Analysis of Globally-Coherent Data Sets

a two-day course for GlaxoSmithKline

Yinyin Yuan and Mauro Castro ∗

http://www.markowetzlab.org/GCDcourse/

florian.markowetz@cancer.org.uk

Stevenage, 28 - 29th Oct 2010

Abstract

This tutorial refers to the practical session on day one of the course Analysis of Globally-Coherent Data Sets.The topic is Molecular Data Integration with R.

Part I

1 Introduction

Globally-coherent datasets (GCDs) contain (at least) three levels of information (i) genome-wide DNA variation,(ii) an intermediate trait, as well as (iii) a (clinical) phenotype. Intermediate traits are typically gene expression,but may also include proteomic, metabolomic, and other molecular data. These data sets make it possible todissect how a genomic perturbation (e.g. a somatic copy-number alteration) leads to changes in cellular networksand pathways, which then shape the phenotype (e.g. how aggressive a type of cancer is). Examples of GCDsare the The Cancer Genome Atlas, the International Cancer Genome Consortium, the METABRIC project atthe CRI in Cambridge, as well as the data collected by SAGE Bionetworks.

The challenge of GCDs is to gain a global understanding of how the different layers of information areconnected. While effective statistical methods can provide a system-level view of the genomic landscape,network visualization methods are key to visualizing complex data sets. Together these tools can help to ‘boildown’ the complex multi-layered GCDs into testable hypotheses for in-depth follow-up studies.

Here we exemplify this type of data analysis with a breast cancer dataset comprising CNA, mRNA andpatient information [1].

2 Main setup

Load the package ‘Dance’ and ‘lol’ and the datasets that will be used in the Part I of this tutorial. To do this,first set the filepath of the working directory to the folder called ‘chin07’ (e.g. setwd(‘./chin07/’) ). Please,check this folder, it must contain two other folders called ‘dataDir’ and ‘resDir’: the first one contains all datayou will need and the second is just for results. Also, the root of this directory must have the folder called‘Package’ which contains the main tools to run this tutorial. You can also check, at the last page, the completeversion information about R, including loaded packages and attachments. Then just follow the commentedworkflow below.

Data input

Install the main packages and set the data folders.

> install.packages('../Package/lol_0.5.tar.gz', repos=NULL, type='source')

> install.packages('../Package/HTSanalyzeR_1.2.5.tar.gz', repos=NULL, type='source')

> install.packages('../Package/IGIR_0.1.zip', repos=NULL, type='source')

> install.packages('../Package/Dance_0.9.tar.gz', repos=NULL, type='source')

∗Cambridge Research Institute - CRUK, Li Ka Shing Centre, Robinson Way Cambridge, CB2 0RE, UK.

1

> library(Dance)

> library(lol)

> dataDir resDir ER er names(er) ge.data ge.h idx ge.data ge.h cn.data cn.h idx cn.data cn.h rownames(cn.data) commonCol commonCol cn.data ge.data er ge.data ge.h cn.data cn.h

Now run the EM algorithm calling discrete copy number:

# minls threshold nSig

First set the color definition (default colors are not pretty!).

> load('./dataDir/pam50colors.rdata')

> load('./dataDir/iclustercolors.rdata')

> pam50clusters pam50Subtype.color color.code color.code data fileName breaks pdf(paste('./resDir/', fileName,'.pdf', sep=''))

> heatmap(as.matrix(data),

+ distfun=function(x) dist(x,method='manhattan'),

+ hclustfun=function(x) hclust(x,method='ward'),

+ col=color.code,breaks=breaks,

+ labRow=GE.cyto,scale='none',ColSideColors=pam50Subtype.color)

> dev.off()

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Figure 1: Result of the hierarchical cluster analysis (GE data).

4

Another feature set (CN data).

> data fileName breaks pdf(paste('./resDir/', fileName,'.pdf', sep=''))

> heatmap(as.matrix(data),

+ distfun=function(x) dist(x,method='manhattan'),

+ hclustfun=function(x) hclust(x,method='ward'),

+ col=color.code,breaks=breaks,

+ labRow=CN.cyto,scale='none',ColSideColors=pam50Subtype.color)

> dev.off()S

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Figure 2: Result of the hierarchical cluster analysis (CN data).

3.2 Dirichlet process mixture model clustering

We first load the package BHC. This library performs Bayesian hierarchical clustering on discretised CN andGE data. It performs bottom-up hierarchical clustering using Dirichlet process to model uncertainty in thedata and Bayesian model selection to decide at each step which cluster merges.

Load BHC package.

> library(BHC)

> samples bhc.cn bhc.ge

Plot results.

> pdf(file='./resDir/BHCplotCN.pdf', width=30, height=8)

> plot(bhc.cn)

> dev.off()

> WriteOutClusterLabels(bhc.cn, "./resDir/BHClabelsCN.txt", verbose=TRUE)

> pdf(file='./resDir/BHCplotGE.pdf', width=30, height=8)

> plot(bhc.ge)

> dev.off()

> WriteOutClusterLabels(bhc.ge, "./resDir/BHClabelsGE.txt", verbose=TRUE)

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Figure 3: Result of bhc() function.

3.3 Integrative clustering

We first load the package iCluster and then organize the data for the analysis [2]. The iCluster libraryimplements a sparse clustering method which will select features from CN and GE data for joint clusteringsamples. Here the idea is to generate integrated cluster assignments based on joint inference across multipledata types.

Load iCluster library and create an input object for iCluster.

> library(iCluster)

> datasets datasets[[1]] datasets[[2]] fit iclusters

> selected.cn.feature selected.ge.feature selected.feature selected.feature names(selected.feature) write.table(selected.feature, file='./resDir/selectedFeatureByIntClust.txt',

+ sep='\t', quote=FALSE, row.names=FALSE, col.names=FALSE)

Plot the integrative clustering result using plotCE() function from Dance package.

