bis

opro

lol h

emifu

mar

ate

salt

Nor

dihy

drog

uaia

retic

aci

d fr

om L

arre

a di

varic

ata

(cre

osot

e bu

sh)

OD

QC

GS

15

943

Eth

opro

pazi

ne h

ydro

chlo

ride

Pro

maz

ine

hydr

ochl

orid

eT

BB

zC

K2

Inhi

bito

r 2

IRA

K

1/4

Inhi

bito

r I

L75

0,66

7 tr

ihyd

roch

lorid

e4

Met

hylp

yraz

ole

hydr

ochl

orid

eD

AP

HS

B 2

0219

0P

D 1

6931

6A

BT

70

2 di

hydr

ochl

orid

eD

ebris

oqui

n su

lfate

R

(+)

8H

ydro

xy

DPA

T h

ydro

brom

ide

Met

ergo

line

SB

415

286

Ani

sotr

opin

e m

ethy

l bro

mid

eF

luph

enaz

ine

dihy

droc

hlor

ide

Noc

odaz

ole

CH

M

1 hy

drat

ect

rl V

inbl

astin

Taxo

lct

rl P

aclit

axel

Vin

cris

tine

sulfa

teV

inbl

astin

e su

lfate

sal

tC

olch

icin

eP

odop

hyllo

toxi

nS

ivel

esta

t sod

ium

sal

t hyd

rate

CB

195

4C

arbo

plat

inS

uper

cinn

amal

dehy

deP

arth

enol

ide

Pifi

thrin

m

uIo

doac

etam

ide

Ben

dam

ustin

e hy

droc

hlor

ide

Tyrp

host

in A

9P

enta

mid

ine

iset

hion

ate

Iver

mec

tinM

G 6

24H

ydra

lazi

ne h

ydro

chlo

ride

PAC

1

A

7763

6 hy

droc

hlor

ide

R(+

)6

Bro

mo

AP

B h

ydro

brom

ide

Sob

uzox

ane

Tyrp

host

in A

G 4

94(+

)B

utac

lam

ol h

ydro

chlo

ride

(+)

Bro

moc

riptin

e m

etha

nesu

lfona

teY

27

632

dihy

droc

hlor

ide

LP44

Aur

anof

inU

74

389G

mal

eate

5(N

E

thyl

N

is

opro

pyl)a

milo

ride

5(N

,N

hexa

met

hyle

ne)a

milo

ride

GR

127

935

hydr

ochl

orid

e hy

drat

eP

erph

enaz

ine

Ben

oxat

hian

hyd

roch

lorid

eLo

pera

mid

e hy

droc

hlor

ide

Inda

tral

ine

hydr

ochl

orid

eC

arve

dilo

lB

rom

oace

tyl a

lpre

nolo

l men

than

eL

703,

606

oxal

ate

salt

hydr

ate

Met

hotr

exat

e hy

drat

eA

min

opte

rin5

azac

ytid

ine

1,10

P

hena

nthr

olin

e m

onoh

ydra

teA

ncita

bine

hyd

roch

lorid

eC

ytos

ine

1be

ta

D

arab

inof

uran

osid

e hy

droc

hlor

ide

NU

2058

Am

sacr

ine

hydr

ochl

orid

eE

topo

side

BN

TX

mal

eate

sal

t hyd

rate

Gan

cicl

ovir

(E)

5(2

B

rom

ovin

yl)

2'

deox

yurid

ine

5B

rom

o2'

de

oxyu

ridin

eA

urot

hiog

luco

seP

hena

mil

met

hane

sulfo

nate

Pho

rbol

12

myr

ista

te 1

3ac

etat

eD

LS

tear

oylc

arni

tine

chlo

ride

Sta

uros

porin

e ag

lyco

neG

W95

082,

3D

imet

hoxy

1,

4na

phth

oqui

none

2m

etho

xyes

trad

iol

IC 2

61S

KF

963

65To

mox

etin

eS

tatti

cB

ay 1

170

85B

ay 1

170

82A

mm

oniu

m p

yrro

lidin

edith

ioca

rbam

ate

Dip

heny

lene

iodo

nium

chl

orid

eR

oten

one

Bre

feld

in A

from

Pen

icill

ium

bre

feld

ianu

mD

ihyd

roou

abai

nO

uaba

inE

llipt

icin

eQ

uina

crin

e di

hydr

ochl

orid

eN

SC

953

97E

met

ine

dihy

droc

hlor

ide

hydr

ate

CG

P

7451

4A h

ydro

chlo

ride

Idar

ubic

inTe

rfen

adin

ebe

ta

Lapa

chon

eN

iclo

sam

ide

Rot

tlerin

Cal

cim

ycin

Tha

psig

argi

n(S

)(+

)C

ampt

othe

cin

Mito

xant

rone

MN

SS

angu

inar

ine

chlo

ride

4Im

idaz

olem

etha

nol h

ydro

chlo

ride

5F

luor

oura

cil

5flu

oro

5'

deox

yurid

ine

Gen

iste

inTr

equi

nsin

hyd

roch

lorid

eN

62

(4

Am

inop

heny

l)eth

ylad

enos

ine

IB

ME

CA

Ena

lapr

ilat d

ihyd

rate

2C

hlor

oade

nosi

ne2

Chl

oroa

deno

sine

trip

hosp

hate

tetr

asod

ium

5'

N

Eth

ylca

rbox

amid

oade

nosi

neF

orsk

olin

A

1349

74 d

ihyd

roch

lorid

e hy

drat

eM

elph

alan

(+

)V

esam

icol

hyd

roch

lorid

ebe

ta

Chl

oro

Lal

anin

e hy

droc

hlor

ide

Pyr

ocat

echo

lP

riloc

aine

hyd

roch

lorid

eA

zath

iopr

ine

Am

oxap

ine

Bud

eson

ide

Gos

sypo

lP

D 9

8,05

9U

0126

ctrl

U01

26W

ortm

anni

n fr

om P

enic

illiu

m fu

nicu

losu

mD

equa

liniu

m c

hlor

ide

hydr

ate

CG

P 5

7380

()

Sul

pirid

eS

U 5

416

Rib

aviri

nN

U60

27B

eclo

met

haso

neB

etam

etha

sone

Met

hapy

rilen

e hy

droc

hlor

ide

Ifenp

rodi

l tar

trat

eB

enzt

ropi

ne m

esyl

ate

Cle

mas

tine

fum

arat

eLo

xapi

ne s

ucci

nate

Pro

met

hazi

ne h

ydro

chlo

ride

Trim

ipra

min

e m

alea

teP

alip

erid

one

Dro

perid

ol(+

)

Chl

oro

AP

B h

ydro

brom

ide

Dom

perid

one

EG

TAB

TO

1C

affe

ic a

cid

phen

ethy

l est

erTy

rpho

stin

AG

555

Z

LP

he c

hlor

omet

hyl k

eton

eTe

trae

thyl

thiu

ram

dis

ulfid

eS

pipe

rone

hyd

roch

lorid

e7

Cyc

lope

ntyl

5

(4

phen

oxy)

phen

yl

7H

pyrr

olo[

2,3

d]py

rimid

A chemical-genetic interaction map of small molecules using … · A chemical-genetic interaction...

Documents

Transcript of A chemical-genetic interaction map of small molecules using … · A chemical-genetic interaction...