LID SPN TRAP

1436966_at

Peli2 pellino 2

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
0.130 0.722 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.652
Model: OLS Adj. R-squared: 0.597
Method: Least Squares F-statistic: 11.84
Date: Tue, 04 Nov 2025 Prob (F-statistic): 0.000133
Time: 09:55:38 Log-Likelihood: -100.98
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 5.7358 149.002 0.038 0.970 -306.130 317.601
C(dose)[T.1] 79.1972 195.558 0.405 0.690 -330.110 488.504
expression 5.9319 18.219 0.326 0.748 -32.200 44.064
expression:C(dose)[T.1] -2.9600 24.663 -0.120 0.906 -54.581 48.661
Omnibus: 0.263 Durbin-Watson: 1.943
Prob(Omnibus): 0.877 Jarque-Bera (JB): 0.448
Skew: 0.027 Prob(JB): 0.799
Kurtosis: 2.318 Cond. No. 465.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.651
Model: OLS Adj. R-squared: 0.616
Method: Least Squares F-statistic: 18.68
Date: Tue, 04 Nov 2025 Prob (F-statistic): 2.66e-05
Time: 09:55:38 Log-Likelihood: -100.99
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 18.9342 98.030 0.193 0.849 -185.552 223.420
C(dose)[T.1] 55.7665 11.037 5.053 0.000 32.743 78.790
expression 4.3167 11.974 0.361 0.722 -20.660 29.293
Omnibus: 0.113 Durbin-Watson: 1.922
Prob(Omnibus): 0.945 Jarque-Bera (JB): 0.337
Skew: 0.027 Prob(JB): 0.845
Kurtosis: 2.410 Cond. No. 181.

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: Tue, 04 Nov 2025 Prob (F-statistic): 3.51e-06
Time: 09:55:38 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: 0.206
Model: OLS Adj. R-squared: 0.168
Method: Least Squares F-statistic: 5.457
Date: Tue, 04 Nov 2025 Prob (F-statistic): 0.0295
Time: 09:55:38 Log-Likelihood: -110.45
No. Observations: 23 AIC: 224.9
Df Residuals: 21 BIC: 227.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 337.4805 110.529 3.053 0.006 107.623 567.338
expression -32.6186 13.963 -2.336 0.029 -61.657 -3.581
Omnibus: 1.138 Durbin-Watson: 2.067
Prob(Omnibus): 0.566 Jarque-Bera (JB): 1.068
Skew: 0.402 Prob(JB): 0.586
Kurtosis: 2.315 Cond. No. 138.

CP101

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

F-statistic p-value df difference
2.687 0.127 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.551
Model: OLS Adj. R-squared: 0.429
Method: Least Squares F-statistic: 4.506
Date: Tue, 04 Nov 2025 Prob (F-statistic): 0.0270
Time: 09:55:38 Log-Likelihood: -69.289
No. Observations: 15 AIC: 146.6
Df Residuals: 11 BIC: 149.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 342.0526 512.371 0.668 0.518 -785.668 1469.773
C(dose)[T.1] 141.8460 568.498 0.250 0.808 -1109.409 1393.101
expression -31.0424 57.903 -0.536 0.603 -158.487 96.402
expression:C(dose)[T.1] -13.3941 65.110 -0.206 0.841 -156.701 129.913
Omnibus: 0.301 Durbin-Watson: 0.801
Prob(Omnibus): 0.860 Jarque-Bera (JB): 0.458
Skew: -0.170 Prob(JB): 0.796
Kurtosis: 2.215 Cond. No. 999.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.550
Model: OLS Adj. R-squared: 0.475
Method: Least Squares F-statistic: 7.322
Date: Tue, 04 Nov 2025 Prob (F-statistic): 0.00835
Time: 09:55:38 Log-Likelihood: -69.318
No. Observations: 15 AIC: 144.6
Df Residuals: 12 BIC: 146.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 435.7663 224.954 1.937 0.077 -54.367 925.899
C(dose)[T.1] 24.9813 20.510 1.218 0.247 -19.706 69.669
expression -41.6355 25.401 -1.639 0.127 -96.979 13.708
Omnibus: 0.560 Durbin-Watson: 0.804
Prob(Omnibus): 0.756 Jarque-Bera (JB): 0.595
Skew: -0.196 Prob(JB): 0.743
Kurtosis: 2.107 Cond. No. 276.

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: Tue, 04 Nov 2025 Prob (F-statistic): 0.00629
Time: 09:55:38 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: 0.494
Model: OLS Adj. R-squared: 0.455
Method: Least Squares F-statistic: 12.69
Date: Tue, 04 Nov 2025 Prob (F-statistic): 0.00348
Time: 09:55:38 Log-Likelihood: -70.192
No. Observations: 15 AIC: 144.4
Df Residuals: 13 BIC: 145.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 639.3224 153.356 4.169 0.001 308.017 970.627
expression -63.9200 17.945 -3.562 0.003 -102.687 -25.153
Omnibus: 2.206 Durbin-Watson: 1.170
Prob(Omnibus): 0.332 Jarque-Bera (JB): 0.921
Skew: 0.600 Prob(JB): 0.631
Kurtosis: 3.186 Cond. No. 184.