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Predicting Pathogen from RNAseq data

Create the model using hyperparameter tuning





In [1]:



    


from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV, StratifiedKFold
from xgboost import XGBClassifier
import pandas as pd
import numpy as np
import xgboost as xgb
import matplotlib
from matplotlib import pyplot
from sklearn.metrics import confusion_matrix, accuracy_score, classification_report

patient_groups=["control", "viral", "bacterial", "fungal"]
group_id = lambda name: patient_groups.index(name)

X = pd.DataFrame.from_csv("combineSV_WTcpmtable_v2.txt", sep="\s+").T
y = [group_id("bacterial")] * 29 \
    + [group_id("viral")] * 42 \
    + [group_id("fungal")] * 10 \
    + [group_id("control")] * 61
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)

print "Training set has %d samples. Testing set has %d samples." % (len(X_train), len(X_test))

model = XGBClassifier(objective="multi:softprob")
# Number of boosted trees to fit.
n_estimators = range(1, 11, 2)
# Maximum tree depth for base learners.
max_depth = range(2, 11, 2)
param_grid = dict(max_depth=max_depth, n_estimators=n_estimators)
kfold = StratifiedKFold(n_splits=2, shuffle=True, random_state=7)
grid_search = GridSearchCV(model, param_grid, scoring="neg_log_loss", 
                           n_jobs=1, cv=kfold, verbose=1)
grid_result = grid_search.fit(X_train, y_train)

# summarize results
print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))
means = grid_result.cv_results_['mean_test_score']
stds = grid_result.cv_results_['std_test_score']
params = grid_result.cv_results_['params']
for mean, stdev, param in zip(means, stds, params):
    print("%f (%f) with: %r" % (mean, stdev, param))

# plot results
%matplotlib inline

scores = np.array(means).reshape(len(max_depth), len(n_estimators))
for i, value in enumerate(max_depth):
    pyplot.plot(n_estimators, scores[i], label='depth: ' + str(value))
pyplot.legend()
pyplot.xlabel('n_estimators')
pyplot.ylabel('Log Loss')



    


















    






Training set has 99 samples. Testing set has 43 samples.
Fitting 2 folds for each of 25 candidates, totalling 50 fits










    






[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:   46.4s finished










    






Best: -1.313247 using {'n_estimators': 7, 'max_depth': 2}
-1.359561 (0.002301) with: {'n_estimators': 1, 'max_depth': 2}
-1.332849 (0.012278) with: {'n_estimators': 3, 'max_depth': 2}
-1.326635 (0.017019) with: {'n_estimators': 5, 'max_depth': 2}
-1.313247 (0.017527) with: {'n_estimators': 7, 'max_depth': 2}
-1.321541 (0.022684) with: {'n_estimators': 9, 'max_depth': 2}
-1.360918 (0.000564) with: {'n_estimators': 1, 'max_depth': 4}
-1.336042 (0.008165) with: {'n_estimators': 3, 'max_depth': 4}
-1.337598 (0.027781) with: {'n_estimators': 5, 'max_depth': 4}
-1.338183 (0.034502) with: {'n_estimators': 7, 'max_depth': 4}
-1.339095 (0.041565) with: {'n_estimators': 9, 'max_depth': 4}
-1.360918 (0.000564) with: {'n_estimators': 1, 'max_depth': 6}
-1.336042 (0.008165) with: {'n_estimators': 3, 'max_depth': 6}
-1.337598 (0.027781) with: {'n_estimators': 5, 'max_depth': 6}
-1.338183 (0.034502) with: {'n_estimators': 7, 'max_depth': 6}
-1.339095 (0.041565) with: {'n_estimators': 9, 'max_depth': 6}
-1.360918 (0.000564) with: {'n_estimators': 1, 'max_depth': 8}
-1.336042 (0.008165) with: {'n_estimators': 3, 'max_depth': 8}
-1.337598 (0.027781) with: {'n_estimators': 5, 'max_depth': 8}
-1.338183 (0.034502) with: {'n_estimators': 7, 'max_depth': 8}
-1.339095 (0.041565) with: {'n_estimators': 9, 'max_depth': 8}
-1.360918 (0.000564) with: {'n_estimators': 1, 'max_depth': 10}
-1.336042 (0.008165) with: {'n_estimators': 3, 'max_depth': 10}
-1.337598 (0.027781) with: {'n_estimators': 5, 'max_depth': 10}
-1.338183 (0.034502) with: {'n_estimators': 7, 'max_depth': 10}
-1.339095 (0.041565) with: {'n_estimators': 9, 'max_depth': 10}










