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peterk87/Generating Newick string output from hierarchical clustering of some cgMLST profiles.ipynb

Created August 3, 2018 21:46
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Generating Newick string output from hierarchical clustering of some cgMLST profiles.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Generating Newick string output from hierarchical clustering of some cgMLST profiles\n",
"\n",
"Assuming you have an array of cgMLST profiles in, for example, a Pandas DataFrame read from a CSV or tab-delimited file, you can perform hierarchical clustering and output a Newick string.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*Enabling in-line figures*"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"%pylab inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Imports"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/CSCScience.ca/pkruczkiewicz/2018-08-03-python-newick-mst/venv/lib/python3.6/importlib/_bootstrap.py:205: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
" return f(*args, **kwds)\n"
]
}
],
"source": [
"from typing import Dict, Tuple, List, Union, Optional\n",
"\n",
"import numpy as np\n",
"import scipy as sp\n",
"import pandas as pd\n",
"\n",
"from fastcluster import linkage\n",
"from scipy.sparse.csgraph import minimum_spanning_tree\n",
"from scipy.cluster.hierarchy import to_tree, ClusterNode, dendrogram\n",
"from scipy.spatial.distance import squareform"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Setting up some random cgMLST profile data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed = 42"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"from string import ascii_lowercase"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"allele_number_min = 1\n",
"allele_number_max = 4\n",
"n_markers = 10"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Our profiles are random integers between a specified range"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"cgmlst_profiles = np.random.randint(allele_number_min, high=allele_number_max + 1, size=len(ascii_lowercase) * n_markers)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([3, 1, 2, 3, 2, 4, 1, 3, 4, 1, 4, 3, 4, 2, 3, 3, 2, 1, 2, 3, 2, 1,\n",
" 2, 4, 1, 2, 4, 2, 2, 4, 1, 4, 2, 4, 2, 3, 2, 2, 3, 1, 3, 3, 2, 4,\n",
" 4, 1, 2, 1, 4, 4, 1, 2, 3, 2, 4, 1, 4, 4, 2, 4, 2, 2, 1, 2, 4, 3,\n",
" 1, 4, 2, 3, 2, 3, 1, 4, 2, 1, 2, 2, 4, 3, 2, 1, 2, 2, 4, 3, 2, 1,\n",
" 1, 1, 3, 1, 2, 4, 3, 2, 4, 1, 3, 2, 2, 2, 4, 1, 1, 4, 2, 4, 3, 2,\n",
" 2, 3, 1, 2, 1, 4, 3, 3, 4, 2, 4, 4, 2, 3, 2, 2, 4, 4, 1, 3, 4, 4,\n",
" 3, 1, 3, 4, 3, 3, 3, 4, 1, 3, 2, 3, 1, 1, 2, 1, 4, 3, 3, 2, 3, 1,\n",
" 1, 4, 1, 2, 3, 1, 1, 4, 2, 4, 3, 1, 4, 4, 3, 4, 2, 3, 4, 2, 1, 4,\n",
" 2, 4, 4, 2, 3, 1, 1, 1, 4, 3, 2, 1, 4, 4, 2, 1, 1, 3, 4, 1, 3, 2,\n",
" 2, 4, 1, 4, 1, 4, 3, 2, 3, 3, 4, 4, 3, 2, 1, 3, 1, 3, 3, 4, 3, 4,\n",
" 1, 4, 3, 2, 4, 3, 2, 1, 2, 4, 4, 1, 1, 2, 2, 2, 1, 4, 1, 1, 4, 2,\n",
" 1, 2, 4, 3, 3, 2, 2, 3, 4, 1, 1, 2, 4, 4, 3, 3, 4, 4])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cgmlst_profiles"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Reshape into 2D array"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"cgmlst_profiles.shape = (len(ascii_lowercase), n_markers)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[3, 1, 2, 3, 2, 4, 1, 3, 4, 1],\n",
