In [1]:

    

from skbio.stats.composition import ancom
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline


    

    




---------------------------------------------------------------------------
ImportError                               Traceback (most recent call last)
<ipython-input-1-b7c963e5dd2d> in <module>()
----> 1 from skbio.stats.composition import ancom
      2 import pandas as pd
      3 import matplotlib.pyplot as plt
      4 import seaborn as sns
      5 get_ipython().magic(u'matplotlib inline')

ImportError: cannot import name ancom

In [2]:

    

table = pd.read_csv('vibrio_biom.csv', index_col=0)
mapping = pd.read_csv('vibrio_mapping.csv', index_col=0)


    

In [4]:

    

table+1


    

    
Out[4]:






  

    

      

      
VF_0001
      
VF_0002
      
VF_0003
      
VF_0004
      
VF_0005
      
VF_0006
      
VF_0007
      
VF_0008
      
VF_0009
      
VF_0010
      
...
      
VF_B0045
      
VF_B0046
      
VF_B0047
      
VF_B0048
      
VF_B0050
      
VF_B0051
      
VF_B0052
      
VF_B0053
      
VF_B0054
      
VF_B0055
    
  
  

    

      
Plk1
      
19.491139
      
277.545263
      
299.488082
      
23.025002
      
1181.063429
      
210.237516
      
106.769318
      
1353.893946
      
33.133491
      
56.226870
      
...
      
52.528642
      
24.586476
      
9.793564
      
5.026959
      
68.718662
      
19.819871
      
20.395151
      
19.162408
      
6.588433
      
57.213064
    
    

      
Plk2
      
12.628665
      
244.190767
      
686.513385
      
71.349809
      
1446.710243
      
69.038522
      
36.391588
      
228.083983
      
85.361989
      
98.724119
      
...
      
14.578813
      
5.405892
      
1.938960
      
1.866733
      
13.423170
      
4.900297
      
7.933862
      
8.872823
      
3.022376
      
45.997876
    
    

      
Plk3
      
8.939793
      
272.813853
      
876.300154
      
89.640346
      
938.205728
      
89.826435
      
42.932032
      
297.253528
      
94.292568
      
104.899636
      
...
      
11.110830
      
6.954845
      
2.984948
      
2.550741
      
16.383349
      
4.907867
      
3.667274
      
8.505586
      
3.419156
      
42.994062
    
    

      
Swt1
      
46.856251
      
192.837907
      
1519.114434
      
135.132496
      
1250.138492
      
11.190278
      
5.087960
      
16.107680
      
261.326055
      
256.171670
      
...
      
17.648070
      
57.698233
      
22.743209
      
13.263881
      
57.816724
      
12.908406
      
15.396730
      
14.982009
      
12.256702
      
23.809634
    
    

      
Swt2
      
52.073162
      
251.429613
      
684.695880
      
51.941530
      
1071.825180
      
8.766280
      
3.237742
      
18.243774
      
196.868208
      
173.042841
      
...
      
8.173936
      
17.388167
      
7.844857
      
5.607115
      
33.776333
      
6.462722
      
9.424439
      
7.318329
      
4.422428
      
11.662181
    
    

      
Swt3
      
44.505061
      
225.382080
      
781.253809
      
60.346578
      
743.423322
      
9.748300
      
3.600846
      
19.678803
      
236.494786
      
196.063452
      
...
      
8.802538
      
18.496601
      
6.911014
      
4.783049
      
31.500831
      
8.093216
      
10.694062
      
8.802538
      
4.073727
      
11.639825
    
    

      
Vnt1
      
1.000000
      
70.778903
      
646.454849
      
120.205625
      
83.862447
      
123.113080
      
128.927988
      
2667.135572
      
107.122081
      
105.668354
      
...
      
1.000000
      
66.417721
      
1.000000
      
1.000000
      
130.381715
      
22.805907
      
40.250633
      
53.334177
      
25.713361
      
112.936990
    
    

      
Vnt2
      
17.942199
      
185.018346
      
568.433340
      
83.886450
      
1088.689173
      
112.818513
      
143.314471
      
2107.306303
      
307.002178
      
261.909864
      
...
      
5.431037
      
5.431037
      
2.824545
      
1.260649
      
11.947267
      
5.170387
      
10.122723
      
7.255581
      
3.085194
      
27.064921
    
    

      
Vnt3
      
15.060796
      
199.555488
      
347.406891
      
70.593840
      
1044.765578
      
94.880670
      
78.689450
      
1286.781709
      
215.178595
      
200.549685
      
...
      
5.544904
      
3.698537
      
1.710141
      
1.284056
      
15.486881
      
5.402876
      
5.686932
      
7.533299
      
2.136226
      
29.973762
    
  

9 rows × 3692 columns

In [5]:

    

res = ancom(table+1, mapping['treatment'])


    

In [6]:

    

res.to_csv('vibro_results.csv')


    

In [ ]: