Creating figures for publication

Goals

The goal of this lab is to learn how to make figures involving phylogenetic trees for purposes of publication. FigTree is one option, but today we will be using the software ggtree in R and iTOL for drawing elegant circle trees for large phylogenies.

We will not use the cluster today: everything will be installed and run from your local laptop.

Using ggtree to make tree figures

Download a tree file

Navigate to the folder (on your local laptop) that you want to use for this lab and download this tree file.

Install ggtree

Visit the Bioconductor page for ggtree and follow the instructions in the section labeled Installation to install the ggtree package.

Here is what I typed in my R console within RStudio (but note that the instructions may have changed since I did this):

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("ggtree")

This will end up installing several R packages: ggtree, treeio, tidytree, and ggplot2 (from which ggtree is derived).

Installing phytools

In addition to ggtree, we will also use the phytools package.

Install this package using the Install button in the Packages panel of the output window within RStudio. Just type “phytools” into the Packages edit control and hit the Install button.

Importing packages

You can import the packages we need for todays lab into your R session either using the command line or the Packages panel of the output window in RStudio.

To use the command line, type (or copy/paste) these lines:

library("ggtree")
library("treeio")
library("phytools")
library("tidytree")
library("ggplot2")

To use RStudio, go to the Packages panel and click the checkbox beside each of these 5 packages. The search box can be used to easily find them if you don’t want to do a lot of scrolling.

Reading and storing a tree

We’re dealing with a tree in the Newick file format, which the function read.newick from the package treeio can handle:

tree <- read.newick("moths.txt")

You may need to use the setwd command to set the working directory to the same directory in which you saved the moths.txt file. For example, if moths.txt were in my ggtreelab directory, I would use a setwd command like this:

setwd("/Users/plewis/ggtreelab")

The above working directory path is for a MacIntosh computer; the path specification is different for Windows machines. If you are confused about how to specify the path. try typing

getwd()

to see what R thinks is the current working directory. This should give you some hints about how to specify your actual working directory path.

R can handle more than just Newick formatted tree files. To see what other file formats from the various phylogenetic software that R can handle, checkout treeio. The functionality within treeio used to be part of the ggtree package itself, but the authors recently split ggtree in two with one part (ggtree) handling mostly plotting, and the other other part (treeio) handling mostly file input/output operations.

Creating a circle tree with two clades highlighted

Plot the tree using all default settings

Plot the tree using the ggtree package:

ggtree(tree)

Note that ggtree has plotted just the tree itself, with no taxon labels. ggtree, by default, plots almost nothing, assuming you will add what you want to your tree plot. The grammar/logic of ggtree is meant to model that of ggplot2 and not the R language in general. The syntax of ggtree and ggplot2 makes them easily extendable and particularly useful for graphics, but is by no means intuitive to someone used to R’s plot function.

Adding/Altering Tree Elements with Geoms and Geom-Like Functions

ggtree has a variety of functions available to you that allow you to add different elements to a tree. Many of them have the prefix geoms and are collectively referred to as geoms (which stands for geometric objects). We’ll only go over some of them. You start with a bare bones tree and add elements to the tree, function by function, until you get the tree looking like you want it to look. You’ll see as we progress through this tutorial that visualizing trees in ggtree is a truly additive process.

Leaf Labels

OK, this tree would be more useful with leaf labels.

Add leaf labels using geom_tiplab:

ggtree(tree) + geom_tiplab()

This tree is a little crowded. You can expand the graphics window vertically to get it all to fit, but it might be better to do a circular tree:

ggtree(tree, layout="circular") + geom_tiplab()

OK that’s a bit easier to work with. Those tip labels are nice but a little big. geom_tiplab has a bunch of arguments that you can play around with, including one for the text size. You can read more about the available arguments for a given function in the ggtree manual.

Plot the tree again but with smaller labels (colored blue):

ggtree(tree, layout="circular") + geom_tiplab2(size=1,color='blue')

Notice we are using geom_tiplab2 and not geom_tiplab to show labels on the circular tree. The geom_tiplab2 function is specific to circular trees.

Saving to a PDF

We usually want to save the tree to a PDF file for use in a manuscript, so let’s start doing that now. In order to save the tree to a file, you need to store it in a variable (we will call it t):

t <- ggtree(tree, layout="circular") + geom_tiplab2(size=1,color='blue')

Then you pass your tree variable on to the ggsave command:

ggsave(t, file="moth_tree.pdf", width=8, height=8)

The width and height are in inches, so I’ve sized it to fit nicely on a standard 8.5x11 inch letter-size page. But, you say, isn’t 8 inches almost too wide? Circle trees in ggtree tend to leave some extra margin space (and apparently there is no way around this), so 8x8 scales the tree nicely for 8.5x11 paper.

