LID SPN TRAP

1436309_at

Neto2 neuropilin (NRP) and tolloid (TLL)-like 2

AIM Score vs. Gene Expression
Full X range:
Auto X range:
Group Comparisons: Boxplots

CP73

Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)

F-statistic p-value df difference
0.304 0.587 1.0

Model:
AIM ~ expression + C(dose) + expression:C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.654
Model: OLS Adj. R-squared: 0.600
Method: Least Squares F-statistic: 11.99
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000124
Time: 03:32:48 Log-Likelihood: -100.89
No. Observations: 23 AIC: 209.8
Df Residuals: 19 BIC: 214.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 101.6338 113.094 0.899 0.380 -135.074 338.341
C(dose)[T.1] 43.0175 157.333 0.273 0.787 -286.284 372.319
expression -6.4641 15.392 -0.420 0.679 -38.679 25.751
expression:C(dose)[T.1] 1.1278 21.988 0.051 0.960 -44.894 47.150
Omnibus: 0.220 Durbin-Watson: 1.734
Prob(Omnibus): 0.896 Jarque-Bera (JB): 0.419
Skew: 0.076 Prob(JB): 0.811
Kurtosis: 2.356 Cond. No. 333.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.654
Model: OLS Adj. R-squared: 0.620
Method: Least Squares F-statistic: 18.93
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.44e-05
Time: 03:32:48 Log-Likelihood: -100.89
No. Observations: 23 AIC: 207.8
Df Residuals: 20 BIC: 211.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 97.5795 78.838 1.238 0.230 -66.874 262.033
C(dose)[T.1] 51.0711 9.624 5.307 0.000 30.995 71.147
expression -5.9115 10.714 -0.552 0.587 -28.261 16.438
Omnibus: 0.196 Durbin-Watson: 1.745
Prob(Omnibus): 0.907 Jarque-Bera (JB): 0.401
Skew: 0.078 Prob(JB): 0.818
Kurtosis: 2.372 Cond. No. 133.

Model:
AIM ~ C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.649
Model: OLS Adj. R-squared: 0.632
Method: Least Squares F-statistic: 38.84
Date: Thu, 21 Nov 2024 Prob (F-statistic): 3.51e-06
Time: 03:32:48 Log-Likelihood: -101.06
No. Observations: 23 AIC: 206.1
Df Residuals: 21 BIC: 208.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 54.2083 5.919 9.159 0.000 41.900 66.517
C(dose)[T.1] 53.3371 8.558 6.232 0.000 35.539 71.135
Omnibus: 0.322 Durbin-Watson: 1.888
Prob(Omnibus): 0.851 Jarque-Bera (JB): 0.485
Skew: 0.060 Prob(JB): 0.785
Kurtosis: 2.299 Cond. No. 2.57

Model:
AIM ~ expression

OLS Regression Results
Dep. Variable: AIM R-squared: 0.168
Model: OLS Adj. R-squared: 0.128
Method: Least Squares F-statistic: 4.229
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0524
Time: 03:32:48 Log-Likelihood: -110.99
No. Observations: 23 AIC: 226.0
Df Residuals: 21 BIC: 228.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 295.5662 105.174 2.810 0.010 76.845 514.287
expression -30.1743 14.674 -2.056 0.052 -60.690 0.342
Omnibus: 0.768 Durbin-Watson: 2.151
Prob(Omnibus): 0.681 Jarque-Bera (JB): 0.736
Skew: 0.374 Prob(JB): 0.692
Kurtosis: 2.544 Cond. No. 117.

CP101

Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)

F-statistic p-value df difference
0.111 0.745 1.0

Model:
AIM ~ expression + C(dose) + expression:C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.462
Model: OLS Adj. R-squared: 0.315
Method: Least Squares F-statistic: 3.146
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0689
Time: 03:32:48 Log-Likelihood: -70.654
No. Observations: 15 AIC: 149.3
Df Residuals: 11 BIC: 152.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -41.9439 214.576 -0.195 0.849 -514.223 430.335
C(dose)[T.1] 173.9900 310.935 0.560 0.587 -510.374 858.354
expression 14.5585 28.518 0.510 0.620 -48.210 77.327
expression:C(dose)[T.1] -16.6014 41.228 -0.403 0.695 -107.344 74.141
Omnibus: 2.763 Durbin-Watson: 0.783
Prob(Omnibus): 0.251 Jarque-Bera (JB): 1.930
Skew: -0.854 Prob(JB): 0.381
Kurtosis: 2.591 Cond. No. 388.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.454
Model: OLS Adj. R-squared: 0.363
Method: Least Squares F-statistic: 4.985
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0265
Time: 03:32:48 Log-Likelihood: -70.764
No. Observations: 15 AIC: 147.5
Df Residuals: 12 BIC: 149.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 17.7318 149.661 0.118 0.908 -308.351 343.815
C(dose)[T.1] 48.9569 15.684 3.121 0.009 14.785 83.129
expression 6.6151 19.863 0.333 0.745 -36.663 49.893
Omnibus: 3.831 Durbin-Watson: 0.781
Prob(Omnibus): 0.147 Jarque-Bera (JB): 2.315
Skew: -0.962 Prob(JB): 0.314
Kurtosis: 2.979 Cond. No. 147.

Model:
AIM ~ C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.449
Model: OLS Adj. R-squared: 0.406
Method: Least Squares F-statistic: 10.58
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00629
Time: 03:32:48 Log-Likelihood: -70.833
No. Observations: 15 AIC: 145.7
Df Residuals: 13 BIC: 147.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 67.4286 11.044 6.106 0.000 43.570 91.287
C(dose)[T.1] 49.1964 15.122 3.253 0.006 16.527 81.866
Omnibus: 2.713 Durbin-Watson: 0.810
Prob(Omnibus): 0.258 Jarque-Bera (JB): 1.868
Skew: -0.843 Prob(JB): 0.393
Kurtosis: 2.619 Cond. No. 2.70

Model:
AIM ~ expression

OLS Regression Results
Dep. Variable: AIM R-squared: 0.010
Model: OLS Adj. R-squared: -0.066
Method: Least Squares F-statistic: 0.1358
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.718
Time: 03:32:48 Log-Likelihood: -75.222
No. Observations: 15 AIC: 154.4
Df Residuals: 13 BIC: 155.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 22.4292 193.544 0.116 0.910 -395.698 440.557
expression 9.4581 25.661 0.369 0.718 -45.980 64.896
Omnibus: 0.549 Durbin-Watson: 1.609
Prob(Omnibus): 0.760 Jarque-Bera (JB): 0.559
Skew: 0.018 Prob(JB): 0.756
Kurtosis: 2.055 Cond. No. 147.