" [4, 3, 4, 2, 3, 3, 2, 1, 2, 3],\n",
" [2, 1, 2, 4, 1, 2, 4, 2, 2, 4],\n",
" [1, 4, 2, 4, 2, 3, 2, 2, 3, 1],\n",
" [3, 3, 2, 4, 4, 1, 2, 1, 4, 4],\n",
" [1, 2, 3, 2, 4, 1, 4, 4, 2, 4],\n",
" [2, 2, 1, 2, 4, 3, 1, 4, 2, 3],\n",
" [2, 3, 1, 4, 2, 1, 2, 2, 4, 3],\n",
" [2, 1, 2, 2, 4, 3, 2, 1, 1, 1],\n",
" [3, 1, 2, 4, 3, 2, 4, 1, 3, 2],\n",
" [2, 2, 4, 1, 1, 4, 2, 4, 3, 2],\n",
" [2, 3, 1, 2, 1, 4, 3, 3, 4, 2],\n",
" [4, 4, 2, 3, 2, 2, 4, 4, 1, 3],\n",
" [4, 4, 3, 1, 3, 4, 3, 3, 3, 4],\n",
" [1, 3, 2, 3, 1, 1, 2, 1, 4, 3],\n",
" [3, 2, 3, 1, 1, 4, 1, 2, 3, 1],\n",
" [1, 4, 2, 4, 3, 1, 4, 4, 3, 4],\n",
" [2, 3, 4, 2, 1, 4, 2, 4, 4, 2],\n",
" [3, 1, 1, 1, 4, 3, 2, 1, 4, 4],\n",
" [2, 1, 1, 3, 4, 1, 3, 2, 2, 4],\n",
" [1, 4, 1, 4, 3, 2, 3, 3, 4, 4],\n",
" [3, 2, 1, 3, 1, 3, 3, 4, 3, 4],\n",
" [1, 4, 3, 2, 4, 3, 2, 1, 2, 4],\n",
" [4, 1, 1, 2, 2, 2, 1, 4, 1, 1],\n",
" [4, 2, 1, 2, 4, 3, 3, 2, 2, 3],\n",
" [4, 1, 1, 2, 4, 4, 3, 3, 4, 4]])"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cgmlst_profiles"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Compute distance matrix\n",
"\n",
"Hamming distance metric is the proportion of matching alleles between profiles"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"dm = sp.spatial.distance.pdist(cgmlst_profiles, metric='hamming')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lower triangular distance matrix output"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1. , 0.8, 0.7, 0.7, 1. , 0.9, 0.8, 0.7, 0.7, 0.9, 0.7, 0.7, 0.8,\n",
" 0.7, 0.6, 0.9, 0.8, 0.7, 0.8, 0.8, 0.8, 1. , 0.6, 1. , 0.6, 0.9,\n",
" 0.8, 0.7, 0.8, 0.6, 0.7, 0.6, 0.8, 0.8, 0.8, 0.8, 0.8, 0.6, 1. ,\n",
" 0.9, 0.6, 0.7, 0.9, 0.9, 0.9, 0.5, 0.8, 0.5, 0.8, 0.7, 0.7, 0.7,\n",
" 0.8, 0.7, 0.7, 0.5, 0.8, 0.8, 0.7, 0.9, 0.8, 0.8, 0.6, 0.8, 0.8,\n",
" 0.5, 0.7, 0.8, 0.8, 0.8, 0.8, 0.8, 0.7, 0.9, 0.9, 0.6, 0.6, 0.7,\n",
" 0.8, 1. , 0.7, 0.8, 0.7, 0.7, 0.5, 0.9, 0.8, 0.9, 0.7, 0.8, 0.6,\n",
" 0.8, 0.8, 1. , 0.7, 0.9, 0.5, 0.6, 0.6, 0.9, 0.8, 0.9, 0.9, 0.4,\n",
" 0.9, 0.6, 0.7, 0.4, 0.7, 0.7, 0.8, 0.6, 1. , 0.9, 0.7, 0.5, 0.9,\n",
" 0.8, 0.9, 0.8, 0.9, 0.8, 0.8, 0.8, 0.8, 0.5, 0.8, 0.8, 0.6, 0.8,\n",
" 0.7, 0.4, 0.8, 0.6, 0.7, 0.7, 0.6, 1. , 0.7, 0.7, 0.8, 1. , 0.9,\n",
" 0.8, 0.9, 0.7, 0.7, 0.6, 0.9, 0.6, 0.6, 0.6, 0.3, 0.7, 0.8, 0.9,\n",
" 0.8, 0.6, 0.8, 1. , 0.5, 0.9, 0.8, 0.6, 0.7, 0.6, 0.7, 0.9, 0.9,\n",
" 0.8, 0.7, 0.8, 0.7, 0.8, 0.8, 0.8, 1. , 0.7, 0.9, 0.9, 0.7, 0.5,\n",
" 0.7, 1. , 0.9, 0.5, 0.6, 0.7, 0.7, 0.8, 0.9, 0.7, 0.8, 0.8, 0.8,\n",
" 0.5, 0.9, 0.7, 0.9, 0.7, 0.8, 0.9, 0.8, 1. , 0.9, 0.6, 0.9, 0.7,\n",
" 0.8, 0.5, 0.8, 0.3, 0.8, 0.9, 1. , 0.6, 0.9, 0.9, 0.9, 0.9, 1. ,\n",
" 0.7, 0.7, 0.8, 1. , 0.3, 0.8, 0.7, 0.6, 0.7, 0.9, 0.8, 0.7, 0.4,\n",
" 0.8, 0.7, 1. , 0.6, 0.9, 1. , 0.9, 0.8, 0.8, 0.9, 0.5, 0.8, 0.9,\n",
" 1. , 0.6, 0.6, 0.9, 0.8, 0.8, 0.5, 0.7, 0.7, 0.9, 0.8, 0.5, 0.9,\n",
" 0.7, 0.6, 0.7, 0.8, 0.8, 0.8, 0.7, 1. , 0.9, 0.9, 0.9, 0.8, 0.8,\n",
" 0.9, 1. , 0.6, 0.9, 0.8, 0.8, 0.9, 0.9, 0.9, 0.8, 0.5, 0.7, 0.7,\n",
" 0.9, 1. , 0.9, 0.8, 0.9, 0.9, 0.8, 0.8, 0.8, 0.9, 0.7, 0.6, 0.7,\n",