Hint: You should get used to saving your tree to a PDF file and using that as the basis for further tweaking. Getting it to look good in the plot window may yield a tree that looks quite awful when printed (e.g. labels may be too big, lines may be too thick, etc.) Use the plot window as a crude guide, but when you get close, start saving to PDF and viewing what will actually be saved.

Storing geoms in variables

The geoms that add layers to your plot can also be stored in variables, making for cleaner, less-cluttered code. This also means that you have less pasting to do when you want to tweak a plot.

Try the following, which stores the label geom in a separate variable bluelabels:

bluelabels <- geom_tiplab2(size=1,color='blue')
circletree <- ggtree(tree, layout="circular")
t <- circletree + bluelabels
ggsave(t, file="moth_tree.pdf", width=8, height=8)

Clean out your plot window periodically

Unbeknownst to you, each time you replot your tree (except when you use ggsave), the new plot gets drawn over the top of the previous plot. These plots pile up silently, leading to a lot of plot baggage in RStudio. Try clicking the red circle with a white X inside it in the toolbar of the plot window: you will see the blue-label version of your tree disappear and be replaced by the previous tree (with large black taxon labels). You can clean out all the old plots using the menu item Plots > Clear all… in the main menu of RStudio.

Now that we have everything stored in variables, you can replot your tree inside RStudio just by typing t!

Clade colors

In order to label clades, we need to tell ggtree which nodes represent the root of each clade we want to label. To get the clade root node of interest, use the findMRCA function (find Most Recent Common Ancestor) from the phytools package. We will pass the function two arguments: the labels of two tips that, when traced back in time, serve to locate the root node of each clade of interest. In Keegan et al. (2019), the Amphipyrinae (as currently classified taxonomically) was found to be polyphyletic. Let’s color two clades: one for the true Amphipyrinae, and one for a tribe (Stiriini) currently classified taxonomically in Amphipyrinae but which is actually far removed phylogenetically and thus has no business being classified within Amphipyrinae.

Here is how to save the two clades in the variables amphipyrinae_clade and stiriini_clade:

amphipyrinae_clade <- findMRCA(tree, c("*Redingtonia_alba_KLKDNA0031","MM01162_Amphipyra_perflua"))
stiriini_clade     <- findMRCA(tree, c("*Chrysoecia_scira_KLKDNA0002","*Annaphila_diva_KLKDNA0180"))

Now define a group that consists of the clades we want colored, and to tell ggtree that it should color the tree by according to the group.

tree <- groupClade(tree, c(amphipyrinae_clade, stiriini_clade), group_name = "group")

In the above line of code, we pass the tree object to the groupClade function. Depsite appearances, we are not overwriting tree so that it consists of only the Amphipyrinae and Stiriini clades, just defining clades within tree. Now if you were to execute ggtree(tree, layout="circular") the tree will still look the same.

Amend the ggtree command to style the tree by the grouping of clades we just made named “group”:

circletree <- ggtree(tree, layout="circular", aes(color=group, linetype="solid"))
circletree

The aes stands for aesthetics and is used to supply various attributes affecting the look of the plotted tree to ggtree. As you can see, the tree gets colored according to some default color scheme. We can define our own color scheme. Let’s call it palette:

palette <- c("#000000", "#009E73","#e5bc06")

The values in palette are color values represented by a hexadecimal value. You can Google one of these hexadecimal values and a little interactive hexadecimal color picker will pop up. Feel free to pick two colors of your choosing to use in the palette, but leave #000000 as it is.

When you’re designing a figure for publication, be sure to consider how easily your colors can be distinguished from each other by colorblind. If you use a Mac, the app Sim Daltonism is very handy for choosing accessible colors.

Amend the ggtree command to use the colors we defined:

cladecolors <- scale_colour_manual(values = palette)
circletree <- ggtree(tree, layout="circular", aes(color=group, linetype="solid"))
circletree + cladecolors

Note that I’ve omitted leaf labels from t to avoid clutter.

The order in which clades are colored is determined by the order of clades in the groupClade command. Every lineage in the tree not within a defined clade (i.e. within stiriini_clade or amphipyrinae_clade) is automatically colored according to the first palette value. The first defined clade (stiriini_clade) is colored according to the second palette value, and the second defined clade (amphipyrinae_clade) is colored according to the third palette value.

