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Workshop Sarah Pendergrass, PhD MS Research Associate Center for Systems Genomics

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Outline ggplot2 Cytoscape PhenoGram

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ggplot2 Plotting system for R Flexible, accessible, visualization of data You must have R installed You must have ggplot2 installed: install.packages("ggplot2") library(ggplot2) We will walk through some examples, great reference is the “R Graphics Cookbook” Many more examples also exist on-line – worth doing image searches when you have a new set of data to plot

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ggplot2 Developed by Hadley Wickham Grammar of graphics: formal structured perspective on describing data graphics Data properties: typically numerical or categorical values Visual properties: x and y positions of points, colors of lines, heights of bars Once you have your code you can reuse reuse reuse Benefits compared to other R packages Structure of the data can remain the same while making very different types of plots Standard format for generating plots

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ggplot2 vocabulary Data: what we want to visualize Consisting of variables in a data frame Data frame: primary data structure in R with properties of matrices Geoms: geometric objects drawn to represent the data Aesthetics (aes): visual properties of geoms such as defining X, defining Y, line color, point shapes, etc. Mappings: mapping from data values to aesthetics Scales: control mapping from data space to aesthetic space Guides: show viewer how to map visual properties back to data space: tick marks and labels, etc

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ggplot2 basics ggplot2 Data has to be saved in a data frame Each type of variable mapped to an aesthetic must be stored in a separate column (your x, y variables) Basic ggplot2 specification: ggplot(dat, aes(x=xval, y=yval) x=xval maps the column xval to the x position y=yval maps the column yval to the y position Now you need to add geometric objects

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ggplot2 Input/Output A little about file input, output Input data <- read.table (“datafile.txt”,header=TRUE) data <- read.csv(“datafile.txt”, header = FALSE) names(data) <- c(“Column1”, “Column2”, “Column3”) There are commands for importing excel spread sheets Windows: windows( ) will open a new figure window Mac: quartz( ) will open a new figure window

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ggplot2 Example 1: Scatter Plot Ggplot2 Data has to be saved in a data frame Each type of variable mapped to an aesthetic must be stored in a separate column Load example data frame 1: dat <- read.table (“datafile.txt”,header=FALSE,sep=“\t”) Name the columns: names(dat) <- c("SampleID","PC1","PC2","Race","Site","Platform","Gender","BMI") Type dat to check your data frame

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ggplot2 Example 1: Scatter Plot Type dat to check your data frame

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ggplot2 Example 1: Scatter Plot Ggplot2 example 1 ggplot(dat, aes (x=PC1, y=PC2)) Indicates the data (our data frame) Indicates that xval column values are mapped to the x position, and yval column values are mapped to the y position But we need to add geometric objects such as points, so we need to add: Command: ggplot(dat, aes (x=PC1, y=PC2)) + geom_point( ) We can add group to the color of the points, by adding specifying aesthetics for that particular geom Command: ggplot(dat, aes (x=PC1, y=PC2)) + geom_point(aes(color=Race))

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ggplot2 Example 1: Scatter Plot

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Ggplot2 example 1 We can add group to the color of the points, by adding specifying aesthetics for that particular geom Command: ggplot(dat, aes (x=PC1, y=PC2, color=Race)) + geom_point()

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ggplot2 Example 1: Scatter Plot Ggplot2 example 1 How about changing the axes? Command: ggplot(dat, aes (x=PC1, y=PC2)) + geom_point( ) Modify the scale: ggplot(dat, aes (x=PC1, y=PC2)) + geom_point( ) + geom_point( )+ scale_x_continuous (limits = c(0,8))

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ggplot2 Example 1: Scatter Plot Ggplot2 example 1 Change points ggplot(dat, aes(x=PC1, y=PC2, color=Race)) + geom_point(shape=1) + scale_colour_hue(l=50) # Use a slightly darker palette than normal Add regression lines ggplot(dat, aes(x=PC1, y=PC2)) + geom_point(shape=1) + scale_colour_hue(l=50) + geom_smooth(method = lm, se=FALSE) #Add linear regression lines but don’t add shaded confidence region ggplot(dat, aes(x=PC1, y=PC2, color=Race)) + geom_point(shape=1) + scale_colour_hue(l=50) + geom_smooth(method=lm, se=FALSE)