" 0.6, 0.5, 0.8, 0.7, 0.5, 0.7, 0.6, 0.7, 0.8, 0.5, 0.5, 0.7, 0.7,\n",
" 0.8, 0.8, 0.5, 0.8, 0.8, 0.6, 0.7, 0.9, 0.6, 0.7, 0.7, 0.6, 0.5])"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dm"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Hierarchical clustering of the distance matrix\n",
"\n",
"Single linkage by default"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"Z = linkage(dm)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[10. , 17. , 0.3, 2. ],\n",
" [11. , 26. , 0.3, 3. ],\n",
" [ 6. , 24. , 0.3, 2. ],\n",
" [25. , 27. , 0.4, 4. ],\n",
" [ 4. , 18. , 0.4, 2. ],\n",
" [14. , 30. , 0.4, 3. ],\n",
" [ 5. , 22. , 0.4, 2. ],\n",
" [15. , 29. , 0.5, 5. ],\n",
" [13. , 33. , 0.5, 6. ],\n",
" [31. , 34. , 0.5, 9. ],\n",
" [ 7. , 35. , 0.5, 10. ],\n",
" [ 8. , 36. , 0.5, 11. ],\n",
" [19. , 37. , 0.5, 12. ],\n",
" [ 2. , 38. , 0.5, 13. ],\n",
" [ 9. , 39. , 0.5, 14. ],\n",
" [16. , 40. , 0.5, 15. ],\n",
" [ 3. , 41. , 0.5, 16. ],\n",
" [32. , 42. , 0.5, 18. ],\n",
" [ 1. , 43. , 0.5, 19. ],\n",
" [28. , 44. , 0.5, 21. ],\n",
" [20. , 45. , 0.5, 22. ],\n",
" [12. , 23. , 0.5, 2. ],\n",
" [ 0. , 46. , 0.6, 23. ],\n",
" [47. , 48. , 0.6, 25. ],\n",
" [21. , 49. , 0.6, 26. ]])"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Z"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Dendrogram of hierarchical clustering of random cgMLST profiles"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"dend = dendrogram(Z, leaf_label_func=lambda x: ascii_lowercase[x])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Complete linkage with Hamming distances might produce a nicer looking tree with distances that make sense to most people."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"dend = dendrogram(linkage(dm, method='complete'), leaf_label_func=lambda x: ascii_lowercase[x])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Complete linkage with Euclidean distances"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"dend = dendrogram(linkage(cgmlst_profiles, method='complete', metric='euclidean'), leaf_label_func=lambda x: ascii_lowercase[x])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Single linkage, Euclidean distances"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"dend = dendrogram(linkage(cgmlst_profiles), leaf_label_func=lambda x: ascii_lowercase[x])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Convert linkage array into ClusterNode object with reference to tree root"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"T = to_tree(Z, rd=False)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<scipy.cluster.hierarchy.ClusterNode at 0x7fc0923c51d0>"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"T"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Functions for converting a SciPy ClusterNode object into a Newick string"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"def _scipy_tree_to_newick_list(node: ClusterNode, newick: List[str], parentdist: float, leaf_names: List[str]) -> List[str]:\n",
" \"\"\"Construct Newick tree from SciPy hierarchical clustering ClusterNode\n",
"\n",
" This is a recursive function to help build a Newick output string from a scipy.cluster.hierarchy.to_tree input with\n",
" user specified leaf node names.\n",
"\n",
" Notes:\n",
" This function is meant to be used with `to_newick`\n",
"\n",
" Args:\n",
" node (scipy.cluster.hierarchy.ClusterNode): Root node is output of scipy.cluster.hierarchy.to_tree from hierarchical clustering linkage matrix\n",
" parentdist (float): Distance of parent node of `node`\n",
" newick (list of string): Newick string output accumulator list which needs to be reversed and concatenated (i.e. `''.join(newick)`) for final output\n",
" leaf_names (list of string): Leaf node names\n",
"\n",
" Returns:\n",
" (list of string): Returns `newick` list of Newick output strings\n",
" \"\"\"\n",
" if node.is_leaf():\n",
" return newick + [f'{leaf_names[node.id]}:{parentdist - node.dist}']\n",
"\n",
" if len(newick) > 0:\n",
" newick.append(f'):{parentdist - node.dist}')\n",
" else:\n",
" newick.append(');')\n",
" newick = _scipy_tree_to_newick_list(node.get_left(), newick, node.dist, leaf_names)\n",
" newick.append(',')\n",
" newick = _scipy_tree_to_newick_list(node.get_right(), newick, node.dist, leaf_names)\n",
" newick.append('(')\n",
" return newick\n",
"\n",
"\n",
"def to_newick(tree: ClusterNode, leaf_names: List[str]) -> str:\n",
" \"\"\"Newick tree output string from SciPy hierarchical clustering tree\n",
"\n",
" Convert a SciPy ClusterNode tree to a Newick format string.\n",
" Use scipy.cluster.hierarchy.to_tree on a hierarchical clustering linkage matrix to create the root ClusterNode for the `tree` input of this function.\n",
"\n",
" Args:\n",
" tree (scipy.cluster.hierarchy.ClusterNode): Output of scipy.cluster.hierarchy.to_tree from hierarchical clustering linkage matrix\n",
" leaf_names (list of string): Leaf node names\n",
"\n",
" Returns:\n",
" (string): Newick output string\n",
" \"\"\"\n",
" newick_list = _scipy_tree_to_newick_list(tree, [], tree.dist, leaf_names)\n",
" return ''.join(newick_list[::-1])"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'((((((((((((((((((((r:0.30000000000000004,k:0.30000000000000004):0.0,l:0.30000000000000004):0.09999999999999998,z:0.4):0.09999999999999998,p:0.5):0.0,n:0.5):0.0,((s:0.4,e:0.4):0.0,o:0.4):0.09999999999999998):0.0,h:0.5):0.0,i:0.5):0.0,t:0.5):0.0,c:0.5):0.0,j:0.5):0.0,q:0.5):0.0,d:0.5):0.0,(w:0.4,f:0.4):0.09999999999999998):0.0,b:0.5):0.0,(y:0.30000000000000004,g:0.30000000000000004):0.19999999999999996):0.0,u:0.5):0.10000000000000009,a:0.6000000000000001):0.0,(x:0.5,m:0.5):0.10000000000000009):0.0,v:0.6000000000000001);'"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"to_newick(T, ascii_lowercase)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Minimum Spanning Tree from cgMLST profiles"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"def mst_adjacency_list(dm: np.array, names: List[str]) -> List[Dict[str, Union[str, int, float]]]:\n",
" mst = minimum_spanning_tree(squareform(dm))\n",
" # scipy.sparse.find returns a tuple with 3 vectors (targets, sources and weights) when given an MST as input\n",
" sources, targets, weights = sp.sparse.find(mst)\n",
" # return a similar data structure as NetworkX json_graph - list of dicts\n",
" links = []\n",
" for s,t,w in zip(sources, targets, weights):\n",
" # undirected graph so order of source to target doesn't matter, however\n",
" # swap source and target ids if source id is larger than target id so\n",
" # that it can be rendered similarly as NetworkX MST graphs\n",
" if s > t:\n",
" s, t = (t, s)\n",
" links.append(dict(source=names[s], target=names[t], weight=w))\n",
" return links"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'source': 'a', 'target': 'p', 'weight': 0.6000000000000001},\n",
" {'source': 'b', 'target': 'w', 'weight': 0.5},\n",
" {'source': 'e', 'target': 'o', 'weight': 0.4},\n",
" {'source': 'e', 'target': 's', 'weight': 0.4},\n",
" {'source': 'f', 'target': 'w', 'weight': 0.4},\n",
" {'source': 'g', 'target': 'y', 'weight': 0.30000000000000004},\n",
" {'source': 'h', 'target': 'o', 'weight': 0.5},\n",
" {'source': 'i', 'target': 'w', 'weight': 0.5},\n",
" {'source': 'c', 'target': 'j', 'weight': 0.5},\n",
" {'source': 'l', 'target': 'r', 'weight': 0.30000000000000004},\n",
" {'source': 'k', 'target': 'p', 'weight': 0.5},\n",
" {'source': 'd', 'target': 'q', 'weight': 0.5},\n",
" {'source': 'f', 'target': 'q', 'weight': 0.5},\n",
" {'source': 'j', 'target': 'q', 'weight': 0.5},\n",
" {'source': 'k', 'target': 'r', 'weight': 0.30000000000000004},\n",
" {'source': 'i', 'target': 's', 'weight': 0.5},\n",
" {'source': 'c', 'target': 't', 'weight': 0.5},\n",
" {'source': 'n', 'target': 'u', 'weight': 0.5},\n",
" {'source': 't', 'target': 'v', 'weight': 0.6000000000000001},\n",
" {'source': 'm', 'target': 'x', 'weight': 0.5},\n",
" {'source': 'b', 'target': 'y', 'weight': 0.5},\n",
" {'source': 'l', 'target': 'z', 'weight': 0.4},\n",
" {'source': 'n', 'target': 'z', 'weight': 0.5},\n",
" {'source': 't', 'target': 'z', 'weight': 0.5},\n",
" {'source': 'x', 'target': 'z', 'weight': 0.6000000000000001}]"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dm = sp.spatial.distance.pdist(cgmlst_profiles, metric='hamming')\n",
"mst_links = mst_adjacency_list(dm=dm, names=list(ascii_lowercase))\n",
"mst_links"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"def colours():\n",
" while True:\n",
" yield '0xFF0000'\n",
" yield '0x00FF00'\n",
" yield '0x0000FF'"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"g_colours = colours()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"jgraph_data = {'nodes': {name: {'color': g_colours.__next__()} for i, name in enumerate(ascii_lowercase)},\n",
" 'edges': [dict(source=edge['source'], target=edge['target'], size=edge['weight']*10) for edge in mst_links]}"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'nodes': {'a': {'color': '0xFF0000'},\n",
" 'b': {'color': '0x00FF00'},\n",
" 'c': {'color': '0x0000FF'},\n",
" 'd': {'color': '0xFF0000'},\n",
" 'e': {'color': '0x00FF00'},\n",
" 'f': {'color': '0x0000FF'},\n",
" 'g': {'color': '0xFF0000'},\n",
" 'h': {'color': '0x00FF00'},\n",
" 'i': {'color': '0x0000FF'},\n",
" 'j': {'color': '0xFF0000'},\n",
" 'k': {'color': '0x00FF00'},\n",
" 'l': {'color': '0x0000FF'},\n",
" 'm': {'color': '0xFF0000'},\n",
" 'n': {'color': '0x00FF00'},\n",
" 'o': {'color': '0x0000FF'},\n",
" 'p': {'color': '0xFF0000'},\n",
" 'q': {'color': '0x00FF00'},\n",
" 'r': {'color': '0x0000FF'},\n",
" 's': {'color': '0xFF0000'},\n",