Clade labels

Let’s add some labels to the two clades. This is relatively straightforward now that we’ve already defined the clade root nodes:

a <- geom_cladelabel(node=amphipyrinae_clade, label="Amphipyrinae")
s <- geom_cladelabel(node=stiriini_clade, label="Stiriini")
circletree + cladecolors + a + s

OK, it is clear that we should adjust the labels so they’re not overlapping the grouping arcs, and let’s hide the legend as it is really not adding anything:

a <-geom_cladelabel(node=amphipyrinae_clade, label="Amphipyrinae", offset.text=0.1)
s <- geom_cladelabel(node=stiriini_clade, label="Stiriini",offset.text=0.3)
nolegend <- theme(legend.position="none")
circletree + cladecolors + a + s + nolegend

Save the circle tree

Let’s finish by saving the circle tree we’ve created to a file named circletree.pdf:

t <- circletree + cladecolors + a + s + nolegend
ggsave(t, file="circletree.pdf", width=8, height=8)

You will see some warnings, but the tree saved in circletree.pdf should look OK.

Plotting a rectangular tree with bootstraps

It is common to want to create a figure of a tree with at least the most important bootstrap/posterior probabilities indicated.

Let’s start by adding node labels to a rectangular (as opposed to circular) tree. We will use the labels to show nodal support values (e.g. bootstraps) which are stored as node labels.

Display the node labels using geom_label:

recttree <- ggtree(tree, layout="rectangular",aes(color=group, linetype="solid"))
bootstraps <- geom_label(aes(label=label))
recttree + bootstraps + cladecolors + a + s + nolegend

You should see A LOT of node labels appear. Let’s subset the node labels in order to just show the ones we want and reduce some of the clutter.

We’ll first grab the data dataframe from within tree q:

q <- ggtree(tree)
d <- q$data

You can explore the structure of objects in the Environment pane of RStudio. Try clicking on d to see what is now stored in that variable. You should see a table with headers parent, node, branch.length, label, group, isTip, x, y, branch, and angle. We will be using only the label and isTip columns. Note as you (slowly) scroll down the rows that the labels column includes taxon labels for tips (i.e. leaves) as well as bootstrap values for internal nodes (and there are a couple of nodes, 155 and 156, with empty labels).

We will use what, in R, is known as logical indexing to extract the subset of labels we want.

To select only internal nodes, try this:

ok <- !d$isTip

We’ve created a variable ok that is a vector of all rows in d for which isTip is FALSE.

Type the following command to see what it looks like:

ok

You should see that ok has FALSE values up to element 155, after which all the elements are TRUE. Why is this?

Let’s also filter out any nodes with bootstraps less than 90:

ok <- !d$isTip & d$label > 90

You can count how many TRUE values are in the ok vector using sum (which adds 1 for every TRUE value and 0 for every FALSE value):

sum

You can count how many TRUE values are in the ok vector using sum (which adds 1 for every TRUE value and 0 for every FALSE value):

sum(ok)

You can count the number of FALSE values using:

sum(!ok)

And you can count all values using:

length(ok)

Do the TRUE and FALSE values add to the total count? If so, we are ready to subset our labels:

dsubset <- d[ok,]
highboots <- geom_label(data=dsubset, aes(label=label))
recttree + highboots + cladecolors + a + s + nolegend

The first line above selects only those rows of d for which ok is TRUE (that’s logical indexing in action). The strange comma is necessary because d is a two dimensional data frame and we only want to mess with the row (first) dimension.

The second line constructs a label geom that takes its data from dsubset rather than the tree’s data object. We still need the aesthetics because we only want the label column from dsubset (if you click dsubset in the Environment panel you’ll see that there is more there than just node labels; it contains just 25 rows of d but retains all of d’s columns).

Scale Bar and Title

Create a scale bar using the scale bar geom:

scalebar <- geom_treescale(x=0, y=12)

I’ve told it to place the scale bar at x=0 (left side; the x axis is measured in units of branch length) and y=12 (12 “taxa” up from the bottom; each unit of the y axis is equivalent to the distance between adjacent tip labels).

