利用建立分级树对酵母基因表达数据进行聚类分析
一、原理
根据基因表达数据，得出距离矩阵
↓
- 最初，每个点都是一个集合
- 每次选取距离最小的两个集合，将他们合并，然后更新这个新集合与其它点的距离
- 新集合与别的集合距离的计算方法
- ①两个集合之间的最短距离
- ②两个集合所有点之间求距离求平均 →
- 把这个新集合加入距离矩阵中，原来的两个小集合就被替换掉
- 如此循环，直到剩下一个集合，那就建立了一棵树
- 在树的某一处横断，就可以得到6类
230个酵母基因表达数据
http://bioinformaticsalgorithms.com/data/realdatasets/Clustering/230genes_log_expression.txt
二、python下利用Sklearn包实现
Sklearn包的安装
参照 https://scikit-learn.org/stable/install.html
代码（python 3.7环境）
from sklearn.cluster import AgglomerativeClustering
import numpy as np
from os.path import dirname
import numpy as np
import math
import random
import matplotlib.pyplot as plt
def InputData(dataset):
dataset = [line.split() for line in dataset]
name = [item[1] for item in dataset[1:]]
points = []
for line in dataset[1:]:
if(len(line)==10):
points.append(list(map(float,line[3:])))
elif(len(line)==9):
points.append(list(map(float,line[2:])))
return points
if __name__ == ‘__main__‘:
f = open("output","w")
INF = 999999
dataset = open(dirname(__file__)+‘230genes_log_expression.txt‘).read().strip().split(‘\n‘)
X = np.array(InputData(dataset))
clustering = AgglomerativeClustering(n_clusters=6).fit(X)
#print(clustering.labels_)
lb = clustering.labels_
clusters = [[] for i in range(6)]
for i in range(len(X)):
clusters[lb[i]].append(i)
fig=plt.figure()
x = [i for i in range(1,8)]
for c in range(6):
plt.subplot(231+c)
for i in clusters[c]:
plt.plot(x,X[i],linewidth=0.5,linestyle=‘-‘,marker=‘‘)
plt.show()
结果
！注意
初次使用sklearn的时候python3.7解释器会报错
/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/externals/joblib/externals/cloudpickle/cloudpickle.py:47: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module‘s documentation for alternative uses import imp
是因为从python3.4开始就不再支持 import module ， 需要按照错误信息找到相关的文件，将import module 改为 import importlib，问题即解决
三、手动实现建立 Hierarchical树
1 ‘‘‘
2 coder Lokwongho 2018.11
3
4 ‘‘‘
5
6 from os.path import dirname
7 import numpy as np
8 import math
9 import random
10 import matplotlib.pyplot as plt
11
12 ######## Get the Distance Matrix ########
13 def PearsonCorrelationDistance(Expres):
14 n = len(Expres)
15 l = len(Expres[0])
16 Distance = np.zeros(shape=[n,n],dtype=float)
17
18 u = [ sum(i)/l for i in Expres ]
19 Sigma = []
20 for i in range(n):
21 sig=0;
22 for x in range(l):
23 sig += (Expres[i][x]-u[i])**2;
24 Sigma.append(sig)
25
26 for i in range(n):
27 for j in range(i,n):
28 sigU=0
29 for x in range(l):
30 sigU += (Expres[i][x]-u[i])*(Expres[j][x]-u[j])
31 Distance[i][j]=1-(sigU)/math.sqrt(Sigma[i]*Sigma[j])
32 Distance[j][i]=Distance[i][j]
33
34 return Distance
35
36 ######## Build the Hierarchical Tree ########
37 def locateMin(size): # calculate the diatance between two cluster: minimum distance method
38 minVal = 999999
39 minId = [-1,-1]
40 for x in range(size):
41 for y in range(size):
42 if x!=y and matrix[x][y]<minVal and delete[x]==False and delete[y]==False:
43 minVal = matrix[x][y]
44 minId = [x,y]
45 #print(minId,size)
46 return [minId,minVal]
47
48 def buildTree():
49 Tree = [[]for i in range(maxn)]
50 global matrix
51 global delete
52 ptr = n
53 delete = [False for i in range(maxn)]
54 num = [0 for i in range(maxn)]
55 for i in range(n):
56 num[i] = 1
57 weight = [0 for i in range(maxn)]
58 while(ptr<maxn):
59
60 [[minX,minY],minVal] = locateMin(ptr)
61 Tree[ptr].append([minX,minVal/2-weight[minX]])
62 Tree[ptr].append([minY,minVal/2-weight[minY]])
63 Tree[minX].append([ptr,minVal/2-weight[minX]])
64 Tree[minY].append([ptr,minVal/2-weight[minY]])
65 weight[ptr]=minVal/2
66 delete[minX]=True
67 delete[minY]=True
68 #print(minX,minY,minVal)
69 #print(delete)
70 for i in range(ptr+1):
71 if delete[i]==False:
72 tmp=(matrix[minX][i]*num[minX]+matrix[minY][i]*num[minY])/(num[minX]+num[minY])
73 matrix[ptr][i] = tmp
74 matrix[i][ptr] = tmp
75 num[ptr] = num[minX]+num[minY]
76 ptr += 1
77 return Tree
78
79 def HierarchicalClustering(DMatrix):
80 global maxn
81 global n
82 global Tree
83 global matrix
84
85
86 n = len(DMatrix)
87 maxn = 2*n-1
88 matrix = np.zeros(shape=(maxn,maxn))
89 for i in range(n):
90 matrix[i][:n] = DMatrix[i]
91 Tree = buildTree()
92
93 ######## Input and output Data ########
94 def InputData(dataset):
95 dataset = [line.split() for line in dataset]
96 name = [item[1] for item in dataset[1:]]
97 #print(m)
98 # print(m,k)
99 points = []
100 for line in dataset[1:]:
101 if(len(line)==10):
102 points.append(list(map(float,line[3:])))
103 elif(len(line)==9):
104 points.append(list(map(float,line[2:])))
105 return [points,name]
106
107 ‘‘‘
108 Reference to ‘whoami_T‘ in csdn for print the tree
109 https://blog.csdn.net/weixin_39722498/article/details/81534247
110 ‘‘‘
111 def tree(lst):
112 # 树状图输出列表
113 l = len(lst)
114 if l == 0:
115 print(‘-‘ * 3)
116 else:
117 for i, j in enumerate(lst):
118 if i != 0:
119 f.write(tabs[0])
120 print(tabs[0], end=‘‘)
121 if l == 1:
122 s = ‘=‘ * 3
123 elif i == 0:
124 s = ‘┬‘ + ‘-‘ * 2
125 elif i + 1 == l:
126 s = ‘└‘ + ‘─‘ * 2
127 else:
128 s = ‘├‘ + ‘─‘ * 2
129 f.write(s)
130 print(s, end=‘‘)
131 if isinstance(j, list) or isinstance(j, tuple):
132 if i + 1 == l:
133 tabs[0] += blank[0] * 3
134 else:
135 tabs[0] += ‘│‘ + blank[0] * 2
136 tree(j)
137 else:
138 print(name[j])
139 f.write(name[j] + "\n")
140 tabs[0] = tabs[0][:-3]
141
142 def traversalTree(v):
143
144 global visited
145 if v < n:
146 return [v]
147 visited[v] = 1
148 items = []
149 for i in Tree[v]:
150 if visited[i[0]] == 0:
151 items += traversalTree(i[0])
152
153 return [items]
154
155 def outPut():
156 global visited
157 global tabs
158 global blank
159
160 blank = [
161 chr(183)] ##此处为空格格式;Windows控制台下可改为chr(12288) ;linux系统中可改为chr(32)【chr(32)==‘ ‘ ;chr(183)==‘·‘ ;chr(12288)==‘ ‘】
162 tabs = [‘‘]
163 visited = [0 for i in range(maxn)]
164 TreeList = traversalTree(maxn-1)
165 #print(Tree)
166 tree(TreeList)
167 ###########################
168
169 if __name__ == ‘__main__‘:
170
171 f = open("output","w")
172
173 INF = 999999
174 dataset = open(dirname(__file__)+‘230genes_log_expression.txt‘).read().strip().split(‘\n‘)
175
176 [points,name] = InputData(dataset)
177
178 DMatrix = PearsonCorrelationDistance(points)
179
180 HierarchicalClustering(DMatrix)
181
182 outPut()
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·········│··············│········└──┬--YHR096C ·········│··············│···········└──YJL045W ·········│··············└──┬--YEL012w ·········│·················└──┬--YBL045C ·········│····················└──┬--YGL259W ·········│·······················└──YLR312C ·········└──┬--┬--YCR021c ············│··└──┬--YDR343C ············│·····└──┬--YER053c ············│········└──YMR250W ············└──┬--┬--┬--┬--┬--YIL136W ···············│··│··│··│··└──┬--YMR090W ···············│··│··│··│·····└──YNL305C ···············│··│··│··└──┬--┬--YHR087W ···············│··│··│·····│··└──YKL142W ···············│··│··│·····└──┬--YDR031w ···············│··│··│········└──YJR096W ···············│··│··└──┬--YDR516C ···············│··│·····└──YIL111W ···············│··└──┬--┬--YDR342C ···············│·····│··└──YBR183W ···············│·····└──┬--┬--┬--┬--YBR072W ···············│········│··│··│··└──┬--YKL085W ···············│········│··│··│·····└──YLR327C ···············│········│··│··└──┬--┬--YGR008C ···············│········│··│·····│··└──┬--YNL200C ···············│········│··│·····│·····└──┬--YNL173C ···············│········│··│·····│········└──YOL053C ···············│········│··│·····└──┬--YGR248W ···············│········│··│········└──YNL015W ···············│········│··└──┬--YLR258W ···············│········│·····└──┬--YKL103C ···············│········│········└──┬--YGL037C ···············│········│···········└──YLR178C ···············│········└──┬--┬--┬--YHL021C ···············│···········│··│··└──YML128C ···············│···········│··└──┬--YFR015C ···············│···········│·····└──YMR105C ···············│···········└──┬--┬--YFL014W ···············│··············│··└──YOR374W ···············│··············└──┬--YGR244C ···············│·················└──┬--YLL041c ···············│····················└──┬--YER150w ···············│·······················└──YNL160W ···············└──┬--┬--┬--┬--┬--YLL026w ··················│··│··│··│··└──YDR258C ··················│··│··│··└──┬--YDR258C ··················│··│··│·····└──YLR149C ··················│··│··└──┬--┬--YBR139W ··················│··│·····│··└──YLR304C ··················│··│·····└──┬--┬--YEL024w ··················│··│········│··└──┬--YDR529C ··················│··│········│·····└──YPR149W ··················│··│········└──┬--YDR178W ··················│··│···········└──YEL011w ··················│··└──┬--┬--┬--YHR051W ··················│·····│··│··└──┬--YNL052W ··················│·····│··│·····└──YNR001C ··················│·····│··└──┬--YKL141W ··················│·····│·····└──YOR065W ··················│·····└──┬--YBL100C ··················│········└──YPR184W ··················└──┬--┬--YIL162W ·····················│··└──┬--┬--┬--YKL193C ·····················│·····│··│··└──┬--YIL113W ·····················│·····│··│·····└──YMR170C ·····················│·····│··└──┬--┬--YOL032W ·····················│·····│·····│··└──┬--YKL151C ·····················│·····│·····│·····└──┬--YDR272W ·····················│·····│·····│········└──YCL035C ·····················│·····│·····└──┬--YFL054C ·····················│·····│········└──┬--YNL274C ·····················│·····│···········└──┬--YLR270W ·····················│·····│··············└──┬--YDR272W ·····················│·····│·················└──YHR104W ·····················│·····└──┬--YLR356W ·····················│········└──YBR241C ·····················└──┬--YDL204w ························└──┬--┬--┬--YBL015W ···························│··│··└──┬--YKL217W ···························│··│·····└──YMR107W ···························│··└──┬--YMR191W ···························│·····└──┬--YKL109W ···························│········└──YML054C ···························└──┬--YGL191W ······························└──┬--┬--┬--YGR243W ·································│··│··└──┬--YBL048W ·································│··│·····└──YGR236C ·································│··└──┬--YDR070c ·································│·····└──┬--YBR147W ·································│········└──┬--YNL134C ·································│···········└──YNL194C ·································└──┬--┬--┬--YBL064C ····································│··│··└──YGR043C ····································│··└──┬--┬--YDR171W ····································│·····│··└──YBL078C ····································│·····└──┬--YKL026C ····································│········└──┬--YOR215C ····································│···········└──┬--YFR033C ····································│··············└──YGR088W ····································└──┬--YER067w ·······································└──┬--YDR533C ··········································└──YOR178C
Hierarchical Tree
原文地址：https://www.cnblogs.com/lokwongho/p/9977753.html
时间： 2024-10-05 05:04:53