" 't': {'color': '0x00FF00'},\n",
" 'u': {'color': '0x0000FF'},\n",
" 'v': {'color': '0xFF0000'},\n",
" 'w': {'color': '0x00FF00'},\n",
" 'x': {'color': '0x0000FF'},\n",
" 'y': {'color': '0xFF0000'},\n",
" 'z': {'color': '0x00FF00'}},\n",
" 'edges': [{'source': 'a', 'target': 'p', 'size': 6.000000000000001},\n",
" {'source': 'b', 'target': 'w', 'size': 5.0},\n",
" {'source': 'e', 'target': 'o', 'size': 4.0},\n",
" {'source': 'e', 'target': 's', 'size': 4.0},\n",
" {'source': 'f', 'target': 'w', 'size': 4.0},\n",
" {'source': 'g', 'target': 'y', 'size': 3.0000000000000004},\n",
" {'source': 'h', 'target': 'o', 'size': 5.0},\n",
" {'source': 'i', 'target': 'w', 'size': 5.0},\n",
" {'source': 'c', 'target': 'j', 'size': 5.0},\n",
" {'source': 'l', 'target': 'r', 'size': 3.0000000000000004},\n",
" {'source': 'k', 'target': 'p', 'size': 5.0},\n",
" {'source': 'd', 'target': 'q', 'size': 5.0},\n",
" {'source': 'f', 'target': 'q', 'size': 5.0},\n",
" {'source': 'j', 'target': 'q', 'size': 5.0},\n",
" {'source': 'k', 'target': 'r', 'size': 3.0000000000000004},\n",
" {'source': 'i', 'target': 's', 'size': 5.0},\n",
" {'source': 'c', 'target': 't', 'size': 5.0},\n",
" {'source': 'n', 'target': 'u', 'size': 5.0},\n",
" {'source': 't', 'target': 'v', 'size': 6.000000000000001},\n",
" {'source': 'm', 'target': 'x', 'size': 5.0},\n",
" {'source': 'b', 'target': 'y', 'size': 5.0},\n",
" {'source': 'l', 'target': 'z', 'size': 4.0},\n",
" {'source': 'n', 'target': 'z', 'size': 5.0},\n",
" {'source': 't', 'target': 'z', 'size': 5.0},\n",
" {'source': 'x', 'target': 'z', 'size': 6.000000000000001}]}"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"jgraph_data"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div id=\"graph-f6fd0a4d-9ba8-4202-98cd-8c402d5cf690\"></div>\n",
" <script type=\"text/javascript\">\n",
" require.config({baseUrl: '/',\n",
" paths: {jgraph: ['nbextensions/jgraph.min', 'https://rawgit.com/patrickfuller/jgraph/master/js/build/jgraph.min']}});\n",
" require(['jgraph'], function () {\n",
" var $d = $('#graph-f6fd0a4d-9ba8-4202-98cd-8c402d5cf690');\n",
" $d.width(600); $d.height(400);\n",
" $d.jgraph = jQuery.extend({}, jgraph);\n",
" $d.jgraph.create($d, {nodeSize: 2.000000,\n",
" edgeSize: 0.250000,\n",
" defaultNodeColor: '0x5bc0de',\n",
" defaultEdgeColor: '0xaaaaaa',\n",
" shader: 'basic',\n",
" z: 100,\n",
" runOptimization: true,\n",
" directed: true,\n",
" showSave: false});\n",
" $d.jgraph.draw({\n",
" \"edges\": [\n",
" { \"size\": 6.000000000000001, \"source\": \"a\", \"target\": \"p\" },\n",
" { \"size\": 5.0, \"source\": \"b\", \"target\": \"w\" },\n",
" { \"size\": 4.0, \"source\": \"e\", \"target\": \"o\" },\n",
" { \"size\": 4.0, \"source\": \"e\", \"target\": \"s\" },\n",
" { \"size\": 4.0, \"source\": \"f\", \"target\": \"w\" },\n",
" { \"size\": 3.0000000000000004, \"source\": \"g\", \"target\": \"y\" },\n",
" { \"size\": 5.0, \"source\": \"h\", \"target\": \"o\" },\n",
" { \"size\": 5.0, \"source\": \"i\", \"target\": \"w\" },\n",
" { \"size\": 5.0, \"source\": \"c\", \"target\": \"j\" },\n",
" { \"size\": 3.0000000000000004, \"source\": \"l\", \"target\": \"r\" },\n",
" { \"size\": 5.0, \"source\": \"k\", \"target\": \"p\" },\n",
