import pandas as pd
import numpy as np

integrins = pd.read_excel("/Users/reneewang/Downloads/gtex_integrin_7_organs.xlsx")
integrins

brain_integrins = integrins[integrins['primary_site'] == 'Brain']
brain_integrins

# Import the required libraries
import matplotlib.pyplot as plt
import seaborn as sns

# violin plot for all the genes of the brain
plt.figure(figsize = (16, 6))
sns.violinplot(data = brain_integrins)
plt.title("Integrin Genes of the Brain")
plt.xlabel("Integrin Genes")
plt.ylabel("Gene Expression Levels")
plt.show()

lung_integrins = integrins[integrins['primary_site'] == 'Lung']
lung_integrins

#violin plot for all the genes of the lung
plt.figure(figsize = (16, 6))
sns.violinplot(data = lung_integrins)
plt.title("Integrin Genes of the Lung")
plt.xlabel("Integrin Genes")
plt.ylabel("Gene Expression Levels")
plt.show()

brain_lung_integrins = integrins[integrins['primary_site'].isin(['Brain', 'Lung'])]     #filter data by organ, display both brain and lung data
brain_lung_integrins

# First, define the data_brain_lung variable before using it
# For example, you might need to load your data from a file:
#data_brain_lung = pd.read_csv('/Users/reneewang/Downloads/gtex_integrin_7_organs.xlsx')
brain_lung_integrins_expression_only = brain_lung_integrins.iloc[:, 1:]
brain_lung_integrins_expression_only

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

#define wwhat is X and what is Y in your model
X=brain_lung_integrins_expression_only[['ITGA10']]
y=brain_lung_integrins_expression_only['primary_site']

#define split between train and test 
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

#define the model you want to use : logistic regression
model = LogisticRegression()
model.fit(X_train, y_train)

# predict and evaluate
y_pred = model.predict(X_test)
accuracy=accuracy_score(y_test,y_pred)
print(f"Accuracy using ITGA10: {accuracy:.2f}")

Accuracy using ITGA10: 0.94

brain_lung_integrins = integrins[integrins['primary_site'].isin(['Brain', 'Lung'])]     #filter data by organ, display both brain and lungdata

#rearrange data
brain_lung_integrins_vertical = brain_lung_integrins_expression_only.melt(id_vars = 'primary_site', var_name = 'integrin_gene', value_name = 'expression_levels')
brain_lung_integrins_vertical

#switch ITGA10 to ITGB4 and see how that impact its accuracy
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

#define wwhat is X and what is Y in your model
X=brain_lung_integrins_expression_only[['ITGB4']]
y=brain_lung_integrins_expression_only['primary_site']

#define split between train and test 
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

#define the model you want to use : logistic regression
model = LogisticRegression()
model.fit(X_train, y_train)

# predict and evaluate
y_pred = model.predict(X_test)
accuracy=accuracy_score(y_test,y_pred)
print(f"Accuracy using ITGB4: {accuracy:.2f}")

Accuracy using ITGB4: 0.81

plt.figure(figsize=(16, 6))
sns.violinplot(x = 'integrin_gene', y = 'expression_levels', hue = 'primary_site', data = brain_lung_integrins_vertical, split = True, inner = 'quartile')
plt.title("Integrin Genes of the Brain vs. the Lung")
plt.xlabel("Integrin Gene")
plt.ylabel("Gene Expression Levels")
plt.legend(title = 'primary_site')
plt.show()

## model accuracy is defined as: number of correction predictions/total number of predictions

#AUROC curve 
from sklearn.metrics import roc_curve, roc_auc_score
import matplotlib.pyplot as plt
#Step 1: Prepare data
X = brain_lung_integrins_expression_only[['ITGB4']]  # 👈 Use your chosen integrin
y = brain_lung_integrins_expression_only['primary_site'].map({'Brain': 0, 'Lung': 1})  # Binary encoding

# Step 2: Train/test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Step 3: Train model
model = LogisticRegression()
model.fit(X_train, y_train)

# Step 4: Predict probabilities
y_proba = model.predict_proba(X_test)[:, 1]  # Probabilities for class "Lung"

# Step 5: Compute ROC curve and AUC
fpr, tpr, thresholds = roc_curve(y_test, y_proba)
auc = roc_auc_score(y_test, y_proba)

# Step 6: Plot
plt.figure(figsize=(6, 6))
plt.plot(fpr, tpr, label=f'AUC = {auc:.2f}')
plt.plot([0, 1], [0, 1], linestyle='--', color='gray')  # random guess line
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('ROC Curve (Brain vs Lung) using ITGB4 Expression')
plt.legend()
plt.grid(True)
plt.show()

#AUROC curve 
from sklearn.metrics import roc_curve, roc_auc_score
import matplotlib.pyplot as plt
#Step 1: Prepare data
X = brain_lung_integrins_expression_only[['ITGA10']]  # 👈 Use your chosen integrin
y = brain_lung_integrins_expression_only['primary_site'].map({'Brain': 0, 'Lung': 1})  # Binary encoding

# Step 2: Train/test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Step 3: Train model
model = LogisticRegression()
model.fit(X_train, y_train)

# Step 4: Predict probabilities
y_proba = model.predict_proba(X_test)[:, 1]  # Probabilities for class "Lung"

# Step 5: Compute ROC curve and AUC
fpr, tpr, thresholds = roc_curve(y_test, y_proba)
auc = roc_auc_score(y_test, y_proba)

# Step 6: Plot
plt.figure(figsize=(6, 6))
plt.plot(fpr, tpr, label=f'AUC = {auc:.2f}')
plt.plot([0, 1], [0, 1], linestyle='--', color='gray')  # random guess line
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('ROC Curve (Brain vs Lung) using ITGA10 Expression')
plt.legend()
plt.grid(True)
plt.show()

from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_curve, roc_auc_score
import matplotlib.pyplot as plt

# Step 1: Prepare data
X = brain_lung_integrins_expression_only[['ITGA3', 'ITGB4']]  # 👈 Include both integrins
y = brain_lung_integrins_expression_only['primary_site'].map({'Brain': 0, 'Lung': 1})  # Binary target

# Step 2: Train/test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Step 3: Train model
model = LogisticRegression()
model.fit(X_train, y_train)

# Step 4: Predict probabilities
y_proba = model.predict_proba(X_test)[:, 1]  # Probabilities for class "Lung"

# Step 5: Compute ROC curve and AUC
fpr, tpr, thresholds = roc_curve(y_test, y_proba)
auc = roc_auc_score(y_test, y_proba)

# Step 6: Plot
plt.figure(figsize=(6, 6))
plt.plot(fpr, tpr, label=f'AUC = {auc:.2f}')
plt.plot([0, 1], [0, 1], linestyle='--', color='gray')  # random guess line
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('ROC Curve (Brain vs Lung) using ITGA3 & ITGB4 Expression')
plt.legend()
plt.grid(True)
plt.show()

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score

# Step 1: Prepare features and target
selected_genes = ['ITGA10', 'ITGB4'] 
#X = integrins.iloc[:, -27:]  # Assuming the last 27 columns are integrins
X = integrins[selected_genes]  # Assuming the last 27 columns are integrins
y = integrins['primary_site']

# Step 2: Encode organ labels as numbers
le = LabelEncoder()
y_encoded = le.fit_transform(y)

# Step 3: Train/test split
X_train, X_test, y_train, y_test = train_test_split(X, y_encoded, test_size=0.2, random_state=42)

# Step 4: Train multinomial logistic regression
model = LogisticRegression(multi_class='multinomial', solver='lbfgs', max_iter=1000)
model.fit(X_train, y_train)

# Step 5: Predict and evaluate
y_pred = model.predict(X_test)

print("Accuracy:", accuracy_score(y_test, y_pred))
print("\nClassification Report:")
print(classification_report(y_test, y_pred, target_names=le.classes_))

print("\nConfusion Matrix:")
print(confusion_matrix(y_test, y_pred))
#a confusion matrix is a table that summarizes the performance of a classification model. It visualizes the counts of true positives, true negatives, false positives, and false negatives, allowing for a detailed analysis of how well a model is predicting different classes. The columns typically represent predicted classes, while the rows represent actual (or true) classes. The values within the matrix represent the counts of instances that fall into each combination of predicted and actual classes.

Accuracy: 0.7939698492462312

Classification Report:
              precision    recall  f1-score   support

 Bone Marrow       0.77      1.00      0.87        10
       Brain       0.81      0.94      0.87       247
      Breast       0.64      0.41      0.50        44
       Liver       1.00      0.65      0.79        23
        Lung       0.76      0.88      0.82        43
       Ovary       0.50      0.10      0.17        10
    Prostate       0.75      0.14      0.24        21

    accuracy                           0.79       398
   macro avg       0.75      0.59      0.61       398
weighted avg       0.78      0.79      0.77       398


Confusion Matrix:
[[ 10   0   0   0   0   0   0]
 [  3 231   3   0   8   1   1]
 [  0  25  18   0   1   0   0]
 [  0   8   0  15   0   0   0]
 [  0   4   1   0  38   0   0]
 [  0   6   0   0   3   1   0]
 [  0  12   6   0   0   0   3]]

/opt/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1264: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. From then on, it will always use 'multinomial'. Leave it to its default value to avoid this warning.
  warnings.warn(

integrins['primary_site'].value_counts()

primary_site
Brain          1152
Lung            288
Breast          179
Liver           110
Prostate        100
Ovary            88
Bone Marrow      70
Name: count, dtype: int64

	Unnamed: 0	primary_site	ITGA10	ITGAD	ITGAM	ITGA3	ITGBL1	ITGAE	ITGA2	ITGB3	...	ITGA6	ITGA2B	ITGB1	ITGAL	ITGA9	ITGB5	ITGA8	ITGA4	ITGA1	ITGA11
0	GTEX-13QIC-0011-R1a-SM-5O9CJ	Brain	0.5763	-6.5064	2.2573	0.7832	1.0363	4.6035	2.5731	-2.8262	...	2.8562	1.3846	5.8430	1.1316	-0.7108	3.5387	-0.0725	-0.4521	0.2029	-2.8262
1	GTEX-1399S-1726-SM-5L3DI	Lung	4.9137	-3.6259	4.7307	7.1584	1.7702	4.9556	1.9149	2.6067	...	4.2412	4.1211	7.7256	4.4900	2.9281	6.1483	5.1867	2.6185	4.7856	-0.0277
2	GTEX-PWCY-1326-SM-48TCU	Ovary	2.3953	-5.0116	1.4547	4.2593	-0.7346	4.4149	0.2642	1.5216	...	3.6816	1.5465	7.2964	-0.9406	2.7742	5.0414	2.0325	0.7579	2.2573	1.2516
3	GTEX-QXCU-0626-SM-2TC69	Lung	4.0541	-2.3147	4.5053	7.5651	4.1788	4.1772	5.3695	1.8444	...	4.9631	1.9149	7.9947	3.3911	2.8462	6.7683	4.1636	2.7951	5.3284	1.2147
4	GTEX-ZA64-1526-SM-5CVMD	Breast	2.0569	-2.4659	3.3993	3.1311	3.0074	4.4977	-1.7809	2.7139	...	4.7340	0.6332	7.3496	-0.9406	2.5338	6.5696	1.7229	-0.6416	3.1195	1.1050
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1982	GTEX-QMRM-0826-SM-3NB33	Lung	5.3067	-3.8160	4.9065	7.5810	5.8714	4.7345	2.6185	3.1095	...	5.6080	3.7324	8.2849	4.6201	3.6440	6.7052	5.1094	3.3364	5.8153	1.6604
1983	GTEX-YFCO-1626-SM-4W1Z3	Prostate	2.9581	-4.6082	1.1641	4.6938	1.5902	5.8625	-0.5125	1.7617	...	3.8798	-1.4699	7.5163	-0.3752	2.9562	5.3035	4.4304	-0.9406	3.6136	0.4233
1984	GTEX-1117F-2826-SM-5GZXL	Breast	4.3184	-6.5064	1.0433	4.8440	3.5498	4.6809	1.0293	3.3478	...	5.3256	-0.0725	7.7516	1.1382	2.1411	7.1132	0.3796	0.0854	3.8650	1.0151
1985	GTEX-Q2AG-2826-SM-2HMJQ	Brain	3.4622	-5.5735	1.5013	5.4835	1.7702	4.7517	0.6790	-3.1714	...	1.1960	4.1740	4.3002	0.5470	-0.9971	3.7982	-0.2498	1.4808	-0.5125	-0.5125
1986	GTEX-XV7Q-0426-SM-4BRVN	Lung	2.5585	-1.7809	6.7916	6.5865	2.7051	4.9519	4.3618	3.1892	...	3.5779	2.8974	7.7685	4.8294	1.9149	5.9989	2.4117	2.4198	4.2080	1.0007

	Unnamed: 0	primary_site	ITGA10	ITGAD	ITGAM	ITGA3	ITGBL1	ITGAE	ITGA2	ITGB3	...	ITGA6	ITGA2B	ITGB1	ITGAL	ITGA9	ITGB5	ITGA8	ITGA4	ITGA1	ITGA11
0	GTEX-13QIC-0011-R1a-SM-5O9CJ	Brain	0.5763	-6.5064	2.2573	0.7832	1.0363	4.6035	2.5731	-2.8262	...	2.8562	1.3846	5.8430	1.1316	-0.7108	3.5387	-0.0725	-0.4521	0.2029	-2.8262
8	GTEX-N7MS-2526-SM-26GMA	Brain	2.2960	-9.9658	0.6608	5.2840	0.4233	4.8510	-0.2671	-0.1031	...	1.5415	4.6623	3.4687	0.5666	-0.0130	3.0654	0.7916	1.0433	-0.7346	-0.7588
10	GTEX-N7MS-2526-SM-26GMR	Brain	-0.2498	-9.9658	-0.8863	3.1685	-1.6394	2.8158	-0.4719	-1.1488	...	1.6045	0.9268	2.8055	-0.5973	0.4657	1.8918	0.3460	0.3907	-1.9942	-1.5522
12	GTEX-NPJ7-0011-R6a-SM-2I3G7	Brain	1.6045	-6.5064	2.3193	3.6335	-2.3147	5.0670	-0.8863	-0.8084	...	3.2018	1.7575	4.6894	0.4125	-0.6643	3.6916	-0.6193	-2.2447	1.2023	-1.9942
14	GTEX-132Q8-3026-SM-5PNVG	Brain	2.8974	-6.5064	1.9601	4.1836	-0.8084	4.5892	-0.5543	0.3460	...	3.6018	2.7931	4.7274	-0.0574	1.2271	4.3793	0.8488	-0.2159	2.1378	-0.6416
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1977	GTEX-13G51-0011-R6b-SM-5LZX4	Brain	-0.3383	-6.5064	1.6234	2.7487	-2.2447	5.2415	-0.8863	-2.9324	...	2.1988	0.4016	4.5142	-1.1811	-0.8084	3.9983	-1.0862	-3.1714	-0.7588	-1.9379
1978	GTEX-YFC4-0011-R10a-SM-4SOK5	Brain	0.4447	-5.5735	0.3231	3.5237	-1.5105	4.9016	0.9419	-2.7274	...	2.8178	1.3567	4.4621	-0.2845	1.0222	3.3336	0.1903	-1.0559	0.0300	-0.4719
1980	GTEX-13112-0011-R4b-SM-5DUXL	Brain	0.6969	-6.5064	-0.9686	2.3760	-2.2447	4.0739	-0.6193	-4.0350	...	2.7357	1.5806	4.6882	-0.9971	-0.5756	3.5136	0.9343	-1.0862	0.4340	-2.2447
1981	GTEX-1313W-0011-R1b-SM-5EQ4A	Brain	0.1124	-5.0116	2.2482	2.8897	-0.5125	4.6445	0.3115	-3.6259	...	2.1147	0.9716	5.1202	0.6608	0.4761	3.2343	0.8408	-0.0574	-0.1828	-2.5479
1985	GTEX-Q2AG-2826-SM-2HMJQ	Brain	3.4622	-5.5735	1.5013	5.4835	1.7702	4.7517	0.6790	-3.1714	...	1.1960	4.1740	4.3002	0.5470	-0.9971	3.7982	-0.2498	1.4808	-0.5125	-0.5125

	Unnamed: 0	primary_site	ITGA10	ITGAD	ITGAM	ITGA3	ITGBL1	ITGAE	ITGA2	ITGB3	...	ITGA6	ITGA2B	ITGB1	ITGAL	ITGA9	ITGB5	ITGA8	ITGA4	ITGA1	ITGA11
1	GTEX-1399S-1726-SM-5L3DI	Lung	4.9137	-3.6259	4.7307	7.1584	1.7702	4.9556	1.9149	2.6067	...	4.2412	4.1211	7.7256	4.4900	2.9281	6.1483	5.1867	2.6185	4.7856	-0.0277
3	GTEX-QXCU-0626-SM-2TC69	Lung	4.0541	-2.3147	4.5053	7.5651	4.1788	4.1772	5.3695	1.8444	...	4.9631	1.9149	7.9947	3.3911	2.8462	6.7683	4.1636	2.7951	5.3284	1.2147
5	GTEX-11EI6-0826-SM-5985V	Lung	6.0732	-2.4659	3.9901	7.3945	4.7688	5.1157	4.3356	2.3366	...	3.7378	4.7247	7.5016	5.1396	2.5036	6.5443	4.6531	3.8136	5.8679	0.7407
6	GTEX-S341-0326-SM-2XCAU	Lung	4.2510	-5.0116	3.3076	6.1715	3.1129	5.2954	2.2960	1.1184	...	4.7104	2.7530	7.5022	4.0730	2.6325	6.0483	5.0562	2.6962	5.1611	0.9343
7	GTEX-WY7C-0426-SM-3NB3C	Lung	3.3633	-2.5479	4.8340	6.6864	3.0585	4.8294	2.6464	0.7999	...	5.1190	1.5013	8.0260	3.6635	3.2435	5.8503	5.2991	2.8076	4.7571	-0.1345
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1962	GTEX-Q2AH-0426-SM-2I3EP	Lung	5.9644	-1.3921	5.1061	6.9470	3.8973	4.8630	3.6089	3.9765	...	5.1115	4.9041	7.9145	4.5559	3.7138	6.5782	4.7512	2.9710	5.0777	1.8444
1970	GTEX-RWS6-0226-SM-2XCA9	Lung	6.0830	-0.5756	4.3889	6.7302	4.6053	5.1065	2.8321	0.9716	...	5.8176	2.5437	7.7929	4.9012	2.7993	6.7510	5.2204	2.8422	5.0951	-0.3201
1975	GTEX-131XE-0726-SM-5HL9K	Lung	3.7971	-1.9379	4.8555	6.4052	3.9561	5.4263	3.2959	4.5199	...	4.6697	6.5777	7.5114	5.2130	2.3816	6.6225	3.7389	3.7248	5.6809	0.8488
1982	GTEX-QMRM-0826-SM-3NB33	Lung	5.3067	-3.8160	4.9065	7.5810	5.8714	4.7345	2.6185	3.1095	...	5.6080	3.7324	8.2849	4.6201	3.6440	6.7052	5.1094	3.3364	5.8153	1.6604
1986	GTEX-XV7Q-0426-SM-4BRVN	Lung	2.5585	-1.7809	6.7916	6.5865	2.7051	4.9519	4.3618	3.1892	...	3.5779	2.8974	7.7685	4.8294	1.9149	5.9989	2.4117	2.4198	4.2080	1.0007

	Unnamed: 0	primary_site	ITGA10	ITGAD	ITGAM	ITGA3	ITGBL1	ITGAE	ITGA2	ITGB3	...	ITGA6	ITGA2B	ITGB1	ITGAL	ITGA9	ITGB5	ITGA8	ITGA4	ITGA1	ITGA11
0	GTEX-13QIC-0011-R1a-SM-5O9CJ	Brain	0.5763	-6.5064	2.2573	0.7832	1.0363	4.6035	2.5731	-2.8262	...	2.8562	1.3846	5.8430	1.1316	-0.7108	3.5387	-0.0725	-0.4521	0.2029	-2.8262
1	GTEX-1399S-1726-SM-5L3DI	Lung	4.9137	-3.6259	4.7307	7.1584	1.7702	4.9556	1.9149	2.6067	...	4.2412	4.1211	7.7256	4.4900	2.9281	6.1483	5.1867	2.6185	4.7856	-0.0277
3	GTEX-QXCU-0626-SM-2TC69	Lung	4.0541	-2.3147	4.5053	7.5651	4.1788	4.1772	5.3695	1.8444	...	4.9631	1.9149	7.9947	3.3911	2.8462	6.7683	4.1636	2.7951	5.3284	1.2147
5	GTEX-11EI6-0826-SM-5985V	Lung	6.0732	-2.4659	3.9901	7.3945	4.7688	5.1157	4.3356	2.3366	...	3.7378	4.7247	7.5016	5.1396	2.5036	6.5443	4.6531	3.8136	5.8679	0.7407
6	GTEX-S341-0326-SM-2XCAU	Lung	4.2510	-5.0116	3.3076	6.1715	3.1129	5.2954	2.2960	1.1184	...	4.7104	2.7530	7.5022	4.0730	2.6325	6.0483	5.0562	2.6962	5.1611	0.9343
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1980	GTEX-13112-0011-R4b-SM-5DUXL	Brain	0.6969	-6.5064	-0.9686	2.3760	-2.2447	4.0739	-0.6193	-4.0350	...	2.7357	1.5806	4.6882	-0.9971	-0.5756	3.5136	0.9343	-1.0862	0.4340	-2.2447
1981	GTEX-1313W-0011-R1b-SM-5EQ4A	Brain	0.1124	-5.0116	2.2482	2.8897	-0.5125	4.6445	0.3115	-3.6259	...	2.1147	0.9716	5.1202	0.6608	0.4761	3.2343	0.8408	-0.0574	-0.1828	-2.5479
1982	GTEX-QMRM-0826-SM-3NB33	Lung	5.3067	-3.8160	4.9065	7.5810	5.8714	4.7345	2.6185	3.1095	...	5.6080	3.7324	8.2849	4.6201	3.6440	6.7052	5.1094	3.3364	5.8153	1.6604
1985	GTEX-Q2AG-2826-SM-2HMJQ	Brain	3.4622	-5.5735	1.5013	5.4835	1.7702	4.7517	0.6790	-3.1714	...	1.1960	4.1740	4.3002	0.5470	-0.9971	3.7982	-0.2498	1.4808	-0.5125	-0.5125
1986	GTEX-XV7Q-0426-SM-4BRVN	Lung	2.5585	-1.7809	6.7916	6.5865	2.7051	4.9519	4.3618	3.1892	...	3.5779	2.8974	7.7685	4.8294	1.9149	5.9989	2.4117	2.4198	4.2080	1.0007

	primary_site	ITGA10	ITGAD	ITGAM	ITGA3	ITGBL1	ITGAE	ITGA2	ITGB3	ITGA7	...	ITGA6	ITGA2B	ITGB1	ITGAL	ITGA9	ITGB5	ITGA8	ITGA4	ITGA1	ITGA11
0	Brain	0.5763	-6.5064	2.2573	0.7832	1.0363	4.6035	2.5731	-2.8262	4.9663	...	2.8562	1.3846	5.8430	1.1316	-0.7108	3.5387	-0.0725	-0.4521	0.2029	-2.8262
1	Lung	4.9137	-3.6259	4.7307	7.1584	1.7702	4.9556	1.9149	2.6067	3.9270	...	4.2412	4.1211	7.7256	4.4900	2.9281	6.1483	5.1867	2.6185	4.7856	-0.0277
3	Lung	4.0541	-2.3147	4.5053	7.5651	4.1788	4.1772	5.3695	1.8444	4.5355	...	4.9631	1.9149	7.9947	3.3911	2.8462	6.7683	4.1636	2.7951	5.3284	1.2147
5	Lung	6.0732	-2.4659	3.9901	7.3945	4.7688	5.1157	4.3356	2.3366	5.0527	...	3.7378	4.7247	7.5016	5.1396	2.5036	6.5443	4.6531	3.8136	5.8679	0.7407
6	Lung	4.2510	-5.0116	3.3076	6.1715	3.1129	5.2954	2.2960	1.1184	5.2392	...	4.7104	2.7530	7.5022	4.0730	2.6325	6.0483	5.0562	2.6962	5.1611	0.9343
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1980	Brain	0.6969	-6.5064	-0.9686	2.3760	-2.2447	4.0739	-0.6193	-4.0350	4.8788	...	2.7357	1.5806	4.6882	-0.9971	-0.5756	3.5136	0.9343	-1.0862	0.4340	-2.2447
1981	Brain	0.1124	-5.0116	2.2482	2.8897	-0.5125	4.6445	0.3115	-3.6259	4.5110	...	2.1147	0.9716	5.1202	0.6608	0.4761	3.2343	0.8408	-0.0574	-0.1828	-2.5479
1982	Lung	5.3067	-3.8160	4.9065	7.5810	5.8714	4.7345	2.6185	3.1095	5.2032	...	5.6080	3.7324	8.2849	4.6201	3.6440	6.7052	5.1094	3.3364	5.8153	1.6604
1985	Brain	3.4622	-5.5735	1.5013	5.4835	1.7702	4.7517	0.6790	-3.1714	5.3597	...	1.1960	4.1740	4.3002	0.5470	-0.9971	3.7982	-0.2498	1.4808	-0.5125	-0.5125
1986	Lung	2.5585	-1.7809	6.7916	6.5865	2.7051	4.9519	4.3618	3.1892	7.7121	...	3.5779	2.8974	7.7685	4.8294	1.9149	5.9989	2.4117	2.4198	4.2080	1.0007