LID SPN TRAP

1459717_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
3.276 0.085 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.701
Model: OLS Adj. R-squared: 0.653
Method: Least Squares F-statistic: 14.82
Date: Thu, 21 Nov 2024 Prob (F-statistic): 3.27e-05
Time: 05:05:32 Log-Likelihood: -99.237
No. Observations: 23 AIC: 206.5
Df Residuals: 19 BIC: 211.0
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -68.6055 85.064 -0.807 0.430 -246.647 109.436
C(dose)[T.1] 100.2893 110.318 0.909 0.375 -130.608 331.187
expression 16.5229 11.418 1.447 0.164 -7.375 40.421
expression:C(dose)[T.1] -5.5940 15.232 -0.367 0.717 -37.476 26.288
Omnibus: 3.336 Durbin-Watson: 2.065
Prob(Omnibus): 0.189 Jarque-Bera (JB): 1.414
Skew: 0.159 Prob(JB): 0.493
Kurtosis: 1.828 Cond. No. 261.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.698
Model: OLS Adj. R-squared: 0.668
Method: Least Squares F-statistic: 23.16
Date: Thu, 21 Nov 2024 Prob (F-statistic): 6.22e-06
Time: 05:05:32 Log-Likelihood: -99.318
No. Observations: 23 AIC: 204.6
Df Residuals: 20 BIC: 208.0
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -45.2426 55.234 -0.819 0.422 -160.459 69.974
C(dose)[T.1] 59.9142 8.905 6.728 0.000 41.340 78.489
expression 13.3797 7.392 1.810 0.085 -2.041 28.800
Omnibus: 2.835 Durbin-Watson: 1.979
Prob(Omnibus): 0.242 Jarque-Bera (JB): 1.364
Skew: 0.208 Prob(JB): 0.506
Kurtosis: 1.882 Cond. No. 101.

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, 21 Nov 2024 Prob (F-statistic): 3.51e-06
Time: 05:05:32 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: 0.016
Model: OLS Adj. R-squared: -0.031
Method: Least Squares F-statistic: 0.3381
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.567
Time: 05:05:32 Log-Likelihood: -112.92
No. Observations: 23 AIC: 229.8
Df Residuals: 21 BIC: 232.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 129.5167 85.941 1.507 0.147 -49.207 308.240
expression -6.9186 11.898 -0.581 0.567 -31.662 17.825
Omnibus: 2.438 Durbin-Watson: 2.425
Prob(Omnibus): 0.296 Jarque-Bera (JB): 1.293
Skew: 0.224 Prob(JB): 0.524
Kurtosis: 1.928 Cond. No. 88.4

CP101

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

F-statistic p-value df difference
4.591 0.053 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.630
Model: OLS Adj. R-squared: 0.529
Method: Least Squares F-statistic: 6.234
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00991
Time: 05:05:32 Log-Likelihood: -67.850
No. Observations: 15 AIC: 143.7
Df Residuals: 11 BIC: 146.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -206.2803 283.891 -0.727 0.483 -831.121 418.561
C(dose)[T.1] -326.0363 418.927 -0.778 0.453 -1248.088 596.016
expression 31.5192 32.672 0.965 0.355 -40.392 103.430
expression:C(dose)[T.1] 44.7152 48.745 0.917 0.379 -62.571 152.001
Omnibus: 2.481 Durbin-Watson: 1.301
Prob(Omnibus): 0.289 Jarque-Bera (JB): 1.401
Skew: -0.747 Prob(JB): 0.496
Kurtosis: 2.909 Cond. No. 705.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.601
Model: OLS Adj. R-squared: 0.535
Method: Least Squares F-statistic: 9.049
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00402
Time: 05:05:32 Log-Likelihood: -68.403
No. Observations: 15 AIC: 142.8
Df Residuals: 12 BIC: 144.9
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -380.7306 209.387 -1.818 0.094 -836.945 75.484
C(dose)[T.1] 58.0432 14.008 4.144 0.001 27.522 88.564
expression 51.6082 24.086 2.143 0.053 -0.870 104.087
Omnibus: 2.365 Durbin-Watson: 1.495
Prob(Omnibus): 0.307 Jarque-Bera (JB): 1.487
Skew: -0.761 Prob(JB): 0.475
Kurtosis: 2.751 Cond. No. 274.

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, 21 Nov 2024 Prob (F-statistic): 0.00629
Time: 05:05:32 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: 0.031
Model: OLS Adj. R-squared: -0.044
Method: Least Squares F-statistic: 0.4144
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.531
Time: 05:05:32 Log-Likelihood: -75.065
No. Observations: 15 AIC: 154.1
Df Residuals: 13 BIC: 155.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -97.0240 296.400 -0.327 0.749 -737.357 543.309
expression 22.1928 34.476 0.644 0.531 -52.287 96.673
Omnibus: 0.292 Durbin-Watson: 1.894
Prob(Omnibus): 0.864 Jarque-Bera (JB): 0.448
Skew: 0.055 Prob(JB): 0.799
Kurtosis: 2.161 Cond. No. 258.