Create a title using ggtitle. Use it just like you would a geom:

title <- ggtitle("This is a Title")

Export Plot to PDF

Save the tree labeled with bootstraps greater than 90:

t <- recttree + highboots + cladecolors + a + s + scalebar + title + nolegend
ggsave(t,file="bstree.pdf", width=7, height=10)

If the layout of your tree just isn’t quite what you wanted, go back and play around with the geoms and geom-like functions until the PDF is to your liking. In particular, you may wish to modify your definition of the a and s clade labels to remove the offsets that we used for the circle tree.

Cite ggtree

Remember to cite ggtree if you use it in a published work!

citation("ggtree")

Getting Help

The Google Group for ggtree is fairly active. The lead author of ggtree chimes in regularly to answer people’s questions; just be sure you’ve read the documentation first, otherwise you may be told that he can’t read it for you!

Speaking of documentation there is:

ggtree manual
more accessible documentation
gallery of examples
a couple of tutorial-like vignettes:
- Data Integration, Manipulation and Visualization of Phylogenetic Trees
- Visualizing and Annotating Phylogenetic Trees with R+ggtree

Most recently, the lead author of ggtree released a comprehensive online book on ggtree.

Using iTOL to make tree figures

If you thought ggtree was a bit difficult to use, you will probably become a big fan of iTOL! Our purpose here is just to give you the barest introduction to this amazing software: the excellent documentation and video tutorials make it easy to learn how to do what you need to do on your own. If you’ve even wondered how people made those beautiful circle trees in journals such as Science and Nature, the answer is that they probably used iTOL.

Download a tree file

Download this tree file. This is a 738-taxon tree of green plants based on the chloroplast-encoded RuBisCO large subunit gene _rbc_L.

Open a new tab in your web browswer and go to the iTOL page

https://itol.embl.de/

You may wish to create a free account now so that you can save your work. It is not necessary to create an account for purposes of completing this lab, however.

Click the Upload a tree button and select the rbcl738.txt file. Name it whatever you like; I chose Green plants.

Coloring the branches of a clade

Let’s thicken the branches of the angiosperm (flowering plant) clade and give these branches a different color.

Type Amborella using the Search Tree Nodes button in the left panel (labeled Aa). It should find a taxon named Amborella trichopoda L12628.1. Click on the link to see where this taxon is located in the tree. This taxon is sister to a small clade of 5 other taxa (call it the Nuphar clade), and Amborella plus the Nuphar clade form the sister group to all other angiosperms.

Click on the relatively long edge that is the parent of the Amborella + Nuphar clade. This should highlight a clade of 496 leaves (two thirds of all leaves in the tree) and choose Branches, then Whole clade, then choose a color of your choice. While here, set the width factor to 2 to make the edges in the angiosperm clade twice the normal thickness.

Highlight a clade using shading

Let’s now shade the clade of all ferns.

Type Angiopteris using the Search Tree Nodes button in the left panel (labeled Aa). It should find a taxon named Angiopteris evecta L11052.1. Click on the link to see where this taxon is located in the tree.

Angiopteris evecta is sister to all other ferns in this tree, so click on the parent edge (the stem of the fern clade, which contains 63 leaves) and choose Colored ranges, then Create a new range and choose a color of your choice as well as a label (e.g. “Ferns”).

Click the + Create range button.

Note that you can choose Label to shade the taxon labels in the clade, Clade to shade the clade itself, or Full to shade the clade all the way back to the root. You can also add a border around the shading using the Colored ranges dialog box.

Adding a color strip

The last thing we will do is add a ring of colored tick marks that indicate plants that are of particular economic importance (or are of interest to Paul).

Create a plain text file (i.e. use BBEdit if you have a Mac or Notepad++ if you have Windows; do not use a word processor) on your laptop named plant-uses.txt and fill it with the following text:

DATASET_COLORSTRIP
SEPARATOR SPACE
DATASET_LABEL Plants
LEGEND_TITLE Uses
LEGEND_SCALE 1
LEGEND_SHAPES 1 1 1 1 1
LEGEND_COLORS #ffa500 #ff0000 #964B00 #00ff00 #0000ff
LEGEND_LABELS Spices/beverages Fragrances/medicines/floral Fruits/grains Trees Other
DATA