    
Out[1]:








<matplotlib.text.Text at 0x111167350>










    
























In [2]:



    


%matplotlib inline
from learning_curves import plot_learning_curve
from sklearn.model_selection import ShuffleSplit
import matplotlib.pyplot as plt

best_estimator = XGBClassifier(**grid_search.best_params_)

title = "Learning Curves for xgboost"
cv = ShuffleSplit(n_splits=2, test_size=0.3, random_state=0)
plot_learning_curve(best_estimator, title, X, y, (0.2, 1.01), cv=cv, n_jobs=1)
plt.show()



    


















    






None

Make predictions based on the model





In [3]:



    


# make predictions
predicted = grid_result.predict(X_test)

print "Accuracy was %.2f%%\n" % (100 * accuracy_score(y_test, predicted))
print classification_report(y_test, predicted, target_names=patient_groups)
cm = confusion_matrix(y_test, predicted, labels=range(len(patient_groups)))
print "Confusion Matrix: rows = predictions, columns = actual\n"
row_format ="{:>15}" * (len(patient_groups)+1)
print row_format.format("", *patient_groups)
for disease, row in zip(patient_groups, cm):
    print row_format.format(disease, *row)



    


















    






Accuracy was 51.16%

             precision    recall  f1-score   support

    control       0.64      0.74      0.68        19
      viral       0.30      0.27      0.29        11
  bacterial       0.56      0.50      0.53        10
     fungal       0.00      0.00      0.00         3

avg / total       0.49      0.51      0.50        43

Confusion Matrix: rows = predictions, columns = actual

                       control          viral      bacterial         fungal
        control             14              2              3              0
          viral              6              3              1              1
      bacterial              1              3              5              1
         fungal              1              2              0              0

Review patients the model misclassified





In [4]:



    


probs = np.array(grid_result.predict_proba(X_test))
probs *= 100

d = {"Probability Control": probs[:, patient_groups.index("control")],
     "Probability Viral": probs[:, patient_groups.index("viral")],
     "Probability Bacterial": probs[:, patient_groups.index("bacterial")],
     "Predicted Class": [patient_groups[i] for i in grid_result.predict(X_test)],
     "Actual Class": [patient_groups[i] for i in y_test]}

patient_df = pd.DataFrame(d, index=X_test.index)
patient_df[patient_df["Predicted Class"] != patient_df["Actual Class"]]



    


















    
Out[4]:










  
    

      
      Actual Class
      Predicted Class
      Probability Bacterial
      Probability Control
      Probability Viral
    

  
  
    

      MNC.014
      viral
      control
      23.392109
      30.087852
      24.744411
    

    

      MNC.674
      viral
      control
      22.655106
      34.055008
      27.102434
    

    

      MNC.098
      viral
      control
      14.892682
      37.064793
      33.956825
    

    

      MN_223
      bacterial
      control
      22.889997
      29.844179
      25.616163
    

    

      MNC.292
      control
      bacterial
      29.759371
      23.411911
      27.948860
    

    

      MNC.131
      control
      bacterial
      31.754002
      25.190596
      24.783957
    

    

      MN_326
      control
      viral
      20.577374
      24.134228
      35.826050
    

    

      MNC.452.x
      bacterial
      viral
      19.016665
      29.888338
      33.108788
    

    

      MNC.412
      viral
      fungal
      13.519282
      23.623735
      22.788357
    

    

      MN_368
      control
      bacterial
      34.392048
      24.539768
      21.278799
    

    

      MNC.234
      viral
      bacterial
      27.746531
      24.541880
      25.473700
    

    

      MN_201
      fungal
      viral
      28.813448
      20.982773
      31.147867
    

    

      MNC.013
      viral
      control
      15.282658
      52.321552
      17.941254
    

    

      MN_137
      bacterial
      viral
      20.282084
      29.400450
      31.134409
    

    

      MNC.557
      bacterial
      viral
      19.837709
      25.516045
      36.179394
    

    

      MN_226
      fungal
      viral
      24.359613
      23.843870
      34.391167
    

    

      MN_283
      bacterial
      fungal
      29.151695
      20.800621
      19.847288
    

    

      MNC.012.y
      viral
      control
      28.730232
      45.267612
      13.496365
    

    

      MNC.091
      viral
      control
      16.422787
      42.907681
      23.612793
    

    

