In [1]:
import pandas as pd
import seaborn as sns
import matplotlib.pylab as plt
import numpy as np
In [2]:
#Positionwise carbon
#df=pd.read_excel("aa_properties.xlsx")
# H-wise Carbon 
df=pd.read_excel("aa_properties2.xlsx")
# identity
#df=pd.read_excel("aa_properties3.xlsx")
df
Out[2]:
.dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; }
aa CA-Gly Pro-MC Carboxyl Amide His Trp Phe-Tyr OH-Tyr CH2 CH CH3 OH SH S NH3 Arg MC
0 A 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 4
1 C 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 4
2 D 0 0 3 0 0 0 0 0 1 0 0 0 0 0 0 0 4
3 E 0 0 3 0 0 0 0 0 2 0 0 0 0 0 0 0 4
4 F 0 0 0 0 0 0 6 0 1 0 0 0 0 0 0 0 4
5 G 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3
6 H 0 0 0 0 5 0 0 0 1 0 0 0 0 0 0 0 4
7 I 0 0 0 0 0 0 0 0 1 1 2 0 0 0 0 0 4
8 K 0 0 0 0 0 0 0 0 4 0 0 0 0 0 1 0 4
9 L 0 0 0 0 0 0 0 0 1 1 2 0 0 0 0 0 4
10 M 0 0 0 0 0 0 0 0 2 0 1 0 0 1 0 0 4
11 N 0 0 0 3 0 0 0 0 1 0 0 0 0 0 0 0 4
12 P 0 2 0 0 0 0 0 0 3 0 0 0 0 0 0 0 2
13 Q 0 0 0 3 0 0 0 0 2 0 0 0 0 0 0 0 4
14 R 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 4 4
15 S 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 4
16 T 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 4
17 Y 0 0 0 0 0 0 6 1 1 0 0 0 0 0 0 0 4
18 V 0 0 0 0 0 0 0 0 0 1 2 0 0 0 0 0 4
19 W 0 0 0 0 0 9 0 0 1 0 0 0 0 0 0 0 4
In [3]:
def peptrans(peptide):
    rows=[]
    for l in peptide:
        rows.append(df[df.aa==l])
    return pd.concat(rows).sum()
In [4]:
pf=pd.read_excel("Clean_results_2_2019.xlsx")

/home/gergely/anaconda3/lib/python3.7/site-packages/openpyxl/worksheet/_read_only.py:79: UserWarning: Unknown extension is not supported and will be removed
  for idx, row in parser.parse():
/home/gergely/anaconda3/lib/python3.7/site-packages/openpyxl/worksheet/_read_only.py:79: UserWarning: Conditional Formatting extension is not supported and will be removed
  for idx, row in parser.parse():
In [5]:
transpf=pf['Peptide'].apply(peptrans)
In [6]:
mergepf = pd.concat([pf, transpf], axis=1, sort=False)
In [7]:
mergepf["Prot_pep"] = mergepf["Protein"].map(str) +"_"+ mergepf["Peptide"]
mergepf["Prot_res_pep"] = mergepf["Protein"].map(str) +"_"+ mergepf["Residue number"].map(str)+"_"+ mergepf["Peptide"]
mergepf=mergepf.set_index('Prot_res_pep')
mergepf=mergepf.rename(columns={u'Survivin, 1 µg/ml': u'Fluorescence'})
In [38]:
%matplotlib

predict=clf.predict(X)
def tollog(number):
    if number==0:
        return 0
    return np.log(number)
mergepf=mergepf.rename(columns={u'Survivin, 1 µg/ml': u'Fluorescence'})
row_colors= mergepf[u'Fluorescence'].map(tollog)/np.max(mergepf[u'Fluorescence'].map(tollog))
row_colors= row_colors.map(str)
row_colors= pd.DataFrame(row_colors)
stuff={'Prediction': np.where(predict==0,'k','c')}

stuff=np.where(predict==0,'k','c')
row_colors['Prediction']=stuff

g=sns.clustermap(mergepf[[u'CA-Gly', u'Pro-MC', 
         u'Carboxyl', u'Amide',
         u'His', u'Trp', u'Phe-Tyr', u'OH-Tyr', u'CH2', u'CH', u'CH3',
         u'OH', u'SH', u'S', u'NH3', u'Arg', u'MC']],z_score=1, method='ward', row_colors=row_colors, yticklabels=1, col_cluster=True)


plt.subplots_adjust(top=0.895,
        bottom=0.11,
        left=0.1,
        right=0.735,
        hspace=0.2,
        wspace=0.2)

Using matplotlib backend: Qt5Agg

/home/gergely/anaconda3/lib/python3.7/site-packages/seaborn/matrix.py:659: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.
  warnings.warn(msg)
In [11]:
%matplotlib qt
from mpl_toolkits.axes_grid.inset_locator import (inset_axes, InsetPosition,
                                                  mark_inset)

f,ax=plt.subplots(1)
ax.hist(mergepf[(mergepf[u'Survivin, 1 µg/ml']<70000)][u'Survivin, 1 µg/ml'],density=True, histtype='step',
                           cumulative=True, bins=1000)
ax.set_xlabel('Fluorescence intensity (a.u.)')
ax.set_ylabel('Cumulative fraction')
ax2 = plt.axes([0,0,1,1])
ip = InsetPosition(ax, [0.4,0.2,0.5,0.5])
ax2.set_axes_locator(ip)

ax2.hist(mergepf[(mergepf[u'Survivin, 1 µg/ml']<70000)][u'Survivin, 1 µg/ml'],density=True, histtype='step',
                           cumulative=True, bins=1000)
ax2.set_xlim([0,5000])
ax2.set_xlabel('Fluorescence intensity (a.u.)')
ax2.set_ylabel('Cumulative fraction')

<ipython-input-11-c423ce7ad724>:2: MatplotlibDeprecationWarning: 
The mpl_toolkits.axes_grid module was deprecated in Matplotlib 2.1 and will be removed two minor releases later. Use mpl_toolkits.axes_grid1 and mpl_toolkits.axisartist, which provide the same functionality instead.
  from mpl_toolkits.axes_grid.inset_locator import (inset_axes, InsetPosition,
Out[11]:
Text(0, 0.5, 'Cumulative fraction')
In [36]:
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.gaussian_process import GaussianProcessClassifier
from sklearn.gaussian_process.kernels import RBF
from sklearn import datasets, svm, metrics
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, MinMaxScaler

clf = MLPClassifier(hidden_layer_sizes=(2,), random_state=1, max_iter=10000)


X = np.array(mergepf[[u'CA-Gly', u'Pro-MC', 
         u'Carboxyl', u'Amide',
         u'His', u'Trp', u'Phe-Tyr', u'OH-Tyr', u'CH2', u'CH', u'CH3',
#         u'OH', u'SH', u'S', u'NH3', u'Arg', u'MC']])[1::3]
         u'OH', u'SH', u'S', u'NH3', u'Arg', u'MC']])

Y=np.array(mergepf[u'Fluorescence']>0)
#Y=np.array(mergepf[u'Fluorescence']>0)[1::3]

Y=np.array(mergepf[u'Fluorescence']>0)
X = np.array(mergepf[[u'CA-Gly', u'Pro-MC', 
         u'Carboxyl', u'Amide',
         u'His', u'Trp', u'Phe-Tyr', u'OH-Tyr', u'CH2', u'CH', u'CH3',
         u'OH', u'SH', u'S', u'NH3', u'Arg', u'MC']])


#X = np.array(mergepf[(mergepf[1::3][u'Fluorescence']>100)|(mergepf[u'Fluorescence']==0)][[u'CA-Gly', u'Pro-MC', 
#         u'Carboxyl', u'Amide',
#         u'His', u'Trp', u'Phe-Tyr', u'OH-Tyr', u'CH2', u'CH', u'CH3',
#         u'OH', u'SH', u'S', u'NH3', u'Arg', u'MC']])

#X = np.array(mergepf[(mergepf[u'Fluorescence']>100)|(mergepf[u'Fluorescence']==0)][[u'CA-Gly', u'Pro-MC', 
#         u'Carboxyl', u'Amide',
#         u'His', u'Trp', u'Phe-Tyr', u'OH-Tyr', u'CH2', u'CH', u'CH3',
#         u'OH', u'SH', u'S', u'NH3', u'Arg', u'MC']])


X = StandardScaler().fit_transform(X)


X_train, X_test, y_train, y_test = train_test_split(
    X, Y, test_size=0.5, shuffle=True)

#    X, np.array(mergepf[(mergepf[u'Fluorescence']>100)|(mergepf[u'Fluorescence']==0)][u'Fluorescence']>100), test_size=0.5, shuffle=True)
#    X, np.array(mergepf[1::3][(mergepf[u'Fluorescence']>100)|(mergepf[u'Fluorescence']==0)][u'Fluorescence']>100), test_size=0.5, shuffle=True)
clf.fit(X_train, y_train)
predict=clf.predict(X_test)
predict
Out[36]:
array([ True,  True, False, ...,  True, False,  True])
In [37]:
disp = metrics.plot_confusion_matrix(clf, X_test, y_test)
disp.figure_.suptitle("Confusion Matrix")
print(f"Confusion matrix:\n{disp.confusion_matrix}")

plt.show()

Confusion matrix:
[[1507  210]
 [ 299  628]]

/home/gergely/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.
  warnings.warn(msg, category=FutureWarning)
No description has been provided for this image
In [38]:
print(f"Classification report for classifier {clf}:\n"
      f"{metrics.classification_report(y_test, predict)}\n")

Classification report for classifier MLPClassifier(hidden_layer_sizes=(2,), max_iter=10000, random_state=1):
              precision    recall  f1-score   support

       False       0.83      0.88      0.86      1717
        True       0.75      0.68      0.71       927

    accuracy                           0.81      2644
   macro avg       0.79      0.78      0.78      2644
weighted avg       0.80      0.81      0.81      2644