LID SPN TRAP

1449037_at

Crem cAMP responsive element modulator

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
0.004 0.949 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.649
Model: OLS Adj. R-squared: 0.594
Method: Least Squares F-statistic: 11.72
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.000142
Time: 22:47:16 Log-Likelihood: -101.06
No. Observations: 23 AIC: 210.1
Df Residuals: 19 BIC: 214.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 73.1249 207.605 0.352 0.729 -361.397 507.647
C(dose)[T.1] 23.9045 423.149 0.056 0.956 -861.756 909.565
expression -1.9310 21.183 -0.091 0.928 -46.267 42.405
expression:C(dose)[T.1] 2.9423 41.299 0.071 0.944 -83.497 89.382
Omnibus: 0.369 Durbin-Watson: 1.887
Prob(Omnibus): 0.832 Jarque-Bera (JB): 0.512
Skew: 0.060 Prob(JB): 0.774
Kurtosis: 2.279 Cond. No. 1.14e+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: 22:47:16 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 65.5419 173.754 0.377 0.710 -296.904 427.987
C(dose)[T.1] 54.0343 13.820 3.910 0.001 25.206 82.863
expression -1.1569 17.726 -0.065 0.949 -38.133 35.819
Omnibus: 0.285 Durbin-Watson: 1.884
Prob(Omnibus): 0.867 Jarque-Bera (JB): 0.462
Skew: 0.050 Prob(JB): 0.794
Kurtosis: 2.313 Cond. No. 406.

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: 22:47:16 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: 0.381
Model: OLS Adj. R-squared: 0.351
Method: Least Squares F-statistic: 12.92
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.00170
Time: 22:47:16 Log-Likelihood: -107.59
No. Observations: 23 AIC: 219.2
Df Residuals: 21 BIC: 221.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -448.8162 147.134 -3.050 0.006 -754.798 -142.835
expression 52.4110 14.579 3.595 0.002 22.092 82.730
Omnibus: 4.123 Durbin-Watson: 2.424
Prob(Omnibus): 0.127 Jarque-Bera (JB): 1.510
Skew: 0.107 Prob(JB): 0.470
Kurtosis: 1.763 Cond. No. 264.

CP101

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

F-statistic p-value df difference
0.033 0.858 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.461
Model: OLS Adj. R-squared: 0.314
Method: Least Squares F-statistic: 3.141
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0691
Time: 22:47:16 Log-Likelihood: -70.660
No. Observations: 15 AIC: 149.3
Df Residuals: 11 BIC: 152.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -4.7769 336.762 -0.014 0.989 -745.985 736.432
C(dose)[T.1] 272.5066 470.829 0.579 0.574 -763.781 1308.794
expression 8.0213 37.388 0.215 0.834 -74.268 90.311
expression:C(dose)[T.1] -24.9312 52.462 -0.475 0.644 -140.399 90.536
Omnibus: 3.095 Durbin-Watson: 0.674
Prob(Omnibus): 0.213 Jarque-Bera (JB): 2.174
Skew: -0.910 Prob(JB): 0.337
Kurtosis: 2.594 Cond. No. 703.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.450
Model: OLS Adj. R-squared: 0.359
Method: Least Squares F-statistic: 4.915
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0276
Time: 22:47:16 Log-Likelihood: -70.812
No. Observations: 15 AIC: 147.6
Df Residuals: 12 BIC: 149.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 109.2060 228.639 0.478 0.641 -388.956 607.368
C(dose)[T.1] 48.8907 15.806 3.093 0.009 14.452 83.330
expression -4.6410 25.367 -0.183 0.858 -59.912 50.630
Omnibus: 2.855 Durbin-Watson: 0.824
Prob(Omnibus): 0.240 Jarque-Bera (JB): 1.972
Skew: -0.868 Prob(JB): 0.373
Kurtosis: 2.620 Cond. No. 265.

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: 22:47:16 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: 0.012
Model: OLS Adj. R-squared: -0.064
Method: Least Squares F-statistic: 0.1585
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.697
Time: 22:47:16 Log-Likelihood: -75.209
No. Observations: 15 AIC: 154.4
Df Residuals: 13 BIC: 155.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 209.6579 291.509 0.719 0.485 -420.109 839.425
expression -12.9359 32.491 -0.398 0.697 -83.128 57.257
Omnibus: 0.675 Durbin-Watson: 1.675
Prob(Omnibus): 0.713 Jarque-Bera (JB): 0.605
Skew: -0.020 Prob(JB): 0.739
Kurtosis: 2.017 Cond. No. 262.