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

2. Outline
ggplot2
Cytoscape
PhenoGram

3. ggplot2 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)

4. ggplot2 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

5. ggplot2 ggplot2 Plotting system for R
Flexible, accessible, visualization of data
We will walk through some examples, great references:
“R Graphics Cookbook”
“ggplot2” by Hadley Wickham
Many more examples also exist on-line
Worth doing image searches when you have a new set of data to plot to get ideas and basic code to modify

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

7. ggplot2 Input/Output A little about file output Raster/bitmap
Array of pixels
Can go high resolution (ex 600 dpi)
Vector
Infinitely “zoomable”
Can be modified more easily in Adobe Illustrator
Slow to render with thousands of graphical objects
ggsave(file = “output.pdf”) Or
pdf(file = “output.pdf”)
Then ggplot command
dev.off()
Raster: png(),jpeg(),bitmap(),tiff()
Vector: pdf(),ps()

8. ggplot2 Input/Output A little about file output
Illustrator: svg
Latex: ps
MS Office: png (600 dpi)
Open Office: png (600 dpi)
Pdflatex: pdf, png (600 dpi)
Web: png (72 dpi)
Or whatever your journal specifies!
Can specify dpi by adding to the command
ggsave(file = “output.png”, dpi=600)

9. 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

10. 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… let’s go make a first plot!

11. ggplot2 Example 1: Scatter Plot
Load example data frame 1:
dat <- read.table ("datafile.txt",header=FALSE,sep="\t")
Name the columns:
names(dat) <-c("SampleID","PC1","PC2","Ancestry","Site","Platform","Sex","BMI")
Type dat to check your data frame
and remember, up and down arrows are your friend in R!

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

13. ggplot2 Example 1: Scatter Plot
ggplot(dat, aes (x=PC1, y=PC2))
Indicates the data (our data frame)
xval column values are mapped to the x position, etc. 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
ggplot(dat,aes(x=PC1,y=PC2)) + geom_point(aes(color=Ancestry))
And if you want
ggsave(file="output1.pdf")

14. ggplot2 Example 1: Scatter Plot

15. ggplot2 Example 1: Scatter Plot
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
last_plot()+geom_point(aes(color=Ancestry))

16. ggplot2 Example 1: Scatter Plot

17. ggplot2 Example 1: Scatter Plot
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() + scale_x_continuous(limits=c(0,8))

18. ggplot2 Example 1: Scatter Plot
Change points
ggplot(dat,aes(x=PC1,y=PC2,color=Ancestry)) + geom_point(shape=1)+scale_colour_hue(l=50) # Open circles and 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=Ancestry)) + geom_point(shape=1) + scale_colour_hue(l=50) + geom_smooth(method=lm,se=FALSE)

19. ggplot2 Example 1: Scatter Plot

20. ggplot2 Example 1: Scatter Plot
Set shape based on a condition ggplot(dat,aes(x=PC1,y=PC2,shape=Ancestry)) + geom_point() Set shape and color based on separate conditions ggplot(dat,aes(x=PC1, y=PC2, color=Platform,shape=Ancestry)) + geom_point() Same but use hollow circles and triangles ggplot(dat,aes(x=PC1,y=PC2,shape=Ancestry, color=Platform)) + geom_point() + scale_shape_manual(values=c(1,2))

21. ggplot2 Example 1: Scatter Plot

22. ggplot2 Example 1: Scatter Plot
Same but increase shape size and text size theme_set(theme_gray(base_size = 15)) ggplot(dat,aes(x=PC1,y=PC2,shape=Ancestry, color=Platform)) + geom_point(size=8)

23. ggplot2 Example 2: Histograms
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)

24. ggplot2 Example 2: Histogram and Density Graphs

25. 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=Ancestry))+ geom_bar(stat="identity")

26. 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()

27. 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()

28. 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()

29. 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=Sex,y=BMI)) + geom_boxplot()

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

31. 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=Sex,y=BMI)) + geom_boxplot()+ stat_summary(fun.y=mean,geom="point", shape=5, size=4)

32. 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=Sex,y=BMI,fill=Sex)) + geom_boxplot()+geom_point()

33. 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=Sex,y=BMI,fill=Sex)) + geom_boxplot()+geom_point()+ggtitle("BMI for each Sex")

34. 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=Sex, y=BMI,fill=Sex)) + geom_boxplot() +geom_point()+ggtitle("BMI for each Sex")+xlab("Sex")+ylab("Body Mass Index")

35. 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=Ancestry))+facet_grid(Sex ~ .)

36. 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
Example
Using scale_fill_manual, you can use color hexadecimal codes (you can get these from Color Brewer
ggplot(dat,aes(x=Sex,y=BMI,fill=Sex))+geom_bar(stat="identity") +scale_fill_manual(values=c("#CC6666","#9999CC"))

37. ggplot2 Other Notes Not covered here but so many options!
Color of background
Grid line modification
Font choice
Other kinds of plots such as heatmaps, and using the techniques here to make Manhattan plots, coloring maps with information

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

39. Cytoscape
Introduction to Cytoscape

40. PhenoGram Chromosomal Ideogram Can add lines, shapes, and text
Can add cytogenetic banding patterns
Web version here:
Example files here:
Currently only human chromosomal information, adding mouse soon and will add other model organisms

41. PhenoGram Chromosomal Ideogram Can add lines, shapes
Can add cytogenetic banding patterns
Download: phenogram-groups-sample.txt
Go to:

42. PhenoGram

43. PhenoGram Chromosomal Ideogram Can plot just lines
Download: phenogram-groups-poscolor.txt
Go to: