LID SPN TRAP

1433494_at

Dos downstream of Stk11

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
0.150 0.702 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.653
Model: OLS Adj. R-squared: 0.598
Method: Least Squares F-statistic: 11.91
Date: Fri, 31 Oct 2025 Prob (F-statistic): 0.000129
Time: 00:18:56 Log-Likelihood: -100.94
No. Observations: 23 AIC: 209.9
Df Residuals: 19 BIC: 214.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -34.4892 194.378 -0.177 0.861 -441.326 372.348
C(dose)[T.1] 141.1951 360.012 0.392 0.699 -612.319 894.710
expression 10.2063 22.355 0.457 0.653 -36.584 56.997
expression:C(dose)[T.1] -10.1158 39.587 -0.256 0.801 -92.973 72.742
Omnibus: 0.174 Durbin-Watson: 1.850
Prob(Omnibus): 0.917 Jarque-Bera (JB): 0.379
Skew: 0.104 Prob(JB): 0.827
Kurtosis: 2.406 Cond. No. 884.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.652
Model: OLS Adj. R-squared: 0.617
Method: Least Squares F-statistic: 18.71
Date: Fri, 31 Oct 2025 Prob (F-statistic): 2.63e-05
Time: 00:18:56 Log-Likelihood: -100.98
No. Observations: 23 AIC: 208.0
Df Residuals: 20 BIC: 211.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -6.4545 156.661 -0.041 0.968 -333.244 320.335
C(dose)[T.1] 49.2703 13.656 3.608 0.002 20.785 77.755
expression 6.9804 18.013 0.388 0.702 -30.595 44.556
Omnibus: 0.016 Durbin-Watson: 1.871
Prob(Omnibus): 0.992 Jarque-Bera (JB): 0.205
Skew: 0.037 Prob(JB): 0.903
Kurtosis: 2.544 Cond. No. 328.

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: Fri, 31 Oct 2025 Prob (F-statistic): 3.51e-06
Time: 00:18:56 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: 0.425
Model: OLS Adj. R-squared: 0.398
Method: Least Squares F-statistic: 15.52
Date: Fri, 31 Oct 2025 Prob (F-statistic): 0.000751
Time: 00:18:56 Log-Likelihood: -106.74
No. Observations: 23 AIC: 217.5
Df Residuals: 21 BIC: 219.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -430.8903 129.735 -3.321 0.003 -700.689 -161.092
expression 56.9298 14.452 3.939 0.001 26.876 86.984
Omnibus: 0.959 Durbin-Watson: 1.902
Prob(Omnibus): 0.619 Jarque-Bera (JB): 0.783
Skew: 0.106 Prob(JB): 0.676
Kurtosis: 2.122 Cond. No. 215.

CP101

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

F-statistic p-value df difference
3.925 0.071 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.585
Model: OLS Adj. R-squared: 0.471
Method: Least Squares F-statistic: 5.162
Date: Fri, 31 Oct 2025 Prob (F-statistic): 0.0181
Time: 00:18:56 Log-Likelihood: -68.710
No. Observations: 15 AIC: 145.4
Df Residuals: 11 BIC: 148.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -388.3678 406.947 -0.954 0.360 -1284.053 507.317
C(dose)[T.1] 70.3084 496.278 0.142 0.890 -1021.992 1162.609
expression 51.1845 45.684 1.120 0.286 -49.365 151.734
expression:C(dose)[T.1] -2.2545 55.751 -0.040 0.968 -124.961 120.452
Omnibus: 1.780 Durbin-Watson: 1.308
Prob(Omnibus): 0.411 Jarque-Bera (JB): 0.970
Skew: -0.212 Prob(JB): 0.616
Kurtosis: 1.828 Cond. No. 901.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.585
Model: OLS Adj. R-squared: 0.515
Method: Least Squares F-statistic: 8.445
Date: Fri, 31 Oct 2025 Prob (F-statistic): 0.00514
Time: 00:18:56 Log-Likelihood: -68.711
No. Observations: 15 AIC: 143.4
Df Residuals: 12 BIC: 145.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -374.8876 223.490 -1.677 0.119 -861.831 112.056
C(dose)[T.1] 50.2482 13.673 3.675 0.003 20.456 80.040
expression 49.6707 25.072 1.981 0.071 -4.957 104.298
Omnibus: 1.745 Durbin-Watson: 1.303
Prob(Omnibus): 0.418 Jarque-Bera (JB): 0.965
Skew: -0.215 Prob(JB): 0.617
Kurtosis: 1.835 Cond. No. 296.

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: Fri, 31 Oct 2025 Prob (F-statistic): 0.00629
Time: 00:18:56 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: 0.117
Model: OLS Adj. R-squared: 0.049
Method: Least Squares F-statistic: 1.725
Date: Fri, 31 Oct 2025 Prob (F-statistic): 0.212
Time: 00:18:56 Log-Likelihood: -74.365
No. Observations: 15 AIC: 152.7
Df Residuals: 13 BIC: 154.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -316.2706 312.241 -1.013 0.330 -990.825 358.284
expression 46.0931 35.092 1.314 0.212 -29.718 121.904
Omnibus: 0.800 Durbin-Watson: 1.902
Prob(Omnibus): 0.670 Jarque-Bera (JB): 0.743
Skew: 0.312 Prob(JB): 0.690
Kurtosis: 2.106 Cond. No. 295.