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ggplot2 Example 1: Scatter Plot

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Set shape based on a condition ggplot(dat, aes(x=PC1, y=PC2, shape=Race)) + geom_point() Set shape and color based on separate conditions ggplot(dat, aes(x=PC1, y=PC2, color=Platform,shape=Race)) + geom_point() Same but use hollow circles and triangles ggplot(dat, aes(x=PC1, y=PC2, shape=Race, color=Platform)) + geom_point() + scale_shape_manual(values=c(1,2))

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ggplot2 Example 1: Scatter Plot

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ggplot2 Example 2: Histograms Histogram ggplot(dat, aes(x=BMI)) + geom_histogram(binwidth=.5, colour="black", fill="white") Histogram adding the mean ggplot(dat, aes(x=BMI)) + geom_histogram(binwidth=.5, colour="black", fill="white") +geom_vline(aes(xintercept=mean(BMI, na.rm=T)),color="red", linetype="dashed", size=1) Tip: you can use “bin width” to adjust bin size (wider bins, more items in each bin) ggplot(dat, aes(x=BMI)) + geom_histogram(binwidth=5, colour="black", fill="white") +geom_vline(aes(xintercept=mean(BMI, na.rm=T)),color="red", linetype="dashed", size=1)

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ggplot2 Example 2: Histogram and Density Graphs

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ggplot2 Example 4: Bar Graph Making a bar graph: ggplot(data=dat, aes(x=SampleID, y=BMI))+ geom_bar(stat="identity”) Colors ggplot(data=dat, aes(x=SampleID, y=BMI, fill=Race))+ geom_bar(stat="identity”)

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Ggplot2: Bar Graph The space below the top line on a bar chart is usually meaningless – only representing the distance between start value and plotted value The information of the bar plot can actually be represented with single dots This can cut down on visual clutter, and also make a more visually meaningful plot One way to show the trends of the points better ggplot(data=dat, aes(x=SampleID, y=BMI))+ geom_line()+geom_point() Another way to show the trends of the points better: Cleveland Dot Plot ggplot(data=dat, aes(x=SampleID, y=BMI))+geom_segment(aes(xend=SampleID),yend=0,color="grey") + geom_point()

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Ggplot2: Bar Graph The space below the top line on a bar chart is usually meaningless – only representing the distance between start value and plotted value The information of the bar plot can actually be represented with single dots This can cut down on visual clutter, and also make a more visually meaningful plot One way to show the trends of the points – but needs more: ggplot(data=dat, aes(x=SampleID, y=BMI))+ geom_point()

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Ggplot2: Bar Graph One way to show the trends of the points better – however this might make it seem like nearby points are related via proximity ggplot(data=dat, aes(x=SampleID, y=BMI))+ geom_line()+geom_point()

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Ggplot2: Cleveland Dot Plot Another way to show the trends of the points better: Cleveland Dot Plot ggplot(data=dat, aes(x=SampleID, y=BMI))+geom_segment(aes(xend=SampleID),yend=0,color="grey") + geom_point()

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ggplot2 Example 5: Creating Boxplots When comparing the distributions of groups of data, boxplots are a great approach instead of bar charts Command: ggplot(dat, aes(x=Gender, y=BMI)) + geom_boxplot() Adding condition as color to box plot ggplot(dat, aes(x=Gender, y=BMI,fill=Gender)) + geom_boxplot() Add summary like mean to box plot (Adding mean as a diamond shape) ggplot(dat, aes(x=Gender, y=BMI)) + geom_boxplot()+ stat_summary(fun.y=mean, geom="point", shape=5, size=4) Add the individual data points as well as box plot

