LID SPN TRAP

1426225_at

Rbp4 retinol binding protein 4, plasma

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
0.001 0.970 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.693
Model: OLS Adj. R-squared: 0.644
Method: Least Squares F-statistic: 14.28
Date: Thu, 03 Apr 2025 Prob (F-statistic): 4.15e-05
Time: 22:49:24 Log-Likelihood: -99.534
No. Observations: 23 AIC: 207.1
Df Residuals: 19 BIC: 211.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -146.4572 246.683 -0.594 0.560 -662.771 369.857
C(dose)[T.1] 937.2884 538.313 1.741 0.098 -189.413 2063.990
expression 20.5704 25.281 0.814 0.426 -32.343 73.483
expression:C(dose)[T.1] -87.6511 53.340 -1.643 0.117 -199.294 23.991
Omnibus: 1.514 Durbin-Watson: 1.923
Prob(Omnibus): 0.469 Jarque-Bera (JB): 0.982
Skew: 0.143 Prob(JB): 0.612
Kurtosis: 2.029 Cond. No. 1.51e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.649
Model: OLS Adj. R-squared: 0.614
Method: Least Squares F-statistic: 18.50
Date: Thu, 03 Apr 2025 Prob (F-statistic): 2.83e-05
Time: 22:49:24 Log-Likelihood: -101.06
No. Observations: 23 AIC: 208.1
Df Residuals: 20 BIC: 211.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 45.6108 226.280 0.202 0.842 -426.402 517.623
C(dose)[T.1] 52.9573 13.295 3.983 0.001 25.223 80.691
expression 0.8813 23.188 0.038 0.970 -47.488 49.250
Omnibus: 0.304 Durbin-Watson: 1.906
Prob(Omnibus): 0.859 Jarque-Bera (JB): 0.474
Skew: 0.054 Prob(JB): 0.789
Kurtosis: 2.305 Cond. No. 521.

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, 03 Apr 2025 Prob (F-statistic): 3.51e-06
Time: 22:49:24 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: 0.371
Model: OLS Adj. R-squared: 0.341
Method: Least Squares F-statistic: 12.37
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.00205
Time: 22:49:24 Log-Likelihood: -107.78
No. Observations: 23 AIC: 219.6
Df Residuals: 21 BIC: 221.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -620.5680 199.182 -3.116 0.005 -1034.790 -206.346
expression 70.3015 19.988 3.517 0.002 28.735 111.868
Omnibus: 1.037 Durbin-Watson: 2.627
Prob(Omnibus): 0.595 Jarque-Bera (JB): 0.907
Skew: 0.258 Prob(JB): 0.636
Kurtosis: 2.176 Cond. No. 350.

CP101

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

F-statistic p-value df difference
0.469 0.506 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.521
Model: OLS Adj. R-squared: 0.391
Method: Least Squares F-statistic: 3.995
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0378
Time: 22:49:24 Log-Likelihood: -69.773
No. Observations: 15 AIC: 147.5
Df Residuals: 11 BIC: 150.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -176.4330 198.719 -0.888 0.394 -613.810 260.943
C(dose)[T.1] 382.3998 299.952 1.275 0.229 -277.791 1042.591
expression 28.7966 23.429 1.229 0.245 -22.769 80.363
expression:C(dose)[T.1] -40.0112 36.641 -1.092 0.298 -120.659 40.636
Omnibus: 1.489 Durbin-Watson: 0.969
Prob(Omnibus): 0.475 Jarque-Bera (JB): 0.934
Skew: -0.593 Prob(JB): 0.627
Kurtosis: 2.702 Cond. No. 417.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.470
Model: OLS Adj. R-squared: 0.381
Method: Least Squares F-statistic: 5.310
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0223
Time: 22:49:24 Log-Likelihood: -70.545
No. Observations: 15 AIC: 147.1
Df Residuals: 12 BIC: 149.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -37.9066 154.178 -0.246 0.810 -373.831 298.018
C(dose)[T.1] 55.4385 17.929 3.092 0.009 16.375 94.502
expression 12.4386 18.157 0.685 0.506 -27.123 52.000
Omnibus: 3.113 Durbin-Watson: 0.711
Prob(Omnibus): 0.211 Jarque-Bera (JB): 1.973
Skew: -0.883 Prob(JB): 0.373
Kurtosis: 2.805 Cond. No. 167.

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, 03 Apr 2025 Prob (F-statistic): 0.00629
Time: 22:49:24 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: 0.047
Model: OLS Adj. R-squared: -0.026
Method: Least Squares F-statistic: 0.6388
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.438
Time: 22:49:24 Log-Likelihood: -74.940
No. Observations: 15 AIC: 153.9
Df Residuals: 13 BIC: 155.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 225.6695 165.452 1.364 0.196 -131.768 583.107
expression -16.0964 20.139 -0.799 0.438 -59.604 27.411
Omnibus: 0.237 Durbin-Watson: 1.484
Prob(Omnibus): 0.888 Jarque-Bera (JB): 0.173
Skew: -0.200 Prob(JB): 0.917
Kurtosis: 2.657 Cond. No. 139.