Intrudoction to caOmicsV...AMIGO3 ECM1 SLC26A6 PLVAP CSRNP1-3 3 From center to outer: Tissue Type RNASeq miRNASeq Methylation CNV Figure 2. caOmicsV bioNetCircos layout plot 3 Making

Intrudoction to caOmicsV

Hongen Zhang, Ph.D.Genetics Branch, Center for Cancer Research,

National Cancer Institute, NIH

March 10, 2015

Contents

1 Introduction 1

2 An Quick Demo 2

3 Making Plot Data Set 4

4 Plot bioMatrix Layout Manually 5

5 Plot bioNetCircos Layout Manually 8

6 sessionInfo 11

1 Introduction

Translational genomics research in cancer, e.g., International Cancer GenomeConsortium (ICGC) and The Cancer Genome Atlas (TCGA), has generatedlarge multidimensional datasets from high-throughput technologies. Multidi-mensional data offers great promise to improve clinical applications of genomicinformation in diagnosis, prognosis and therapeutics of cancers. Tools to effec-tively visualize integrated multidimensional data are important for understand-ing and describing the relationship between genomic variations and cancers.The caOmicsV package provides methods to visualize multidimensional cancergenomic data in two layouts: a heatmap-like matrix layout, bioMatrix, and cir-cular layouts superimposed on a biological network or graph, bioNetCircos.

The data that could be plotted with each layout is listed below:

� Clinical (phenotypes) data such as gender, tissue type, and diagnosis,plotted as colored rectangles

� Expression data such as RNASeq and miRNASeq, plotted as heatmap

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� Category data such as DNA methylation status, plotted as colored boxoutlines on bioMatrix layout and bars on bioNetCircos layout

� Binary data such as mutation status and DNA copy number variations,plotted as colored points

� Text labelling, for gene names, sample names, summary in text format

In addition, link lines can also be plotted on bioNetCircos layout to showthe relationship between two samples.

For bioNetCircos layout, igraph and bc3net packages must be installed first.

2 An Quick Demo

Following code will generate a bioMatrix layout image with the build-in demodata.

> library(caOmicsV)

> data(biomatrixPlotDemoData)

> plotBioMatrix(biomatrixPlotDemoData, summaryType="text")

> bioMatrixLegend(heatmapNames=c("RNASeq", "miRNASeq"),

+ categoryNames=c("Methyl H", "Methyl L"),

+ binaryNames=c("CN LOSS", "CN Gain"),

+ heatmapMin=-3, heatmapMax=3, colorType="BlueWhiteRed")

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sample_type

ECM1SLC26A6

ADAMTS13FCN3

CFPCXCL12PTH1R

LIFRPLVAP

TOMM40LLEPREL1AMIGO3CSRNP1

DBHOLFML3RCAN1

COL15A1LYVE1

CDKN3RND3MT1F

SEMA3FLOC222699

LILRB5ESM1BCO2

hsa−mir−10bhsa−mir−139hsa−mir−10bhsa−mir−10bhsa−mir−10bhsa−mir−10bhsa−mir−10bhsa−mir−10bhsa−mir−424hsa−mir−101−2hsa−mir−10bhsa−mir−450bhsa−mir−10bhsa−mir−10bhsa−mir−10bhsa−mir−10bhsa−mir−424hsa−mir−10bhsa−mir−101−1hsa−mir−10bhsa−mir−10bhsa−mir−139hsa−mir−139hsa−mir−10bhsa−mir−450bhsa−mir−10b

Solid Tissue NormalPrimary Tumor

logFC

−4.0 2.6−3.6−5.9−4.4−3.8−4.4−3.6 2.9 1.9−2.5 2.0−2.5−5.0−2.9−2.7 4.3−4.0 4.2−2.9−6.7 1.6 2.5−2.5 5.0−5.1

−3 3Top: RNASeq Bottom: miRNASeq

Methyl HMethyl L

CN LOSSCN Gain

Figure 1. caOmicsV bioMatrix layout plot

Run the code below will get a bioNetCircos layout image with the build-indemo data.