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ggplot2 Example 5: Creating Boxplots When comparing the distributions of groups of data, boxplots are a great approach instead of bar charts Command: ggplot(dat, aes(x=Gender, y=BMI)) + geom_boxplot()

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ggplot2 Example 5: Creating Boxplots When comparing the distributions of groups of data, boxplots are a great approach instead of bar charts Command: ggplot(dat, aes(x=Gender, y=BMI)) + geom_boxplot() Adding color to box plot ggplot(dat, aes(x=Gender, y=BMI,fill=Gender)) + geom_boxplot()

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ggplot2 Example 5: Creating Boxplots When comparing the distributions of groups of data, boxplots are a great approach instead of bar charts Add summary like mean to box plot (Adding mean as a diamond shape) ggplot(dat, aes(x=Gender, y=BMI)) + geom_boxplot()+ stat_summary(fun.y=mean, geom="point", shape=5, size=4)

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ggplot2 Example 5: Creating Boxplots When comparing the distributions of groups of data, boxplots are a great approach instead of bar charts Adding individual data points to the box plot ggplot(dat, aes(x=Gender, y=BMI,fill=Gender)) + geom_boxplot() +geom_point()

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ggplot2 Example 5: Creating Boxplots When comparing the distributions of groups of data, boxplots are a great approach instead of bar charts What about adding a title? ggplot(dat, aes(x=Gender, y=BMI,fill=Gender)) + geom_boxplot() +geom_point()+ggtitle('BMI for each Gender')

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ggplot2 Example 5: Creating Boxplots When comparing the distributions of groups of data, boxplots are a great approach instead of bar charts What about adding modifying the axis titles? ggplot(dat, aes(x=Gender, y=BMI,fill=Gender)) + geom_boxplot() +geom_point()+ggtitle('BMI for each Gender')++xlab("Sex")+ylab("Body Mass Index")

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ggplot2 Example 6:Facets You to split up your data by one or more variables and plot the subsets of data together: ggplot(dat, aes (x=PC1, y=PC2)) + geom_point(aes(color=Race))+facet_grid(Gender ~.)

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ggplot2 A Note on Colors In the examples, we used mostly ggplot2 default colors There are lots of options for getting into different colors for ggplot2 http://www.cookbook-r.com/Graphs/Colors_(ggplot2)/ Example Using scale_fill_manual, you can use color hexadecimal codes (you can get these from Color Brewer http://colorbrewer2.org/) ggplot(dat, aes(x=Gender, y=BMI,fill=Gender))+geom_bar(stat="identity") +scale_fill_manual(values=c("#CC6666", "#9999CC"))

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ggplot2 Other Notes A note on saving your image > png("BMI_Boxplot.png") > ggplot(dat, aes (x=PC1, y=PC2)) + geom_point(aes(color=Race))+facet_grid(Gender ~.) > dev.off() Not covered here but so many options! Color of background Grid line modification Font choice Text on the plot Other kinds of plots such as heatmaps, and using the techniques here to make Manhattan plots, coloring maps with information

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ggplot2 Other Notes Examples and code are EVERYWHERE!! This was just a Google Image search on “ggplot2”!

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Cytoscape Introduction to Cytoscape

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PhenoGram Chromosomal Ideogram Can add lines, shapes, and text Can add cytogenetic banding patterns Web version here: http://visualization.ritchielab.psu.edu/phenograms/plot Example files here: http://visualization.ritchielab.psu.edu/phenograms/examples Currently only human chromosomal information, adding mouse soon and will add other model organisms

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PhenoGram Chromosomal Ideogram Can add lines, shapes Can add cytogenetic banding patterns Download: phenogram-groups-sample.txt Go to: http://visualization.ritchielab.psu.edu/phenograms/plot

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PhenoGram

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Chromosomal Ideogram Can plot just lines Download: phenogram-groups- poscolor.txt Go to: http://visualization.ritchielab.psu.ed u/phenograms/plot

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