" { \"size\": 5.0, \"source\": \"d\", \"target\": \"q\" },\n",
" { \"size\": 5.0, \"source\": \"f\", \"target\": \"q\" },\n",
" { \"size\": 5.0, \"source\": \"j\", \"target\": \"q\" },\n",
" { \"size\": 3.0000000000000004, \"source\": \"k\", \"target\": \"r\" },\n",
" { \"size\": 5.0, \"source\": \"i\", \"target\": \"s\" },\n",
" { \"size\": 5.0, \"source\": \"c\", \"target\": \"t\" },\n",
" { \"size\": 5.0, \"source\": \"n\", \"target\": \"u\" },\n",
" { \"size\": 6.000000000000001, \"source\": \"t\", \"target\": \"v\" },\n",
" { \"size\": 5.0, \"source\": \"m\", \"target\": \"x\" },\n",
" { \"size\": 5.0, \"source\": \"b\", \"target\": \"y\" },\n",
" { \"size\": 4.0, \"source\": \"l\", \"target\": \"z\" },\n",
" { \"size\": 5.0, \"source\": \"n\", \"target\": \"z\" },\n",
" { \"size\": 5.0, \"source\": \"t\", \"target\": \"z\" },\n",
" { \"size\": 6.000000000000001, \"source\": \"x\", \"target\": \"z\" }\n",
" ],\n",
" \"nodes\": {\n",
" \"a\": { \"color\": \"0xFF0000\" },\n",
" \"b\": { \"color\": \"0x00FF00\" },\n",
" \"c\": { \"color\": \"0x0000FF\" },\n",
" \"d\": { \"color\": \"0xFF0000\" },\n",
" \"e\": { \"color\": \"0x00FF00\" },\n",
" \"f\": { \"color\": \"0x0000FF\" },\n",
" \"g\": { \"color\": \"0xFF0000\" },\n",
" \"h\": { \"color\": \"0x00FF00\" },\n",
" \"i\": { \"color\": \"0x0000FF\" },\n",
" \"j\": { \"color\": \"0xFF0000\" },\n",
" \"k\": { \"color\": \"0x00FF00\" },\n",
" \"l\": { \"color\": \"0x0000FF\" },\n",
" \"m\": { \"color\": \"0xFF0000\" },\n",
" \"n\": { \"color\": \"0x00FF00\" },\n",
" \"o\": { \"color\": \"0x0000FF\" },\n",
" \"p\": { \"color\": \"0xFF0000\" },\n",
" \"q\": { \"color\": \"0x00FF00\" },\n",
" \"r\": { \"color\": \"0x0000FF\" },\n",
" \"s\": { \"color\": \"0xFF0000\" },\n",
" \"t\": { \"color\": \"0x00FF00\" },\n",
" \"u\": { \"color\": \"0x0000FF\" },\n",
" \"v\": { \"color\": \"0xFF0000\" },\n",
" \"w\": { \"color\": \"0x00FF00\" },\n",
" \"x\": { \"color\": \"0x0000FF\" },\n",
" \"y\": { \"color\": \"0xFF0000\" },\n",
" \"z\": { \"color\": \"0x00FF00\" }\n",
" }\n",
"});\n",
"\n",
" $d.resizable({\n",
" aspectRatio: 600 / 400,\n",
" resize: function (evt, ui) {\n",
" $d.jgraph.renderer.setSize(ui.size.width,\n",
" ui.size.height);\n",
" }\n",
" });\n",
" });\n",
" </script>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"jgraph.draw(jgraph_data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Flat clusters at a number of distance thresholds"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"from scipy.cluster.hierarchy import fcluster"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 4,\n",
" 2, 1, 2, 2], dtype=int32)"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fcluster(Z, t=0.5, criterion='distance')"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"distances = np.unique(dm)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"distances.sort()"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ])"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"distances"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"df_clusters = pd.DataFrame([fcluster(Z, t=distance, criterion='distance') for distance in distances]).transpose()\n",
"df_clusters.index = list(ascii_lowercase)\n",
"df_clusters.columns = distances"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0.30000000000000004</th>\n",
" <th>0.4</th>\n",
" <th>0.5</th>\n",
" <th>0.6000000000000001</th>\n",
" <th>0.7000000000000001</th>\n",
" <th>0.8</th>\n",
" <th>0.9</th>\n",
" <th>1.0</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>a</th>\n",
" <td>22</td>\n",
" <td>18</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b</th>\n",
" <td>20</td>\n",
" <td>16</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>c</th>\n",
" <td>16</td>\n",
" <td>12</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d</th>\n",
" <td>19</td>\n",
" <td>15</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>e</th>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>f</th>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>g</th>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>h</th>\n",
" <td>13</td>\n",
" <td>9</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>i</th>\n",
" <td>14</td>\n",
" <td>10</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>j</th>\n",
" <td>17</td>\n",
" <td>13</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>k</th>\n",
" <td>9</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>l</th>\n",
" <td>9</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>m</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>n</th>\n",
" <td>12</td>\n",
" <td>8</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>o</th>\n",
" <td>8</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>p</th>\n",
" <td>11</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q</th>\n",
" <td>18</td>\n",
" <td>14</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>r</th>\n",
" <td>9</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>s</th>\n",
" <td>7</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>t</th>\n",
" <td>15</td>\n",
" <td>11</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>u</th>\n",
" <td>21</td>\n",
" <td>17</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>v</th>\n",
" <td>23</td>\n",
" <td>19</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>w</th>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>x</th>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>y</th>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>z</th>\n",
" <td>10</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0\n",
"a 22 18 3 1 1 1 1 1\n",
"b 20 16 2 1 1 1 1 1\n",
"c 16 12 2 1 1 1 1 1\n",
"d 19 15 2 1 1 1 1 1\n",
"e 6 5 2 1 1 1 1 1\n",
"f 4 4 2 1 1 1 1 1\n",
"g 3 3 2 1 1 1 1 1\n",
"h 13 9 2 1 1 1 1 1\n",
"i 14 10 2 1 1 1 1 1\n",
"j 17 13 2 1 1 1 1 1\n",
"k 9 6 2 1 1 1 1 1\n",
"l 9 6 2 1 1 1 1 1\n",
"m 1 1 1 1 1 1 1 1\n",
"n 12 8 2 1 1 1 1 1\n",
"o 8 5 2 1 1 1 1 1\n",
"p 11 7 2 1 1 1 1 1\n",
"q 18 14 2 1 1 1 1 1\n",
"r 9 6 2 1 1 1 1 1\n",
"s 7 5 2 1 1 1 1 1\n",
"t 15 11 2 1 1 1 1 1\n",
"u 21 17 2 1 1 1 1 1\n",
"v 23 19 4 1 1 1 1 1\n",
"w 5 4 2 1 1 1 1 1\n",
"x 2 2 1 1 1 1 1 1\n",
"y 3 3 2 1 1 1 1 1\n",
"z 10 6 2 1 1 1 1 1"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_clusters"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
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"name": "ipython",
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