      MN_305
      fungal
      control
      25.740545
      31.418831
      27.724262
    

    

      MNC.294
      control
      viral
      18.772388
      28.390049
      35.082394
    

  




















In [5]:



    


from IPython.core.display import display, HTML

colname = "Importance"
df = pd.DataFrame(grid_result.best_estimator_.booster().get_score(importance_type="weight"),
                  index=[colname]).T
df = df[df[colname] > 0]
df = df.sort_values(by=[colname], ascending=False)

s="""
<h2>List of genes by importance</h2>
<p>Note: the NCBI link will open the target in a new window or tab.</p>
<table>
<tr><th>Gene</th><th>Importance</th><th>NCBI</th></tr>
"""

ncbi_url = lambda gene: "https://www.ncbi.nlm.nih.gov/gene/?term=%s[Gene Name] AND Human[Organism]" % (gene)

for index, row in df.iterrows():
    s += """
    <tr><td>%s</td><td>%d</td>
    <td><a target=\"_blank\" href=\"%s\">NCBI Search</a></td></tr>
    """ %(index, row[0], ncbi_url(index))
s += "</table>"

display(HTML(s))



    


















    








List of genes by importance


Note: the NCBI link will open the target in a new window or tab.




GeneImportanceNCBI


    
RHCE7
    NCBI Search

    
    
SDC44
    NCBI Search

    
    
PTGES34
    NCBI Search

    
    
PABPC14
    NCBI Search

    
    
MOBP4
    NCBI Search

    
    
BAZ1B4
    NCBI Search

    
    
CAMK14
    NCBI Search

    
    
LINC012474
    NCBI Search

    
    
ABAT3
    NCBI Search

    
    
CARD113
    NCBI Search

    
    
DSCAM3
    NCBI Search

    
    
A1CF2
    NCBI Search

    
    
PRF12
    NCBI Search

    
    
MUM12
    NCBI Search

    
    
LOC1019295672
    NCBI Search

    
    
WDR62
    NCBI Search

    
    
EEF1A12
    NCBI Search

    
    
ATP11B2
    NCBI Search

    
    
EIF3L1
    NCBI Search

    
    
ACVR11
    NCBI Search

    
    
TTL1
    NCBI Search

    
    
SOX51
    NCBI Search

    
    
ABCA51
    NCBI Search

    
    
RPSA1
    NCBI Search

    
    
RPL341
    NCBI Search

    
    
ABCA81
    NCBI Search

    
    
RBP51
    NCBI Search

    
    
ABI3BP1
    NCBI Search

    
    
ACP11
    NCBI Search

    
    
ADHFE11
    NCBI Search

    
    
FAM206A1
    NCBI Search

    
    
AHRR1
    NCBI Search

    
    
MAP3K81
    NCBI Search

    
    
MAP3K131
    NCBI Search

    
    
LUZP11
    NCBI Search

    
    
DNAJB121
    NCBI Search

    
    
LINC005751
    NCBI Search

    
    
JADE21
    NCBI Search

    
    
G3BP21
    NCBI Search

    
    
KLF131
    NCBI Search

    

















In [6]:



    


# Dump booster info
print grid_result.best_estimator_.base_score
print "\n".join(grid_result.best_estimator_.booster().get_dump())



    


















    






0.5
0:[PABPC1<2108.71] yes=1,no=2,missing=1
	1:[PTGES3<90.9317] yes=3,no=4,missing=3
		3:leaf=-0.0248649
		4:leaf=0.115789
	2:[MOBP<129.057] yes=5,no=6,missing=5
		5:leaf=0.164045
		6:leaf=-0.0352941

0:[CARD11<115.479] yes=1,no=2,missing=1
	1:[RPSA<3.21717] yes=3,no=4,missing=3
		3:leaf=0.0513274
		4:leaf=-0.0434783
	2:[ABCA5<20.3476] yes=5,no=6,missing=5
		5:leaf=0.157895
		6:leaf=0.0117647

0:[LINC01247<0.844941] yes=1,no=2,missing=1
	1:[LOC101929567<349.976] yes=3,no=4,missing=3
		3:leaf=-0.0540773
		4:leaf=0.0956522
	2:[CAMK1<10.0913] yes=5,no=6,missing=5
		5:leaf=0.163636
		6:leaf=-0.0482759

0:[RHCE<37.9593] yes=1,no=2,missing=1
	1:[ACP1<248.937] yes=3,no=4,missing=3
		3:leaf=-0.0647273
		4:leaf=0.0588235
	2:[A1CF<30.3216] yes=5,no=6,missing=5
		5:leaf=0.12
		6:leaf=0.0117647

