LID SPN TRAP

1436094_at

Vgf VGF nerve growth factor inducible

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
0.295 0.593 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.660
Model: OLS Adj. R-squared: 0.607
Method: Least Squares F-statistic: 12.30
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000106
Time: 03:40:46 Log-Likelihood: -100.69
No. Observations: 23 AIC: 209.4
Df Residuals: 19 BIC: 213.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -115.3199 215.193 -0.536 0.598 -565.723 335.083
C(dose)[T.1] 226.0705 307.658 0.735 0.471 -417.865 870.006
expression 17.8153 22.605 0.788 0.440 -29.497 65.128
expression:C(dose)[T.1] -18.1303 31.264 -0.580 0.569 -83.566 47.305
Omnibus: 0.558 Durbin-Watson: 1.774
Prob(Omnibus): 0.757 Jarque-Bera (JB): 0.604
Skew: -0.033 Prob(JB): 0.739
Kurtosis: 2.209 Cond. No. 906.

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: 18.91
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.45e-05
Time: 03:40:46 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 -25.1251 146.233 -0.172 0.865 -330.162 279.911
C(dose)[T.1] 47.8287 13.368 3.578 0.002 19.943 75.715
expression 8.3370 15.354 0.543 0.593 -23.691 40.365
Omnibus: 0.051 Durbin-Watson: 1.850
Prob(Omnibus): 0.975 Jarque-Bera (JB): 0.256
Skew: -0.063 Prob(JB): 0.880
Kurtosis: 2.499 Cond. No. 336.

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: 03:40:46 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: 0.433
Model: OLS Adj. R-squared: 0.406
Method: Least Squares F-statistic: 16.02
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000645
Time: 03:40:46 Log-Likelihood: -106.58
No. Observations: 23 AIC: 217.2
Df Residuals: 21 BIC: 219.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -412.1213 122.986 -3.351 0.003 -667.885 -156.358
expression 50.0250 12.497 4.003 0.001 24.037 76.013
Omnibus: 2.627 Durbin-Watson: 2.102
Prob(Omnibus): 0.269 Jarque-Bera (JB): 1.216
Skew: 0.066 Prob(JB): 0.544
Kurtosis: 1.881 Cond. No. 225.

CP101

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

F-statistic p-value df difference
0.360 0.560 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.529
Model: OLS Adj. R-squared: 0.401
Method: Least Squares F-statistic: 4.124
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0346
Time: 03:40:46 Log-Likelihood: -69.647
No. Observations: 15 AIC: 147.3
Df Residuals: 11 BIC: 150.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -250.1451 244.451 -1.023 0.328 -788.178 287.888
C(dose)[T.1] 477.3386 351.101 1.360 0.201 -295.429 1250.106
expression 36.5515 28.106 1.300 0.220 -25.310 98.413
expression:C(dose)[T.1] -48.9589 39.855 -1.228 0.245 -136.679 38.761
Omnibus: 2.854 Durbin-Watson: 1.012
Prob(Omnibus): 0.240 Jarque-Bera (JB): 1.364
Skew: -0.733 Prob(JB): 0.506
Kurtosis: 3.187 Cond. No. 549.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.465
Model: OLS Adj. R-squared: 0.376
Method: Least Squares F-statistic: 5.211
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0235
Time: 03:40:46 Log-Likelihood: -70.611
No. Observations: 15 AIC: 147.2
Df Residuals: 12 BIC: 149.3
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -38.5924 177.131 -0.218 0.831 -424.528 347.343
C(dose)[T.1] 46.4741 16.159 2.876 0.014 11.266 81.683
expression 12.2026 20.345 0.600 0.560 -32.126 56.531
Omnibus: 2.766 Durbin-Watson: 0.742
Prob(Omnibus): 0.251 Jarque-Bera (JB): 2.022
Skew: -0.860 Prob(JB): 0.364
Kurtosis: 2.477 Cond. No. 205.

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: 03:40:46 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: 0.096
Model: OLS Adj. R-squared: 0.026
Method: Least Squares F-statistic: 1.379
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.261
Time: 03:40:46 Log-Likelihood: -74.544
No. Observations: 15 AIC: 153.1
Df Residuals: 13 BIC: 154.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -158.5598 214.968 -0.738 0.474 -622.969 305.850
expression 28.6381 24.383 1.175 0.261 -24.038 81.314
Omnibus: 2.956 Durbin-Watson: 1.638
Prob(Omnibus): 0.228 Jarque-Bera (JB): 1.353
Skew: 0.362 Prob(JB): 0.509
Kurtosis: 1.719 Cond. No. 199.