> library(caOmicsV)

> data(bionetPlotDemoData)

> plotBioNetCircos(bionetPlotDemoData)

[1] "plot polygon"

[1] "plot heatmap"

[1] "plot heatmap"

[1] "plot bar"

[1] "plot points"

> dataNames <- c("Tissue Type", "RNASeq", "miRNASeq", "Methylation", "CNV")

> bioNetLegend(dataNames, heatmapMin=-3, heatmapMax=3)

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ADAMTS13

FCN3

CXCL12

PTH1R

LEPREL1CSRNP1

OLFML3

RND3

LIFR

TOMM40L

RCAN1

CDKN3

SEMA3F

BCO2

CFP

ESM1

MT1F

LILRB5

LOC222699

DBH

COL15A1

AMIGO3

LYVE1

ECM1

SLC26A6

PLVAP

−3 3From center to outer:

Tissue TypeRNASeqmiRNASeqMethylationCNV

Figure 2. caOmicsV bioNetCircos layout plot

3 Making Plot Data Set

To use the default plot function shown as above, the first step is making an plotdata set to hold all datasets in a list object. Two demo data sets are includedin the package installation and could be explored with:

> library(caOmicsV)


> names(biomatrixPlotDemoData)

[1] "sampleNames" "geneNames" "secondGeneNames" "sampleInfo"

[5] "heatmapData" "categoryData" "binaryData" "summaryInfo"


> names(bionetPlotDemoData)



caOmicsV package has a function getPlotDataSet() to make the plot dataset as above. The input data to pass to the function are as below:

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� sampleNames: required, character vector with names of samples to plot

� geneNames: required, character vector with names of genes to plot

� sampleData: required, data frame with rows for samples and columns forfeatures. The first column must be sample names same as the sample-Names above in same order

� heatmapData: list of data frames (maximum 2), continue numeric datasuch as gene expression data.

� categoryData: list of data frames(maximum 2), categorical data such asmethylation High, Low, NO.

� binaryData: list of data frames(maximum 3), binary data such as 0/1.

� summaryData: list of data frames (maximum 2), summarization for genesor for samples.

� secondGeneNames: names of second set of genes, e.g. miRNA names, tolabel genes on right side of matrix layout.

All genomic data must be held with data frame in the format of rows forgenes and columns for samples. The first column of each data frame must begene names same as the geneNames as above in same order. The column namesof each data frame must be sample names same as sampleNames as above insame order. caOmicsV package contains functions to sort data frame for givenorder and several supplement functions are also included in the package to helpextract required data set from big datasets by supplying required gene namesand sample names in a given order.

To make caOmicsV plot, the plot data set must contains at least one genomicdata (one of heatmap data, category data, or binary data)

4 Plot bioMatrix Layout Manually

The default plot method, plotBiomatrix(), is a convenient and efficient way tomake bioMatirx plot. In case of necessary, users can follow procedures below togenerate bioMatrix layout plot manually.

1. Demo data

> library(caOmicsV)


> dataSet <- biomatrixPlotDemoData

> names(dataSet)



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2. Initialize bioMatrix Layout

> numOfGenes <- length(dataSet$geneNames);

> numOfSamples <- length(dataSet$sampleNames);

> numOfPhenotypes <- nrow(dataSet$sampleInfo)-1;

> numOfHeatmap <- length(dataSet$heatmapData);

> numOfSummary <- length(dataSet$summaryData);

> phenotypes <- rownames(dataSet$sampleInfo)[-1];

> sampleHeight <- 0.4;

> sampleWidth <- 0.1;

> samplePadding <- 0.025;

> geneNameWidth <- 1;

> sampleNameHeight <- 2.5;

> remarkWidth <- 2;

> summaryWidth <- 1;

> rowPadding <- 0.1;

> initializeBioMatrixPlot(numOfGenes, numOfSamples, numOfPhenotypes,

+ sampleHeight, sampleWidth, samplePadding, rowPadding,

+ geneNameWidth, remarkWidth, summaryWidth, sampleNameHeight)

> caOmicsVColors <- getCaOmicsVColors()

> png("caOmicsVbioMatrixLayoutDemo.png", height=8, width=12,

+ unit="in", res=300, type="cairo")

> par(cex=0.75)

> showBioMatrixPlotLayout(dataSet$geneNames,dataSet$sampleNames, phenotypes)

3. Plot tissue types on phenotype area

> head(dataSet$sampleInfo)[,1:3]

TCGA.BC.A216.Normal TCGA.BD.A2L6.Normal TCGA.BD.A3EP.Normal

sampleID "TCGA.BC.A216.Normal" "TCGA.BD.A2L6.Normal" "TCGA.BD.A3EP.Normal"

sample_type "Solid Tissue Normal" "Solid Tissue Normal" "Solid Tissue Normal"

> rowIndex <- 2;

> sampleGroup <- as.character(dataSet$sampleInfo[rowIndex,])

> sampleTypes <- unique(sampleGroup)

> sampleColors <- rep("blue", length(sampleGroup));

> sampleColors[grep("Tumor", sampleGroup)] <- "red"

> rowNumber <- 1

> areaName <- "phenotype"

> plotBioMatrixSampleData(rowNumber, areaName, sampleColors);

> geneLabelX <- getBioMatrixGeneLabelWidth()

> maxAreaX <- getBioMatrixDataAreaWidth()

> legendH <- getBioMatrixLegendHeight()

> plotAreaH <- getBioMatrixPlotAreaHeigth()

> sampleH<- getBioMatrixSampleHeight()

> sampleLegendX <- geneLabelX + maxAreaX

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> sampleLegendY <- plotAreaH + legendH - length(sampleTypes)*sampleH

> colors <- c("blue", "red")

> legend(sampleLegendX, sampleLegendY, legend=sampleTypes,

+ fill=colors, bty="n", xjust=0)

4. Heatmap plot

> heatmapData <- as.matrix(dataSet$heatmapData[[1]][,]);

> plotBioMatrixHeatmap(heatmapData, maxValue=3, minValue=-3)

> heatmapData <- as.matrix(dataSet$heatmapData[[2]][,])

> plotBioMatrixHeatmap(heatmapData, topAdjust=sampleH/2,

+ maxValue=3, minValue=-3);

> secondNames <- as.character(dataSet$secondGeneNames)

> textColors <- rep(caOmicsVColors[3], length(secondNames));

> plotBioMatrixRowNames(secondNames, "omicsData", textColors,

+ side="right", skipPlotColumns=0);

5. Draw outline for each samples to show methylation status.

> categoryData <- dataSet$categoryData[[1]]

> totalCategory <- length(unique(as.numeric(dataSet$categoryData[[1]])))

> plotColors <- rev(getCaOmicsVColors())

> plotBioMatrixCategoryData(categoryData, areaName="omicsData",

+ sampleColors=plotColors[1:totalCategory])

6. Binary data plot

> binaryData <- dataSet$binaryData[[1]];

> plotBioMatrixBinaryData(binaryData, sampleColor=caOmicsVColors[4]);

> binaryData <- dataSet$binaryData[[2]];

> plotBioMatrixBinaryData(binaryData, sampleColor=caOmicsVColors[3])

7. Plot summary data on right side of plot area

> summaryData <- dataSet$summaryInfo[[1]][, 2];

> summaryTitle <- colnames(dataSet$summaryInfo[[1]])[2];

> remarkWidth <- getBioMatrixRemarkWidth();

> sampleWidth <- getBioMatrixSampleWidth();

> col2skip <- remarkWidth/2/sampleWidth + 2;

> plotBioMatrixRowNames(summaryTitle, areaName="phenotype",

+ colors="black", side="right", skipPlotColumns=col2skip);

> plotBioMatrixRowNames(summaryData, "omicsData",

+ colors=caOmicsVColors[3], side="right",

+ skipPlotColumns=col2skip)

8. Add legend

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> bioMatrixLegend(heatmapNames=c("RNASeq", "miRNASeq"),

+ categoryNames=c("Methyl H", "Methyl L"),

+ binaryNames=c("CN LOSS", "CN Gain"),

+ heatmapMin=-3, heatmapMax=3,

+ colorType="BlueWhiteRed")

> dev.off()

null device

1

Run code above should generate an image same as Figure 1.

5 Plot bioNetCircos Layout Manually

With default bioNetCircos layout plot method, the node layout and labellingrely on the igraph package and sometimes the node layout and labelling maynot be in desired location. In that case, it is recommended to manually checkout the layout first then plot each item.

Following are basic procedures to make a bioNetCircos plot:

1. Demo data

> library(caOmicsV)


> dataSet <- bionetPlotDemoData

> sampleNames <- dataSet$sampleNames

> geneNames <- dataSet$geneNames

> numOfSamples <- length(sampleNames)

> numOfSampleInfo <- nrow(dataSet$sampleInfo) - 1

> numOfSummary <- ifelse(dataSet$summaryByRow, 0, col(dataSet$summaryInfo)-1)

> numOfHeatmap <- length(dataSet$heatmapData)

> numOfCategory <- length(dataSet$categoryData)

> numOfBinary <- length(dataSet$binaryData)

> expr <- dataSet$heatmapData[[1]]

> bioNet <- bc3net(expr)

2. Initialize bioNetCircos layout

> widthOfSample <- 100

> widthBetweenNode <- 3

> lengthOfRadius <- 10

> dataNum <- sum(numOfSampleInfo, numOfSummary, numOfHeatmap,

+ numOfCategory, numOfBinary)

> trackheight <- 1.5

> widthOfPlotArea <- dataNum*2*trackheight

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> initializeBioNetCircos(bioNet, numOfSamples, widthOfSample,

+ lengthOfRadius, widthBetweenNode, widthOfPlotArea)

> caOmicsVColors <- getCaOmicsVColors()

> supportedType <- getCaOmicsVPlotTypes()

> par(cex=0.75)

> showBioNetNodesLayout()

3. Manually label each node

At this point, each node has its index labelled. Manually check out the de-sired location for node name (gene) labelling then label each node (the nodeindex here may be different from your graph).