0:[PABPC1<2108.71] yes=1,no=2,missing=1
	1:[PTGES3<90.9317] yes=3,no=4,missing=3
		3:leaf=-0.0240633
		4:leaf=0.103782
	2:[MOBP<129.057] yes=5,no=6,missing=5
		5:leaf=0.140908
		6:leaf=-0.0348951

0:[SDC4<5.7941] yes=1,no=2,missing=1
	1:[CARD11<75.8036] yes=3,no=4,missing=3
		3:leaf=0.0100998
		4:leaf=0.139593
	2:leaf=-0.0596555

0:[LINC01247<0.844941] yes=1,no=2,missing=1
	1:[WDR6<337.282] yes=3,no=4,missing=3
		3:leaf=-0.0527528
		4:leaf=0.0922695
	2:[CAMK1<10.0913] yes=5,no=6,missing=5
		5:leaf=0.143667
		6:leaf=-0.047482

0:[RHCE<37.9593] yes=1,no=2,missing=1
	1:[LINC00575<94.8312] yes=3,no=4,missing=3
		3:leaf=-0.0634419
		4:leaf=0.0582775
	2:[ABAT<49.9433] yes=5,no=6,missing=5
		5:leaf=0.113719
		6:leaf=0.0103783

0:[PABPC1<2108.71] yes=1,no=2,missing=1
	1:[PTGES3<90.9317] yes=3,no=4,missing=3
		3:leaf=-0.0233558
		4:leaf=0.0940393
	2:[MOBP<129.057] yes=5,no=6,missing=5
		5:leaf=0.124608
		6:leaf=-0.0345373

0:[SDC4<5.7941] yes=1,no=2,missing=1
	1:[CARD11<75.8036] yes=3,no=4,missing=3
		3:leaf=0.00911136
		4:leaf=0.124169
	2:leaf=-0.0584118

0:[LINC01247<0.844941] yes=1,no=2,missing=1
	1:[WDR6<337.282] yes=3,no=4,missing=3
		3:leaf=-0.0513965
		4:leaf=0.08575
	2:[CAMK1<10.0913] yes=5,no=6,missing=5
		5:leaf=0.128376
		6:leaf=-0.0465911

0:[RHCE<37.9593] yes=1,no=2,missing=1
	1:[ADHFE1<111.115] yes=3,no=4,missing=3
		3:leaf=-0.0622423
		4:leaf=0.0565825
	2:[ACVR1<48.1243] yes=5,no=6,missing=5
		5:leaf=0.106738
		6:leaf=0.00958404

0:[PABPC1<2108.71] yes=1,no=2,missing=1
	1:[RPL34<68.1889] yes=3,no=4,missing=3
		3:leaf=-0.028314
		4:leaf=0.0712867
	2:[MOBP<129.057] yes=5,no=6,missing=5
		5:leaf=0.112101
		6:leaf=-0.0343725

0:[SDC4<5.7941] yes=1,no=2,missing=1
	1:[MUM1<27.7506] yes=3,no=4,missing=3
		3:leaf=0.0582352
		4:leaf=-0.055976
	2:leaf=-0.0572051

0:[LINC01247<0.844941] yes=1,no=2,missing=1
	1:[LOC101929567<349.976] yes=3,no=4,missing=3
		3:leaf=-0.0501281
		4:leaf=0.0805816
	2:[CAMK1<10.0913] yes=5,no=6,missing=5
		5:leaf=0.116117
		6:leaf=-0.0456158

0:[RHCE<37.9593] yes=1,no=2,missing=1
	1:[FAM206A<119.303] yes=3,no=4,missing=3
		3:leaf=-0.06116
		4:leaf=0.0554058
	2:[AHRR<53.1435] yes=5,no=6,missing=5
		5:leaf=0.100779
		6:leaf=0.00868072

0:[EEF1A1<5251.18] yes=1,no=2,missing=1
	1:[PTGES3<78.3124] yes=3,no=4,missing=3
		3:leaf=-0.0362949
		4:leaf=0.0942908
	2:[PRF1<34.6688] yes=5,no=6,missing=5
		5:leaf=0.115423
		6:leaf=-0.0203355

0:[JADE2<233.487] yes=1,no=2,missing=1
	1:[MAP3K13<174.184] yes=3,no=4,missing=3
		3:leaf=-0.0341893
		4:leaf=0.057816
	2:[ABCA8<7.8292] yes=5,no=6,missing=5
		5:leaf=0.122936
		6:leaf=-0.0378523

0:[DSCAM<6.96523] yes=1,no=2,missing=1
	1:[ATP11B<470.176] yes=3,no=4,missing=3
		3:leaf=-0.0560397
		4:leaf=0.0632467
	2:[BAZ1B<102.433] yes=5,no=6,missing=5
		5:leaf=0.11236
		6:leaf=-0.0508661

0:[RHCE<37.9593] yes=1,no=2,missing=1
	1:[MAP3K8<342.627] yes=3,no=4,missing=3
		3:leaf=-0.0601231
		4:leaf=0.0542153
	2:[ABAT<49.9433] yes=5,no=6,missing=5
		5:leaf=0.0955389
		6:leaf=0.00780039

0:[EIF3L<182.278] yes=1,no=2,missing=1
	1:[LUZP1<122.993] yes=3,no=4,missing=3
		3:leaf=-0.0345764
		4:leaf=0.0714058
	2:[SOX5<17.5] yes=5,no=6,missing=5
		5:leaf=0.101701
		6:leaf=-0.0322464

0:[SDC4<5.7941] yes=1,no=2,missing=1
	1:[MUM1<27.7506] yes=3,no=4,missing=3
		3:leaf=0.0497779
		4:leaf=-0.0539235
	2:leaf=-0.055282

0:[DSCAM<6.96523] yes=1,no=2,missing=1
	1:[TTL<144.481] yes=3,no=4,missing=3
		3:leaf=-0.0550153
		4:leaf=0.062154
	2:[BAZ1B<102.433] yes=5,no=6,missing=5
		5:leaf=0.103543
		6:leaf=-0.0496796

0:[RHCE<37.9593] yes=1,no=2,missing=1
	1:[RBP5<52.6298] yes=3,no=4,missing=3
		3:leaf=-0.0591756
		4:leaf=0.052677
	2:[A1CF<30.3216] yes=5,no=6,missing=5
		5:leaf=0.0903729
		6:leaf=0.00694476

0:[EEF1A1<5251.18] yes=1,no=2,missing=1
	1:[BAZ1B<112.009] yes=3,no=4,missing=3
		3:leaf=-0.0425257
		4:leaf=0.0633252
	2:[PRF1<34.6688] yes=5,no=6,missing=5
		5:leaf=0.0984699
		6:leaf=-0.0201828

0:[G3BP2<323.801] yes=1,no=2,missing=1
	1:[KLF13<11.0899] yes=3,no=4,missing=3
		3:leaf=0.0366868
		4:leaf=-0.0446938
	2:[ABI3BP<362.729] yes=5,no=6,missing=5
		5:leaf=0.107516
		6:leaf=0.00433634

0:[DSCAM<6.96523] yes=1,no=2,missing=1
	1:[ATP11B<470.176] yes=3,no=4,missing=3
		3:leaf=-0.0540243
		4:leaf=0.0592059
	2:[BAZ1B<102.433] yes=5,no=6,missing=5
		5:leaf=0.0955973
		6:leaf=-0.0486365

0:[RHCE<37.9593] yes=1,no=2,missing=1
	1:[DNAJB12<136.354] yes=3,no=4,missing=3
		3:leaf=-0.0582741
		4:leaf=0.0512088
	2:[ABAT<49.9433] yes=5,no=6,missing=5
		5:leaf=0.0858952
		6:leaf=0.00657047


















In [7]:



    


idf = X_test.loc[:, df.index.tolist()].copy()
idf["group"] = [patient_groups[idx] for idx in y_test]
idf



    


















    
Out[7]:










  
    

      
      RHCE
      SDC4
      PTGES3
      PABPC1
      MOBP
      BAZ1B
      CAMK1
      LINC01247
      ABAT
      CARD11
      ...
      AHRR
      MAP3K8
      MAP3K13
      LUZP1
      DNAJB12
      LINC00575
      JADE2
      G3BP2
      KLF13
      group
    

  
  
    

      MNC.014
      30.634908
      56.163999
      51.058181
      546.322535
      56.163999
      142.962906
      45.952363
      0.000000
      71.481453
      132.751270
      ...
      71.481453
      71.481453
      148.068724
      142.962906
      0.000000
      0.000000
      265.502540
      25.529090
      86.798907
      viral
    

    