> dataCol dataColors CN.data GE.data rownames(CN.data) rownames(GE.data) plotCE(CN.data[selected.cn.feature, ], GE.data[selected.ge.feature, ], dataCol=dataCol,

+ grouping=iclusters, dataColors=dataColors, fileName='./resDir/IntClustOutcome.pdf')

1q25.31q32.21q32.31q32.11q21.21q23.31q24.11q32.11q221q23.31q42.38q24.131q21.21q21.31q24.21q32.11q42.131q42.21q448q24.111q32.18q22.11q21.21q221q24.21q25.11q25.21q25.31q31.31q42.131q42.28q24.1216q24.31q21.11q32.18q21.21q21.21q21.21q21.31q21.31q228q22.38q23.18q24.1116q22.116q23.21q23.11q23.31q25.21q438q22.316q22.116q24.216q24.31q21.31q23.18q22.18q22.28q22.38q24.1316q1316q1316q24.11q21.11q21.11q21.11q21.31q21.31q228q22.18q24.316q1316q22.31q221q31.28q21.118q21.138q21.38q24.316q1316q1316q22.18q11.238q13.18q24.228q24.221q21.31q31.31q32.11q42.138q13.28q21.38q22.316q12.216q22.116q23.116q23.216q24.21q221q228q21.118q21.1316q23.3

1q23.31q21.21q42.121q23.31q411q25.38q24.121q21.11q42.111q21.21q21.31q221q23.31q32.11q42.118q22.316q22.116q23.21q42.121q42.131q42.131q42.21q438q22.28q22.38q24.131q24.21q441q448q22.18q22.216q131q21.11q21.31q221q25.31q32.11q42.128q24.1316q131q23.28q21.118q21.138q21.38q22.18q22.316q1316q2116q22.116q22.116q24.31q21.21q21.21q221q32.11q42.131q42.38q11.238q22.18q24.31q21.21q21.21q21.31q23.11q23.21q24.21q25.11q411q42.131q448q13.28q13.28q21.118q22.38q22.316q22.116q23.116q23.216q24.216q24.31q221q221q23.31q23.31q31.11q32.18q11.238q21.118q22.316q22.116q22.116q22.116q22.116q22.316q24.216q24.316q24.3

Figure 4: Result of plotCE() function.

How these outcomes from different clustering algorithms differ?

> source("./dataDir/myfunctions.r")

> bhc.cn.clusters bhc.ge.clusters clustering1 clustering2 clustering3

> data rownames(data) colnames(data) pdf(file='./resDir/coOccuranceClustering.pdf')

> heatmap(as.matrix(data),

+ distfun=function(x) dist(x, method='manhattan'),

+ hclustfun=function(x) hclust(x, method='ward'),

+ col=c('white','darkblue','darkred','black'),breaks=c(-1, 0.5, 1.5, 2.5, 3.5),

+ scale='none', ColSideColors=pam50Subtype.color)

> dev.off()

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Figure 5: Result of co-occurance clustering. Blue is co-occurance once, red is twice and black is three times.

Discussions

Only CN data from chromosome 1 and 8 are chosen in the iCluster output, why? And what happenswhen the penalty (lambda) is set lower?

Setting the parameters for making calls from segmented CN data can be tricky in CGHcall. The minimumsegment to be fit has to be adjusted according to the probe designed on the array.

Co-occurance matrix enables comparisons across many clustering outcomes, but how many cluster shouldbe there in this dataset, and does the POD score tell the same story?

8

Homework: Is there an optimal number of clusters?

Compute scores of iCluster using different number of clusters k.

# score.pod

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Figure 6: Result of plotiCluster() function.

10

R version 2.12.0 (2010-10-15)

Platform: i386-pc-mingw32/i386 (32-bit)

attached base packages:

[1] splines grid stats graphics grDevices utils datasets

[8] methods base

other attached packages:

[1] iCluster_1.2.0 corpcor_1.5.7 penalized_0.9-32

[4] survival_2.35-8 DLBCL_1.3.1 snow_0.3-3

[7] Dance_0.9 IGIR_0.1 igraph_0.5.4-2

[10] KEGG.db_2.4.5 RSQLite_0.9-2 DBI_0.2-5

[13] Matrix_0.999375-44 lol_0.5 CGHregions_1.8.0

[16] CGHbase_1.8.0 marray_1.27.0 limma_3.5.21

[19] qvalue_1.24.0 samr_1.28 impute_1.22.0

[22] biomaRt_2.5.1 HTSanalyzeR_1.2.5 RankProd_2.22.0

[25] cellHTS2_2.14.0 locfit_1.5-6 lattice_0.19-13

[28] akima_0.5-4 hwriter_1.2 vsn_3.17.2

[31] splots_1.16.0 genefilter_1.31.2 RColorBrewer_1.0-2

[34] BioNet_1.8.0 RBGL_1.25.1 GSEABase_1.12.0

[37] graph_1.28.0 annotate_1.27.3 AnnotationDbi_1.11.10

[40] Biobase_2.9.2 R.utils_1.5.3 R.oo_1.7.4

[43] R.methodsS3_1.2.1

loaded via a namespace (and not attached):

[1] affy_1.28.0 affyio_1.18.0 Category_2.16.0

[4] MASS_7.3-7 prada_1.26.0 preprocessCore_1.12.0

[7] RCurl_1.4-4.1 rrcov_1.1-00 stats4_2.12.0

[10] tcltk_2.12.0 tools_2.12.0 XML_3.2-0.1

[13] xtable_1.5-6

11