LID SPN TRAP

1454630_at

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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
0.156 0.697 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.660
Model: OLS Adj. R-squared: 0.607
Method: Least Squares F-statistic: 12.30
Date: Tue, 28 Oct 2025 Prob (F-statistic): 0.000106
Time: 00:04:57 Log-Likelihood: -100.69
No. Observations: 23 AIC: 209.4
Df Residuals: 19 BIC: 213.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 206.1803 195.702 1.054 0.305 -203.429 615.790
C(dose)[T.1] -118.8445 248.347 -0.479 0.638 -638.640 400.951
expression -22.5801 29.063 -0.777 0.447 -83.410 38.250
expression:C(dose)[T.1] 25.7268 37.556 0.685 0.502 -52.878 104.332
Omnibus: 0.181 Durbin-Watson: 2.039
Prob(Omnibus): 0.913 Jarque-Bera (JB): 0.329
Skew: 0.173 Prob(JB): 0.848
Kurtosis: 2.528 Cond. No. 513.

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: 18.72
Date: Tue, 28 Oct 2025 Prob (F-statistic): 2.62e-05
Time: 00:04:57 Log-Likelihood: -100.97
No. Observations: 23 AIC: 207.9
Df Residuals: 20 BIC: 211.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 102.4837 122.380 0.837 0.412 -152.796 357.763
C(dose)[T.1] 51.1285 10.372 4.929 0.000 29.492 72.765
expression -7.1728 18.161 -0.395 0.697 -45.056 30.711
Omnibus: 0.153 Durbin-Watson: 1.984
Prob(Omnibus): 0.927 Jarque-Bera (JB): 0.367
Skew: 0.071 Prob(JB): 0.832
Kurtosis: 2.398 Cond. No. 190.

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, 28 Oct 2025 Prob (F-statistic): 3.51e-06
Time: 00:04:57 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: 0.229
Model: OLS Adj. R-squared: 0.192
Method: Least Squares F-statistic: 6.227
Date: Tue, 28 Oct 2025 Prob (F-statistic): 0.0210
Time: 00:04:57 Log-Likelihood: -110.12
No. Observations: 23 AIC: 224.2
Df Residuals: 21 BIC: 226.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 444.6616 146.381 3.038 0.006 140.245 749.078
expression -55.4367 22.215 -2.495 0.021 -101.636 -9.238
Omnibus: 4.177 Durbin-Watson: 2.557
Prob(Omnibus): 0.124 Jarque-Bera (JB): 2.033
Skew: 0.434 Prob(JB): 0.362
Kurtosis: 1.830 Cond. No. 156.

CP101

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

F-statistic p-value df difference
0.071 0.794 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.453
Model: OLS Adj. R-squared: 0.304
Method: Least Squares F-statistic: 3.034
Date: Tue, 28 Oct 2025 Prob (F-statistic): 0.0749
Time: 00:04:57 Log-Likelihood: -70.778
No. Observations: 15 AIC: 149.6
Df Residuals: 11 BIC: 152.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 34.6192 248.256 0.139 0.892 -511.788 581.027
C(dose)[T.1] -5.2979 425.366 -0.012 0.990 -941.522 930.926
expression 5.4645 41.300 0.132 0.897 -85.436 96.365
expression:C(dose)[T.1] 8.7926 69.886 0.126 0.902 -145.025 162.610
Omnibus: 2.256 Durbin-Watson: 0.879
Prob(Omnibus): 0.324 Jarque-Bera (JB): 1.586
Skew: -0.766 Prob(JB): 0.452
Kurtosis: 2.566 Cond. No. 405.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.452
Model: OLS Adj. R-squared: 0.361
Method: Least Squares F-statistic: 4.950
Date: Tue, 28 Oct 2025 Prob (F-statistic): 0.0271
Time: 00:04:57 Log-Likelihood: -70.788
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 16.1826 191.999 0.084 0.934 -402.147 434.512
C(dose)[T.1] 48.1768 16.150 2.983 0.011 12.990 83.364
expression 8.5352 31.921 0.267 0.794 -61.015 78.086
Omnibus: 2.537 Durbin-Watson: 0.911
Prob(Omnibus): 0.281 Jarque-Bera (JB): 1.759
Skew: -0.814 Prob(JB): 0.415
Kurtosis: 2.598 Cond. No. 154.

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, 28 Oct 2025 Prob (F-statistic): 0.00629
Time: 00:04:57 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: 0.046
Model: OLS Adj. R-squared: -0.028
Method: Least Squares F-statistic: 0.6222
Date: Tue, 28 Oct 2025 Prob (F-statistic): 0.444
Time: 00:04:57 Log-Likelihood: -74.949
No. Observations: 15 AIC: 153.9
Df Residuals: 13 BIC: 155.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -94.5638 238.846 -0.396 0.699 -610.558 421.431
expression 31.0214 39.329 0.789 0.444 -53.944 115.986
Omnibus: 0.040 Durbin-Watson: 1.705
Prob(Omnibus): 0.980 Jarque-Bera (JB): 0.261
Skew: 0.049 Prob(JB): 0.878
Kurtosis: 2.362 Cond. No. 150.