      MNC.674
      0.000000
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      0.000000
      1428.182066
      196.238757
      261.651676
      0.000000
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      ...
      0.000000
      0.000000
      141.727991
      0.000000
      0.000000
      0.000000
      0.000000
      152.630144
      0.000000
      viral
    

    

      MN_363
      14.414103
      14.414103
      0.000000
      79.277565
      100.898719
      50.449360
      64.863462
      57.656411
      79.277565
      108.105771
      ...
      100.898719
      28.828206
      317.110261
      122.519873
      7.207051
      14.414103
      79.277565
      93.691668
      100.898719
      bacterial
    

    

      MNC.098
      14.078360
      0.000000
      211.175402
      1956.892061
      0.000000
      0.000000
      0.000000
      0.000000
      140.783602
      126.705241
      ...
      0.000000
      0.000000
      126.705241
      225.253762
      0.000000
      0.000000
      84.470161
      0.000000
      126.705241
      viral
    

    

      MNC.112
      10.712182
      60.702362
      32.136545
      1799.646498
      82.126725
      114.263270
      0.000000
      10.712182
      135.687633
      28.565817
      ...
      53.560908
      35.707272
      174.965632
      110.692543
      67.843816
      21.424363
      53.560908
      189.248540
      89.268179
      bacterial
    

    

      MN_223
      0.000000
      19.775469
      17.797922
      1210.258722
      0.000000
      148.316020
      1.977547
      0.000000
      21.753016
      5.932641
      ...
      0.000000
      288.721852
      543.825406
      35.595845
      15.820375
      0.000000
      0.000000
      96.899800
      110.742628
      bacterial
    

    

      MNC.292
      0.000000
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      0.000000
      1231.890890
      0.000000
      0.000000
      0.000000
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      ...
      0.000000
      0.000000
      94.277364
      0.000000
      0.000000
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      0.000000
      0.000000
      0.000000
      control
    

    

      MNC.691
      0.000000
      0.000000
      640.949201
      1043.405676
      0.000000
      462.079657
      298.115907
      0.000000
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      ...
      0.000000
      0.000000
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      0.000000
      0.000000
      0.000000
      0.000000
      0.000000
      119.246363
      control
    

    

      MN_386
      0.000000
      0.000000
      471.343772
      736.474643
      88.376957
      206.212900
      132.565436
      0.000000
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      ...
      0.000000
      0.000000
      73.647464
      0.000000
      0.000000
      0.000000
      0.000000
      0.000000
      132.565436
      control
    

    

      MNC.151
      0.000000
      0.000000
      0.000000
      2303.907381
      0.000000
      0.000000
      0.000000
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      0.000000
      ...
      0.000000
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      0.000000
      0.000000
      0.000000
      0.000000
      0.000000
      control
    

    

      MNC.114
      0.000000
      80.468865
      93.880343
      2802.998806
      0.000000
      80.468865
      0.000000
      0.000000
      254.818073
      0.000000
      ...
      0.000000
      0.000000
      93.880343
      0.000000
      0.000000
      0.000000
      80.468865
      0.000000
      590.105012
      control
    

    

      MNC.555
      0.000000
      0.000000
      25.950098
      3775.739253
      0.000000
      311.401176
      0.000000
      0.000000
      0.000000
      155.700588
      ...
      0.000000
      116.775441
      142.725539
      64.875245
      129.750490
      0.000000
      51.900196
      142.725539
      220.575833
      control
    

    

      MN_224
      24.289826
      18.217370
      18.217370
      85.014392
      60.724566
      48.579652
      12.144913
      6.072457
      54.652109
      24.289826
      ...
      36.434739
      48.579652
      115.376674
      42.507196
      18.217370
      12.144913
      206.463523
      72.869479
      0.000000
      bacterial
    

    

      MNC.131
      0.000000
      63.443725
      19.033118
      139.576196
      145.920568
      44.410608
      82.476843
      82.476843
      38.066235
      120.543078
      ...
      19.033118
      12.688745
      260.119274
      145.920568
      69.788098
      0.000000
      31.721863
      0.000000
      6.344373
      control
    

    

      MNC.676
      0.000000
      0.000000
      0.000000
      4000.111114
      0.000000
      527.792439
      0.000000
      0.000000
      0.000000
      0.000000
      ...
      0.000000
      0.000000
      0.000000
      0.000000
      0.000000
      0.000000
      0.000000
      208.339121
      0.000000
      control
    

    

