LID SPN TRAP

1442601_at

Gmeb1 glucocorticoid modulatory element binding protein 1

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
0.043 0.837 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.738
Model: OLS Adj. R-squared: 0.696
Method: Least Squares F-statistic: 17.82
Date: Thu, 21 Nov 2024 Prob (F-statistic): 9.47e-06
Time: 06:21:21 Log-Likelihood: -97.711
No. Observations: 23 AIC: 203.4
Df Residuals: 19 BIC: 208.0
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 107.4015 32.055 3.350 0.003 40.309 174.494
C(dose)[T.1] -81.0815 53.795 -1.507 0.148 -193.677 31.514
expression -10.8210 6.429 -1.683 0.109 -24.276 2.634
expression:C(dose)[T.1] 27.4190 10.860 2.525 0.021 4.688 50.150
Omnibus: 0.464 Durbin-Watson: 1.885
Prob(Omnibus): 0.793 Jarque-Bera (JB): 0.448
Skew: -0.289 Prob(JB): 0.799
Kurtosis: 2.635 Cond. No. 86.9

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.650
Model: OLS Adj. R-squared: 0.615
Method: Least Squares F-statistic: 18.56
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.77e-05
Time: 06:21:21 Log-Likelihood: -101.04
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 60.1748 29.321 2.052 0.053 -0.988 121.338
C(dose)[T.1] 53.3103 8.761 6.085 0.000 35.035 71.586
expression -1.2137 5.836 -0.208 0.837 -13.388 10.960
Omnibus: 0.299 Durbin-Watson: 1.927
Prob(Omnibus): 0.861 Jarque-Bera (JB): 0.471
Skew: 0.052 Prob(JB): 0.790
Kurtosis: 2.307 Cond. No. 34.7

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: 06:21:21 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: 0.002
Model: OLS Adj. R-squared: -0.046
Method: Least Squares F-statistic: 0.03259
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.858
Time: 06:21:21 Log-Likelihood: -113.09
No. Observations: 23 AIC: 230.2
Df Residuals: 21 BIC: 232.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 88.2328 47.716 1.849 0.079 -10.998 187.464
expression -1.7360 9.616 -0.181 0.858 -21.733 18.261
Omnibus: 2.882 Durbin-Watson: 2.509
Prob(Omnibus): 0.237 Jarque-Bera (JB): 1.569
Skew: 0.342 Prob(JB): 0.456
Kurtosis: 1.918 Cond. No. 34.1

CP101

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

F-statistic p-value df difference
0.011 0.918 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.505
Model: OLS Adj. R-squared: 0.370
Method: Least Squares F-statistic: 3.735
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0451
Time: 06:21:21 Log-Likelihood: -70.032
No. Observations: 15 AIC: 148.1
Df Residuals: 11 BIC: 150.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 9.3410 67.480 0.138 0.892 -139.180 157.862
C(dose)[T.1] 146.4968 88.815 1.649 0.127 -48.983 341.977
expression 12.0578 13.807 0.873 0.401 -18.331 42.446
expression:C(dose)[T.1] -21.2205 19.144 -1.108 0.291 -63.357 20.916
Omnibus: 4.931 Durbin-Watson: 0.706
Prob(Omnibus): 0.085 Jarque-Bera (JB): 2.499
Skew: -0.957 Prob(JB): 0.287
Kurtosis: 3.578 Cond. No. 73.8

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.449
Model: OLS Adj. R-squared: 0.358
Method: Least Squares F-statistic: 4.895
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0279
Time: 06:21:21 Log-Likelihood: -70.826
No. Observations: 15 AIC: 147.7
Df Residuals: 12 BIC: 149.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 62.5118 47.911 1.305 0.216 -41.877 166.900
C(dose)[T.1] 49.7454 16.567 3.003 0.011 13.649 85.842
expression 1.0206 9.655 0.106 0.918 -20.016 22.057
Omnibus: 2.490 Durbin-Watson: 0.799
Prob(Omnibus): 0.288 Jarque-Bera (JB): 1.775
Skew: -0.810 Prob(JB): 0.412
Kurtosis: 2.540 Cond. No. 29.9

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: 06:21:21 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: 0.036
Model: OLS Adj. R-squared: -0.039
Method: Least Squares F-statistic: 0.4788
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.501
Time: 06:21:21 Log-Likelihood: -75.029
No. Observations: 15 AIC: 154.1
Df Residuals: 13 BIC: 155.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 130.2118 53.749 2.423 0.031 14.093 246.330
expression -8.0663 11.657 -0.692 0.501 -33.251 17.118
Omnibus: 0.361 Durbin-Watson: 1.653
Prob(Omnibus): 0.835 Jarque-Bera (JB): 0.400
Skew: -0.298 Prob(JB): 0.819
Kurtosis: 2.465 Cond. No. 26.0