> par(cex=0.6)

> onTop <- c(14, 15, 16, 9, 7, 20, 8, 24, 10, 25)

> labelBioNetNodeNames(nodeList=onTop,labelColor="blue",

+ labelLocation="top", labelOffset = 0.7)

> onBottom <- c(26, 22, 23, 18, 19, 3, 5)

> labelBioNetNodeNames(nodeList=onBottom,labelColor="black",

+ labelLocation="bottom", labelOffset = 0.7)

> onLeft <- c(2, 11, 21, 17)

> labelBioNetNodeNames(nodeList=onLeft,labelColor="red",

+ labelLocation="left", labelOffset = 0.7)

> onRight <- c(13, 12, 4, 1, 6)

> labelBioNetNodeNames(nodeList=onRight,labelColor="brown",

+ labelLocation="right", labelOffset = 0.7)

Once all node names are labelled correctly, plot area of each node could beerased for plotting.

> eraseBioNetNode()

4. Plot each data set

> inner <- lengthOfRadius/2

> outer <- inner + trackheight

Plot tissue type for each node. Repeat this step if there are more than oneclinical features.

> groupInfo <- as.character(dataSet$sampleInfo[2, ])

> sampleColors <- rep("blue", numOfSamples);

> sampleColors[grep("Tumor", groupInfo)] <- "red"

> plotType=supportedType[1]

> groupInfo <- matrix(groupInfo, nrow=1)

> bioNetCircosPlot(dataValues=groupInfo,

+ plotType, outer, inner, sampleColors)

> inner <- outer + 0.5


9

Heatmap plot for each node. Repeat this step if there are more heatmapdata.

> exprData <- dataSet$heatmapData[[1]]

> plotType <- supportedType[4]

> bioNetCircosPlot(exprData, plotType, outer, inner,

+ plotColors="BlueWhiteRed", maxValue=3, minValue=-3)



Category data plot for each node. Repeat this step if there are more categorydatasets.

> categoryData <- dataSet$categoryData[[1]]

> plotType <- supportedType[2];

> bioNetCircosPlot(categoryData, plotType,

+ outer, inner, plotColors="red")

length of data values and colors are different.

First color will be used for all samples.



Binary data plot for each node. Repeat this step if there are more binarydatasets

> binaryData <- dataSet$binaryData[[1]]

> plotType <- supportedType[3]

> plotColors <- rep(caOmicsVColors[1], ncol(binaryData))

> bioNetCircosPlot(binaryData, plotType,

+ outer, inner, plotColors)



Link samples on a node. Repeat this step for each node when needed.

> outer <- 2.5

> bioNetGraph <- getBioNetGraph()

> nodeIndex <- which(V(bioNetGraph)$name=="PLVAP")

> fromSample <- 10

> toSample <- 50

> plotColors <- "red"

> linkBioNetSamples(nodeIndex, fromSample,

+ toSample, outer, plotColors)

> fromSample <- 40

> toSample <- 20

> plotColors <- "blue"

> linkBioNetSamples(nodeIndex, fromSample,

+ toSample, outer, plotColors)

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5. Add legend

> dataNames <- c("Tissue Type", "RNASeq", "Methylation", "CNV")

> bioNetLegend(dataNames, heatmapMin=-3, heatmapMax=3)

The output from the code above should be same as Figure 2.

6 sessionInfo

> sessionInfo()

R version 4.0.3 (2020-10-10)

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

Running under: Ubuntu 18.04.5 LTS

Matrix products: default

BLAS: /home/biocbuild/bbs-3.12-bioc/R/lib/libRblas.so

LAPACK: /home/biocbuild/bbs-3.12-bioc/R/lib/libRlapack.so

locale:

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

[3] LC_TIME=en_US.UTF-8 LC_COLLATE=C

[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8

[7] LC_PAPER=en_US.UTF-8 LC_NAME=C

[9] LC_ADDRESS=C LC_TELEPHONE=C

[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C

attached base packages:

[1] stats graphics grDevices utils datasets methods base

other attached packages:

[1] caOmicsV_1.20.0 bc3net_1.0.4 lattice_0.20-41 Matrix_1.2-18

[5] infotheo_1.2.0 c3net_1.1.1 igraph_1.2.6

loaded via a namespace (and not attached):

[1] compiler_4.0.3 magrittr_1.5 tools_4.0.3 grid_4.0.3

[5] pkgconfig_2.0.3

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Documents

Intrudoction to caOmicsV...AMIGO3 ECM1 SLC26A6 PLVAP CSRNP1-3 3 From center to outer: Tissue Type RNASeq miRNASeq Methylation CNV Figure 2. caOmicsV bioNetCircos layout plot 3 Making