      MN_171
      0.000000
      0.000000
      0.000000
      532.362304
      0.000000
      0.000000
      0.000000
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      ...
      0.000000
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      0.000000
      1160.549824
      0.000000
      0.000000
      0.000000
      0.000000
      819.837949
      viral
    

    

      MN_326
      0.000000
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      0.000000
      0.000000
      0.000000
      16.296996
      0.000000
      0.000000
      0.000000
      0.000000
      ...
      0.000000
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      0.000000
      0.000000
      0.000000
      0.000000
      0.000000
      control
    

    

      MN_366
      0.000000
      226.900290
      54.023879
      4393.942122
      0.000000
      857.178873
      0.000000
      0.000000
      18.007960
      0.000000
      ...
      50.422287
      39.617511
      277.322577
      169.274819
      54.023879
      0.000000
      140.462084
      226.900290
      234.103474
      control
    

    

      MNC.454
      0.000000
      0.000000
      0.000000
      1563.166122
      0.000000
      0.000000
      0.000000
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      ...
      0.000000
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      0.000000
      0.000000
      0.000000
      0.000000
      620.098957
      0.000000
      2454.558373
      control
    

    

      MNC.452.x
      0.000000
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      0.000000
      0.000000
      0.000000
      0.000000
      0.000000
      0.000000
      0.000000
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      ...
      0.000000
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      0.000000
      0.000000
      0.000000
      0.000000
      0.000000
      bacterial
    

    

      MNC.412
      221.307930
      0.000000
      0.000000
      2021.949723
      0.000000
      0.000000
      0.000000
      0.000000
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      0.000000
      ...
      0.000000
      0.000000
      0.000000
      291.724089
      40.237805
      0.000000
      50.297257
      734.339949
      0.000000
      viral
    

    

      MN_368
      0.000000
      11.914336
      23.828672
      399.130253
      679.117148
      23.828672
      83.400351
      5.957168
      59.571680
      5.957168
      ...
      95.314687
      101.271855
      256.158222
      29.785840
      95.314687
      0.000000
      238.286718
      119.143359
      29.785840
      control
    

    

      MNC.234
      0.000000
      93.245310
      0.000000
      0.000000
      0.000000
      0.000000
      0.000000
      0.000000
      223.788743
      0.000000
      ...
      0.000000
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      18.649062
      0.000000
      0.000000
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      0.000000
      0.000000
      viral
    

    

      MNC.473
      0.000000
      31.125619
      27.234917
      953.222085
      54.469833
      81.704750
      167.300203
      0.000000
      23.344214
      19.453512
      ...
      0.000000
      38.907024
      120.611774
      237.332846
      50.579131
      0.000000
      0.000000
      105.048964
      159.518798
      bacterial
    

    

      MNC.331
      0.000000
      0.000000
      978.430064
      2479.430732
      744.941072
      0.000000
      0.000000
      0.000000
      389.148321
      0.000000
      ...
      0.000000
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      567.044696
      0.000000
      0.000000
      0.000000
      0.000000
      0.000000
      0.000000
      viral
    

    

      MN_385
      0.000000
      19.088705
      19.088705
      515.395041
      1985.225342
      146.346740
      0.000000
      25.451607
      133.620936
      12.725803
      ...
      12.725803
      31.814509
      95.443526
      50.903214
      76.354821
      25.451607
      25.451607
      120.895133
      38.177410
      bacterial
    

    

      MN_201
      0.000000
      0.000000
      0.000000
      0.000000
      0.000000
      0.000000
      0.000000
      0.000000
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      ...
      0.000000
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      0.000000
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      0.000000
      0.000000
      0.000000
      0.000000
      0.000000
      fungal
    

    

      MNC.276
      0.000000
      59.978408
      19.992803
      2199.208285
      19.992803
      59.978408
      19.992803
      19.992803
      199.928026
      259.906434
      ...
      99.964013
      0.000000
      99.964013
      99.964013
      0.000000
      79.971210
      159.942421
      139.949618
      279.899236
      control
    

    

      MNC.013
      0.000000
      0.000000
      118.512106
      2282.646221
      0.000000
      108.206706
      5.152700
      0.000000
      0.000000
      41.221602
      ...
      0.000000
      0.000000
      15.458101
      170.039109
      15.458101
      0.000000
      139.122907
      273.093114
      334.925518
      viral
    

    

      MNC.554
      0.000000
      2.925773
      99.476287
      1559.437081
      0.000000
      90.698967
      0.000000
      0.000000
      0.000000
      0.000000
      ...
      0.000000
      11.703093
      122.882472
      5.851546
      17.554639
      0.000000
      40.960824
      175.546388
      134.585564
      control
    