# Spices, beverages
Thymus_vulgaris_Z37472.1_Thyme #ffa500
Origanum_vulgare_Z37427.1_Oregano #ffa500
Salvia_officinalis_Z37446.1_Sage #ffa500
Rosmarinus_officinalis_Z37435.1_Rosemary #ffa500
Coriandrum_sativum_L11676.1_Coriander_Cilantro #ffa500
Zingiber_gramineum_L05465.1_Z._officinale_is_Ginger #ffa500
Piper_betle_L12660.2_P._nigrum_is_Black_Pepper #ffa500
Cinnamomum_camphora_L12641.2_Cinnamon #ffa500
Coffea_arabica_X83631.1_Coffee #ffa500
Cichorium_intybus_L13652.1_Chicory #ffa500
Vitis_aestivalis_L01960.2_Grape #ffa500
Humulus_lupulus_AF206777.1_Hops #ffa500
Sassafras_albidum_AF206819.1_Sassafras #ffa500

# Fragrances, medicines, drugs, floral
Lavandula_angustifolia_Z37407.1_Lavender #ff0000
Jasminum_suavissimum_L01929.2_Jasmine #ff0000
Dianthus_caryophyllus_M77699.1_Carnation #ff0000
Passiflora_quadrangularis_L01940.2_Passionflower #ff0000
Ranunculus_trichophyllus_L08766.1_Buttercup #ff0000
Papaver_orientale_L08764.1_Poppy #ff0000
Strelitzia_nicolai_L05461.1_Bird_Of_Paradise_Flower #ff0000
Sansevieria_cylindrica_Z73698.1_Snake_Plant #ff0000
Lilium_superbum_L12682.2_Turks_Cap_Lily #ff0000
Nicotiana_tabacum_J01450.1_Tobacco #ff0000
Digitalis_purpurea_L01902.2_Foxglove #ff0000
Panax_quinquefolius_U50250.1_Ginseng #ff0000
Colchicum_speciosum_L12673.2_Colchicine #ff0000

# Foods (grains, fruits)
Sesamum_indicum_L14408.1_Sesame #964B00
Lycopersicon_esculentum_AM087200.2_Tomato #964B00
Vaccinium_macrocarpon_L12625.2_Cranberry #964B00
Diospyros_virginiana_L12613.2_Persimmon #964B00
Sambucus_racemosa_AF446928.1_Elderberry #964B00
Spinacia_oleracea_V00168.1_Spinach #964B00
Fagopyrum_esculentum_D86285.1_Buckwheat #964B00
Ficus_pretoriae_AJ390067.1_F._carica_is_Fig #964B00
Morus_rubra_U06812.1_Red_Mulberry #964B00
Prunus_persica_AF206813.1_Prunus_includes_Almond_Apricot_Cherry_Plum #964B00
Cucurbita_pepo_AF206756.1_Squash_Pumpkin #964B00
Cucumis_sativus_AF206755.1_Cucumber #964B00
Pisum_sativum_X03853.1_Pea #964B00
Ceratonia_siliqua_Z70165.1_Carob #964B00
Glycyrrhiza_glabra_Z70171.1_Licorice #964B00
Pistacia_vera_AJ235786.1_Pistachio #964B00
Mangifera_indica_U39269.2_Mango #964B00
Anacardium_occidentale_AY462008.1_Cashew #964B00
Citrus_paradisi_AJ238407.1_Grapefruit #964B00
Theobroma_cacao_AF022125.1_Chocolate #964B00
Brassica_rapa_DQ231548.1_Turnip #964B00
Armoracia_rusticana_AF020323.1_Horseradish #964B00
Carica_papaya_M95671.1_Papaya #964B00
Punica_granatum_L10223.1_Pomegranate #964B00
Avena_sativa_L15300.1_Oats #964B00
Triticum_aestivum_AB042240.3_Wheat #964B00
Oryza_sativa_DQ167807.1_Rice #964B00
Ananas_comosus_L19977.1_Pineapple #964B00
Cocos_nucifera_AY012507.1_Coconut_Palm #964B00
Dioscorea_polygonoides_AF206762.1_Yam #964B00
Persea_americana_L14620.1_Avocado #964B00
Myristica_fragrans_AY298839.1_Nutmeg_Mace #964B00

# Trees
Betula_nigra_L12634.2_Birch #00ff00
Carya_glabra_L12637.2_Hickory #00ff00
Fagus_americana_L13338.1_Beech #00ff00
Acer_saccharum_L01881.2_Sugar_Maple #00ff00
Ginkgo_biloba_AJ235804.1 #0000ff Ginkgo #00ff00
Metasequoia_glyptostroboides_AJ235805.1_Dawn_Redwood #00ff00
Sequoia_sempervirens_L25755.2_California_Redwood #00ff00
Taxodium_distichum_S75127.1_Bald_Cypress #00ff00
Pinus_radiata_AY497250.1_Monterrey_Pine #00ff00
Picea_pungens_AF456382.1_Blue_Spruce #00ff00
Larix_occidentalis_X63663.1_Western_Larch #00ff00
Pseudotsuga_menziesii_X52937.1_Douglas_Fir #00ff00
Cedrus_deodara_AF456381.1_Deodar_Cedar #00ff00
Abies_magnifica_X58391.1_Red_Fir #00ff00
Liriodendron_tulipifera_AF190430.1_Tulip_Tree #00ff00

# Other
Monarda_didyma_Z37418.1_Bee_Balm #0000ff
Nepeta_cataria_Z37421.1_Catnip #0000ff
Asclepias_exaltata_L14390.1_Milkweed #0000ff
Taraxacum_officinale_AY395562.1_Dandelion #0000ff
Drosera_filliformis_L01911.2_Sundew #0000ff
Dionaea_muscipula_L01904.2_Venus_Flytrap #0000ff
Medicago_sativa_X04975.1_Alfalfa #0000ff
Viola_soraria_L11674.1_Violet #0000ff
Rhus_copallina_U00440.1_Winged_Sumac #0000ff
Gossypium_robinsonii_L13186.2_Cotton #0000ff
Nelumbo_lutea_M77032.1_Lotus #0000ff
Bambusa_valida_AJ746171.1_Bamboo #0000ff
Typha_latifolia_M91634.1_Cattail #0000ff
Tillandsia_elizabethae_L19971.1_Spanish_Moss #0000ff
Smilax_glauca_AF206822.1_Greenbriar #0000ff
Lemna_minuta_M91630.1_Duckweed #0000ff
Aristolochia_macrophylla_L12630.2_Dutchmans_Pipe #0000ff
Nuphar_advena_DQ069501.1_Water_Lily #0000ff
Victoria_cruziana_M77036.1_Victoria_Lily #0000ff
Dennstaedtia_punctilobula_U05918.1_Hay_Scented_Fern #0000ff
Pteridium_aquilinum_U05939.1_Bracken_Fern #0000ff
Osmunda_cinnamomea_D14882.1_Cinnamon_Fern #0000ff
Equisetum_arvense_L11053.1_Horsetail_Scouring_Rush #0000ff
Psilotum_nudum_AP004638.1_Whisk_Fern #0000ff
Selaginella_apoda_AJ010854.1_Meadow_Spike_Moss #0000ff

Using your file manager, simply drag the file onto your tree in iTOL to create a color strip (a ring of colored tick marks) indicating the different uses of plants defined in the file. If dragging doesn’t work, you can also choose the Datasets tab in the Control panel, then click Upload annotation files to load the file.

Each non-blank,non-comment line of file below the DATA keyword lists a taxon name (as defined in the original tree file that you uploaded) followed by the hexadecimal code for a color. The keyword SEPARATOR near the top of the file specifies that a single SPACE should separate the taxon name from the color code. The keyword DATASET_COLORSTRIP at the very beginning of the file says that the data contained in this file is for use in making a color strip.

The Tree annotation help page has a link to a collection of templates that can be used for various sorts of annotations. We modified the template named dataset_color_strip_template.txt to create the example above. There are also nice video tutorials on annotating trees that you should watch if you get serious about creating an annotated tree.

Exporting a tree image

To export the tree to a PDF file, click on the Export tab of the main Control panel.

Choose PDF: Portable Document Format from the Format dropdown.

Choose Full image from the Export area radio button panel.

Type rbcl738tree.pdf as the file name into the File name text box.

Push the Export button to start the process of generating the figure. You will be notified when the download has begun.

Explore iTOL

We’ve just given you a very small taste of what iTOL can do. Check out the Tree Gallery page to see what’s possible.

References

K Keegan, JD Lafontaine, N Wahlberg, D Wagner. 2019. Towards resolving Amphipyrinae (Lepidoptera, Noctuoidea, Noctuidae): a massively polyphyletic taxon. Systematic Entomology 44:451-464. DOI:10.1111/syen.12336

G Yu, D Smith, H Zhu, Y Guan, and TT Lam. 2017. ggtree: an R package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods in Ecology and Evolution 8:28-36. DOI:10.1111/2041-210X.12628

What to turn in

Send circletree.pdf, bstree.pdf, and rbcl738tree.pdf to Analisa in order to get credit for this lab.

Acknowledgements

The ggtree part of this lab was developed by Kevin Keegan and only slightly modified later by Paul O. Lewis