ToyTree with matrix/heatmap

Plot tree with matrix/heatmap¶

In [5]:

import toytree
import numpy as np
import toyplot

# load tree with variable name lengths
tree = toytree.tree("https://eaton-lab.org/data/Cyathophora.tre")
tree = tree.root(13) #13th node corresponds to przwilenskii outgroup

import toytree import numpy as np import toyplot # load tree with variable name lengths tree = toytree.tree("https://eaton-lab.org/data/Cyathophora.tre") tree = tree.root(13) #13th node corresponds to przwilenskii outgroup

Method 1:¶

The simplest method is to plot the tree and markers on shared coordinate axes. To make it easy to space items on the x-axis I set the tree to be 2X the width of the data (matrix), which allows me to use units of x=1 to space items on the x-axis. Then I generate a canvas and axes by drawing a tree, as usual, and here I add the data as square scatterplot markers with different opacities to represent the (randomly generated) data.

The only tricky thing here is that you need to use tip_labels_style to offset the x-location of the tre tip labels, and also to extend the x-axis max domain if the names are long to prevent them from getting cut off.

In [6]:

# generate some random data for this columns
spdata = np.random.randint(low=1, high=10, size=(tree.ntips, 5))
spdata

# generate some random data for this columns spdata = np.random.randint(low=1, high=10, size=(tree.ntips, 5)) spdata

Out[6]:

array([[2, 5, 7, 7, 7],
       [6, 2, 2, 4, 2],
       [5, 3, 9, 3, 5],
       [8, 6, 5, 9, 6],
       [5, 6, 7, 2, 4],
       [8, 2, 9, 4, 3],
       [4, 2, 2, 3, 2],
       [9, 2, 1, 4, 8],
       [6, 6, 1, 5, 3],
       [3, 5, 2, 8, 2],
       [7, 9, 1, 5, 1],
       [1, 4, 1, 8, 7],
       [9, 8, 7, 6, 4]])

In [11]:

# scale tree
ctree = tree.mod.edges_scale_to_root_height()

# get canvas and axes with tree plot
canvas, axes, mark = ctree.draw(
    width=600,
    height=300,
    tip_labels_align=True,
    tip_labels_style={"-toyplot-anchor-shift": "80px"}
    
);

# add n columns of data (here random data)
ncols = 5
xoffset = 1
for col in range(5):
    
    # select the column of data
    data = spdata[:, col]
    
    # plot the data column
    axes.scatterplot(
        np.repeat(col, tree.ntips) + xoffset, 
        np.arange(tree.ntips),
        marker='s',
        size=10,
        color="magenta",           
        opacity=0.1 + data[::-1] / data.max(),
        title=data,
    );

# stretch domain to fit long tip names
axes.x.domain.max = 40

# scale tree ctree = tree.mod.edges_scale_to_root_height() # get canvas and axes with tree plot canvas, axes, mark = ctree.draw( width=600, height=300, tip_labels_align=True, tip_labels_style={"-toyplot-anchor-shift": "80px"} ); # add n columns of data (here random data) ncols = 5 xoffset = 1 for col in range(5): # select the column of data data = spdata[:, col] # plot the data column axes.scatterplot( np.repeat(col, tree.ntips) + xoffset, np.arange(tree.ntips), marker='s', size=10, color="magenta", opacity=0.1 + data[::-1] / data.max(), title=data, ); # stretch domain to fit long tip names axes.x.domain.max = 40

Method 2:¶

Using both a matrix and cartesian axes in toyplot. The key to aligning the two is that matrices have a margin of 50px by default. There aren't as many options to style matrix cells as there are in the option above. Here I used the right-side matrix labels to add and align tip names.

In [8]:

# a random rectangular matrix
matrix = np.arange(tree.ntips * 5).reshape(tree.ntips, 5)
matrix.shape

# a random rectangular matrix matrix = np.arange(tree.ntips * 5).reshape(tree.ntips, 5) matrix.shape

Out[8]:

(13, 5)

In [9]:

# create a canvas
canvas = toyplot.Canvas(width=500, height=350);

# add tree 
axes = canvas.cartesian(bounds=(50, 150, 70, 250))
tree.draw(axes=axes, tip_labels=False, tip_labels_align=True)

# add matrix
table = canvas.table(
    rows=13,
    columns=5, 
    margin=0,
    bounds=(175, 250, 65, 255),
)

colormap = toyplot.color.brewer.map("BlueRed")

# apply a color to each cell in the table
for ridx in range(matrix.shape[0]):
    for cidx in range(matrix.shape[1]):
        cell = table.cells.cell[ridx, cidx]
        cell.style = {
            "fill": colormap.colors(matrix[ridx, cidx], 0, 100), 
        }

# style the gaps between cells
table.body.gaps.columns[:] = 3
table.body.gaps.rows[:] = 3 

# hide axes coordinates
axes.show = False

# create a canvas canvas = toyplot.Canvas(width=500, height=350); # add tree axes = canvas.cartesian(bounds=(50, 150, 70, 250)) tree.draw(axes=axes, tip_labels=False, tip_labels_align=True) # add matrix table = canvas.table( rows=13, columns=5, margin=0, bounds=(175, 250, 65, 255), ) colormap = toyplot.color.brewer.map("BlueRed") # apply a color to each cell in the table for ridx in range(matrix.shape[0]): for cidx in range(matrix.shape[1]): cell = table.cells.cell[ridx, cidx] cell.style = { "fill": colormap.colors(matrix[ridx, cidx], 0, 100), } # style the gaps between cells table.body.gaps.columns[:] = 3 table.body.gaps.rows[:] = 3 # hide axes coordinates axes.show = False

In [10]:

# create a canvas
canvas = toyplot.Canvas(width=500, height=350);

# add tree 
axes = canvas.cartesian(bounds=(50, 150, 70, 250))
tree.draw(axes=axes, tip_labels=False, tip_labels_align=True)

# add matrix
colormap = toyplot.color.brewer.map("BlueRed")
table = canvas.matrix(
    (matrix, colormap),
    bounds=(120, 300, 25, 295), 
    tshow=True,
    tlabel="Traits",
    lshow=False,
    rshow=True,
    margin=0,
    rlocator=toyplot.locator.Explicit(range(tree.ntips), tree.get_tip_labels()[::-1])
)

# hide axes coordinates
axes.show = False

# create a canvas canvas = toyplot.Canvas(width=500, height=350); # add tree axes = canvas.cartesian(bounds=(50, 150, 70, 250)) tree.draw(axes=axes, tip_labels=False, tip_labels_align=True) # add matrix colormap = toyplot.color.brewer.map("BlueRed") table = canvas.matrix( (matrix, colormap), bounds=(120, 300, 25, 295), tshow=True, tlabel="Traits", lshow=False, rshow=True, margin=0, rlocator=toyplot.locator.Explicit(range(tree.ntips), tree.get_tip_labels()[::-1]) ) # hide axes coordinates axes.show = False