LID SPN TRAP

1451236_at

Rerg RAS-like, estrogen-regulated, growth-inhibitor

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
0.002 0.961 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.651
Model: OLS Adj. R-squared: 0.596
Method: Least Squares F-statistic: 11.82
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.000135
Time: 22:58:24 Log-Likelihood: -101.00
No. Observations: 23 AIC: 210.0
Df Residuals: 19 BIC: 214.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 30.5297 152.277 0.200 0.843 -288.189 349.248
C(dose)[T.1] 131.2394 240.845 0.545 0.592 -372.855 635.333
expression 3.0438 19.558 0.156 0.878 -37.892 43.979
expression:C(dose)[T.1] -10.7239 32.866 -0.326 0.748 -79.513 58.065
Omnibus: 0.771 Durbin-Watson: 1.896
Prob(Omnibus): 0.680 Jarque-Bera (JB): 0.710
Skew: 0.105 Prob(JB): 0.701
Kurtosis: 2.166 Cond. No. 495.

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: 22:58:24 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 60.0729 119.668 0.502 0.621 -189.551 309.697
C(dose)[T.1] 52.7950 14.106 3.743 0.001 23.371 82.219
expression -0.7539 15.363 -0.049 0.961 -32.800 31.293
Omnibus: 0.319 Durbin-Watson: 1.891
Prob(Omnibus): 0.853 Jarque-Bera (JB): 0.484
Skew: 0.071 Prob(JB): 0.785
Kurtosis: 2.304 Cond. No. 208.

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: 22:58:24 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: 0.403
Model: OLS Adj. R-squared: 0.375
Method: Least Squares F-statistic: 14.19
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.00113
Time: 22:58:24 Log-Likelihood: -107.17
No. Observations: 23 AIC: 218.3
Df Residuals: 21 BIC: 220.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 420.2051 90.547 4.641 0.000 231.903 608.507
expression -45.7924 12.155 -3.768 0.001 -71.069 -20.516
Omnibus: 2.048 Durbin-Watson: 2.141
Prob(Omnibus): 0.359 Jarque-Bera (JB): 1.700
Skew: 0.540 Prob(JB): 0.427
Kurtosis: 2.222 Cond. No. 123.

CP101

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

F-statistic p-value df difference
0.652 0.435 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.501
Model: OLS Adj. R-squared: 0.365
Method: Least Squares F-statistic: 3.683
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0467
Time: 22:58:24 Log-Likelihood: -70.084
No. Observations: 15 AIC: 148.2
Df Residuals: 11 BIC: 151.0
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -337.4331 377.555 -0.894 0.391 -1168.426 493.560
C(dose)[T.1] 482.2917 596.648 0.808 0.436 -830.922 1795.505
expression 47.8518 44.604 1.073 0.306 -50.321 146.024
expression:C(dose)[T.1] -51.1809 70.395 -0.727 0.482 -206.118 103.757
Omnibus: 2.092 Durbin-Watson: 0.735
Prob(Omnibus): 0.351 Jarque-Bera (JB): 1.407
Skew: -0.728 Prob(JB): 0.495
Kurtosis: 2.638 Cond. No. 837.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.477
Model: OLS Adj. R-squared: 0.390
Method: Least Squares F-statistic: 5.476
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0204
Time: 22:58:24 Log-Likelihood: -70.436
No. Observations: 15 AIC: 146.9
Df Residuals: 12 BIC: 149.0
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -163.5794 286.384 -0.571 0.578 -787.557 460.399
C(dose)[T.1] 48.6439 15.344 3.170 0.008 15.212 82.076
expression 27.3035 33.823 0.807 0.435 -46.390 100.997
Omnibus: 3.080 Durbin-Watson: 0.677
Prob(Omnibus): 0.214 Jarque-Bera (JB): 1.897
Skew: -0.868 Prob(JB): 0.387
Kurtosis: 2.851 Cond. No. 322.

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: 22:58:24 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: 0.039
Model: OLS Adj. R-squared: -0.035
Method: Least Squares F-statistic: 0.5316
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.479
Time: 22:58:24 Log-Likelihood: -74.999
No. Observations: 15 AIC: 154.0
Df Residuals: 13 BIC: 155.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -178.1524 372.929 -0.478 0.641 -983.817 627.513
expression 32.0862 44.006 0.729 0.479 -62.983 127.155
Omnibus: 2.751 Durbin-Watson: 1.565
Prob(Omnibus): 0.253 Jarque-Bera (JB): 1.154
Skew: 0.201 Prob(JB): 0.562
Kurtosis: 1.702 Cond. No. 322.