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Edit and run



In [1]:

    
%pylab inline









    



Populating the interactive namespace from numpy and matplotlib



In [2]:

    
from SuchTree import SuchTree, SuchLinkedTrees, pearson
import seaborn
import pandas as pd



In [3]:

    
from ete2 import Tree, TreeStyle, NodeStyle, TextFace
from numpy import linspace

ts = TreeStyle()
ts.mode = 'r'
ts.show_leaf_name = True
ts.branch_vertical_margin = 2
ts.scale = 5000
ts.show_leaf_name = False
ts.show_scale = False

nstyle = NodeStyle()
nstyle['size'] = 0

ete_tree = Tree( 'gopher.tree' )

for node in ete_tree.traverse() :
    node.set_style(nstyle)
    if node.is_leaf :
        tf = TextFace( node.name.replace('_',' ').replace('\'','') )
        tf.fsize = 10
        tf.hz_align = 100
        node.add_face( tf, 0 )

ete_tree.render("%%inline", w=200, units="mm", tree_style=ts)









    Out[3]:



In [4]:

    
from ete2 import Tree, TreeStyle, NodeStyle, TextFace
from numpy import linspace

ts = TreeStyle()
ts.mode = 'r'
ts.show_leaf_name = True
ts.branch_vertical_margin = 2
ts.scale = 5000
ts.show_leaf_name = False
ts.show_scale = False

nstyle = NodeStyle()
nstyle['size'] = 0

ete_tree = Tree( 'lice.tree' )

for node in ete_tree.traverse() :
    node.set_style(nstyle)
    if node.is_leaf :
        tf = TextFace( node.name.replace('_',' ').replace('\'','') )
        tf.fsize = 10
        tf.hz_align = 100
        node.add_face( tf, 0 )

ete_tree.render("%%inline", w=200, units="mm", tree_style=ts)









    Out[4]:



In [5]:

    
T1 = SuchTree( 'gopher.tree' )
D1 = zeros( ( len(T1.leafs),len(T1.leafs) ) )
for i,a in enumerate(T1.leafs.values()) :
    for j,b in enumerate( T1.leafs.values() ) :
        D1[i,j] = T1.distance( a, b )
seaborn.clustermap(D1)









    Out[5]:





<seaborn.matrix.ClusterGrid at 0x7f2a98ed4110>



In [6]:

    
T2 = SuchTree( 'lice.tree' )
D2 = zeros( ( len(T2.leafs),len(T2.leafs) ) )
for i,a in enumerate(T2.leafs.values()) :
    for j,b in enumerate( T2.leafs.values() ) :
        D2[i,j] = T2.distance( a, b )
seaborn.clustermap(D2)









    Out[6]:





<seaborn.matrix.ClusterGrid at 0x7f2a6fb06890>



In [7]:

    
links = pd.DataFrame.from_csv( 'gopher-louse-associations.txt' )
links



In [8]:

    
SLT = SuchLinkedTrees( T1, T2, links )









    



139820938546784 allocating columns in 2573192824
bulding default subset.
bulding default link list.



In [9]:

    
result = SLT.linked_distances()
seaborn.jointplot( result['TreeA'], result['TreeB'] )









    Out[9]:





<seaborn.axisgrid.JointGrid at 0x7f2a8403ca50>



In [10]:

    
from scipy.stats import kendalltau, pearsonr
print kendalltau( result['TreeA'], result['TreeB'] )
print pearsonr( result['TreeA'], result['TreeB'] )









    



KendalltauResult(correlation=0.20975684102929301, pvalue=0.00029167123165993626)
(0.49018498968585178, 1.3847762345214803e-09)



In [11]:

    
from skbio import TreeNode

t1 = TreeNode.read( 'gopher.tree' )
t2 = TreeNode.read( 'lice.tree' )

d1 = t1.tip_tip_distances()
d2 = t2.tip_tip_distances()



In [12]:

    
from itertools import combinations

l = links.unstack()
l = l[l>0]

d_host = []
p_host = []
d_lice = []
p_lice = []
for (a,b),(c,d) in combinations( l.index, 2 ) :
    B,D = map( d1.ids.index, [b,d] )
    A,C = map( d2.ids.index, [a,c] )
    d_host.append( d1[B,D] )
    d_lice.append( d2[A,C] )
    p_host.append( (b,d) )
    p_lice.append( (a,c) )



In [13]:

    
seaborn.jointplot( array(d_host), array(d_lice) )









    Out[13]:





<seaborn.axisgrid.JointGrid at 0x7f067ebd0190>



In [14]:

    
d_st, d_sb = [],[]
for a,b in combinations( T2.leafs.keys(), 2 ) :
    d_st.append( T2.distance( a, b ) )
    A, B = map( d2.ids.index, [a,b] )
    d_sb.append( d2[ A, B ] )
plot( d_st, d_sb )









    Out[14]:





[<matplotlib.lines.Line2D at 0x7f067d458e10>]



In [15]:

    
d_st, d_sb = [],[]
d = []
for a,b in combinations( T1.leafs.keys(), 2 ) :
    d.append( map( lambda x : T1.leafs[x], ( a, b ) ) ) 
    A, B = map( d1.ids.index, [a,b] )
    d_sb.append( d1[ A, B ] )
d_st = T1.distances( array(d) )
plot( d_st, d_sb, 'ro' )









    Out[15]:





[<matplotlib.lines.Line2D at 0x7f067d32ae50>]



In [16]:

    
p_host_ids = map( lambda x:(T1.leafs[x[0]],T1.leafs[x[1]]), p_host )
p_lice_ids = map( lambda x:(T2.leafs[x[0]],T2.leafs[x[1]]), p_lice )



In [17]:

    
subplot(2,2,1)
title( 'correct host' )
a,b = zip(*p_host_ids)
plot(a,b, 'ro')
subplot(2,2,2)
title( 'correct guest' )
a,b = zip(*p_lice_ids)
plot(a,b, 'ro')
subplot(2,2,3)
a,b = zip(*result['ids_A'])
title( 'candidate host' )
plot(a,b, 'ro')
subplot(2,2,4)
a,b = zip(*result['ids_B'])
title( 'candidate guest' )
plot(a,b, 'ro')









    Out[17]:





[<matplotlib.lines.Line2D at 0x7f067d16b150>]



In [18]:

    
links.T.reindex( SLT.col_names )



In [19]:

    
SLT.linklist









    Out[19]:





array([[10,  4],
       [16, 10],
       [32, 28],
       [22, 18],
       [26, 22],
       [ 0, 26],
       [30, 26],
       [20, 14],
       [ 2, 28],
       [ 4, 12],
       [ 8,  2],
       [28, 24],
       [12,  6],
       [24, 20],
       [14,  8],
       [18, 16],
       [ 6,  0]])



In [20]:

    
ndarray( (SLT.subset_a_size, SLT.subset_b_size) ).shape









    Out[20]:





(15, 17)



In [21]:

    
subplot(1,2,1)
for i in xrange( SLT.linkmatrix.shape[0] ) :
    for j in xrange( SLT.linkmatrix.shape[1] ) :
        if SLT.linkmatrix[i,j] : plot( [i], [j], 'ro' )
subplot(1,2,2)
for i in xrange( SLT.linkmatrix.shape[0] ) :
    for j in xrange( SLT.linkmatrix.shape[1] ) :
        if bool( links.values[i,j] ) : plot( [i], [j], 'ro' )



In [22]:

    
subplot(1,2,1)
for i in SLT.row_names :
    for j in SLT.col_names :
        
subplot(1,2,2)
for i in xrange( SLT.linkmatrix.shape[0] ) :
    for j in xrange( SLT.linkmatrix.shape[1] ) :
        if bool( links.values[i,j] ) : plot( [i], [j], 'ro' )









    



  File "<ipython-input-22-ea19e58de4f1>", line 5
    subplot(1,2,2)
                  ^
IndentationError: expected an indented block



In [12]:

    
from scipy.stats import kendalltau

kendalltau( result['TreeA'], result['TreeB'] )









    Out[12]:





KendalltauResult(correlation=0.20975684102929301, pvalue=0.00029167123165993626)



In [ ]:

    
SLT.col_ids



In [ ]:

	Gset	Gpan	Gche	Gcos	Gcha	Ggeo	Gokl	Gewi	Gtex	Gexp	Gact	Gpero	Gtho	Gtri	Gnad	Tmin	Tbar
GburA	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
GburB	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
Gbre	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
Gpers	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
Ound	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
Ocav	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
Oche	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
Ohet	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0
Ohis	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0
Pbul	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
Ccas	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
Cmer	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
Ztri	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0
Tbot	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0
Ttal	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1

	GburA	GburB	Gbre	Gpers	Ound	Ocav	Oche	Ohet	Ohis	Pbul	Ccas	Cmer	Ztri	Tbot	Ttal
Gpan	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
Gnad	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
Tmin	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
Gtex	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
Gokl	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
Gtho	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1
Tbar	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1
Gexp	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0
Gact	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
Gpero	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0
Gcos	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
Ggeo	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0
Gset	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
Gewi	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0
Gcha	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
Gtri	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
Gche	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0