    

      MN_137
      0.000000
      0.000000
      0.000000
      405.323245
      0.000000
      135.107748
      67.553874
      0.000000
      67.553874
      0.000000
      ...
      67.553874
      202.661623
      337.769371
      270.215497
      0.000000
      0.000000
      0.000000
      0.000000
      0.000000
      bacterial
    

    

      MNC.557
      0.000000
      0.000000
      0.000000
      852.636067
      0.000000
      900.004737
      15.789557
      0.000000
      110.526898
      15.789557
      ...
      0.000000
      110.526898
      0.000000
      0.000000
      0.000000
      0.000000
      236.843352
      315.791136
      0.000000
      bacterial
    

    

      MN_226
      0.000000
      0.000000
      0.000000
      0.000000
      267.892101
      0.000000
      0.000000
      0.000000
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      0.000000
      ...
      0.000000
      0.000000
      0.000000
      0.000000
      0.000000
      0.000000
      0.000000
      1174.603829
      556.391287
      fungal
    

    

      MN_283
      43.127243
      43.127243
      34.501794
      129.381728
      120.756279
      77.629037
      34.501794
      43.127243
      60.378140
      112.130831
      ...
      138.007176
      8.625449
      215.636213
      69.003588
      34.501794
      17.250897
      69.003588
      34.501794
      120.756279
      bacterial
    

    

      MNC.012.y
      7.700512
      7.700512
      0.000000
      2791.435491
      0.000000
      211.764072
      3.850256
      23.101535
      38.502558
      69.304605
      ...
      7.700512
      53.903582
      96.256396
      42.352814
      115.507675
      0.000000
      34.652303
      300.319956
      88.555885
      viral
    

    

      MNC.172
      0.000000
      31.886738
      49.601593
      2724.544640
      0.000000
      88.574273
      0.000000
      0.000000
      0.000000
      0.000000
      ...
      0.000000
      74.402389
      251.550936
      81.488331
      0.000000
      0.000000
      120.461011
      336.582238
      109.832099
      control
    

    

      MNC.091
      0.000000
      151.925177
      0.000000
      2791.625125
      0.000000
      294.355030
      0.000000
      0.000000
      47.476618
      170.915824
      ...
      0.000000
      170.915824
      28.485971
      0.000000
      161.420500
      0.000000
      588.710060
      161.420500
      265.869059
      viral
    

    

      MNC.115
      0.000000
      54.600268
      37.050182
      1961.709612
      5.850029
      140.400688
      1.950010
      0.000000
      42.900210
      3.900019
      ...
      0.000000
      85.800420
      185.250908
      48.750239
      27.300134
      0.000000
      19.500096
      146.250717
      87.750430
      control
    

    

      MNC.071
      0.000000
      0.000000
      0.000000
      384.221311
      0.000000
      0.000000
      0.000000
      0.000000
      102.459016
      315.915301
      ...
      0.000000
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      0.000000
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      0.000000
      0.000000
      0.000000
      614.754098
      0.000000
      viral
    

    

      MN_305
      0.000000
      0.000000
      190.882194
      159.068495
      174.975344
      95.441097
      63.627398
      0.000000
      0.000000
      0.000000
      ...
      0.000000
      238.602742
      254.509592
      0.000000
      0.000000
      0.000000
      334.043839
      270.416441
      0.000000
      fungal
    

    

      MNC.294
      0.000000
      0.000000
      0.000000
      3896.217350
      1782.126321
      0.000000
      0.000000
      0.000000
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      0.000000
      ...
      0.000000
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      0.000000
      0.000000
      0.000000
      0.000000
      0.000000
      5084.301564
      0.000000
      control
    

    

      MNC.111
      0.000000
      0.000000
      0.000000
      3644.501279
      0.000000
      237.486299
      0.000000
      0.000000
      0.000000
      0.000000
      ...
      0.000000
      82.206796
      118.743149
      0.000000
      374.497625
      0.000000
      365.363537
      0.000000
      137.011326
      control
    

    

      MNC.054
      0.000000
      8.651455
      56.234454
      2054.720450
      0.000000
      69.211636
      34.605818
      0.000000
      0.000000
      43.257273
      ...
      0.000000
      190.332000
      129.771818
      0.000000
      43.257273
      0.000000
      30.280091
      103.817454
      134.097545
      control
    

  



43 rows × 41 columns

Content source: massie/notebooks

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