LID SPN TRAP

1456036_x_at

Gsto1 glutathione S-transferase omega 1

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
0.073 0.789 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.678
Model: OLS Adj. R-squared: 0.627
Method: Least Squares F-statistic: 13.35
Date: Thu, 03 Apr 2025 Prob (F-statistic): 6.39e-05
Time: 23:03:03 Log-Likelihood: -100.07
No. Observations: 23 AIC: 208.1
Df Residuals: 19 BIC: 212.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -87.5670 179.979 -0.487 0.632 -464.267 289.133
C(dose)[T.1] 359.5995 238.915 1.505 0.149 -140.456 859.655
expression 16.5746 21.029 0.788 0.440 -27.440 60.590
expression:C(dose)[T.1] -35.6800 27.835 -1.282 0.215 -93.939 22.579
Omnibus: 0.102 Durbin-Watson: 1.929
Prob(Omnibus): 0.950 Jarque-Bera (JB): 0.285
Skew: 0.119 Prob(JB): 0.867
Kurtosis: 2.510 Cond. No. 647.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.650
Model: OLS Adj. R-squared: 0.615
Method: Least Squares F-statistic: 18.60
Date: Thu, 03 Apr 2025 Prob (F-statistic): 2.73e-05
Time: 23:03:03 Log-Likelihood: -101.02
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 86.6355 119.880 0.723 0.478 -163.431 336.702
C(dose)[T.1] 53.5482 8.788 6.093 0.000 35.216 71.881
expression -3.7910 13.997 -0.271 0.789 -32.988 25.406
Omnibus: 0.324 Durbin-Watson: 1.806
Prob(Omnibus): 0.851 Jarque-Bera (JB): 0.485
Skew: 0.032 Prob(JB): 0.785
Kurtosis: 2.292 Cond. No. 239.

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: 23:03:03 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: 0.001
Model: OLS Adj. R-squared: -0.046
Method: Least Squares F-statistic: 0.02693
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.871
Time: 23:03:03 Log-Likelihood: -113.09
No. Observations: 23 AIC: 230.2
Df Residuals: 21 BIC: 232.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 47.3397 197.434 0.240 0.813 -363.247 457.926
expression 3.7734 22.995 0.164 0.871 -44.046 51.593
Omnibus: 3.211 Durbin-Watson: 2.500
Prob(Omnibus): 0.201 Jarque-Bera (JB): 1.611
Skew: 0.325 Prob(JB): 0.447
Kurtosis: 1.878 Cond. No. 238.

CP101

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

F-statistic p-value df difference
0.003 0.956 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.465
Model: OLS Adj. R-squared: 0.319
Method: Least Squares F-statistic: 3.190
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0667
Time: 23:03:03 Log-Likelihood: -70.606
No. Observations: 15 AIC: 149.2
Df Residuals: 11 BIC: 152.0
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -172.6777 508.486 -0.340 0.741 -1291.848 946.492
C(dose)[T.1] 444.4702 683.770 0.650 0.529 -1060.498 1949.438
expression 27.7955 58.848 0.472 0.646 -101.729 157.320
expression:C(dose)[T.1] -45.2074 78.058 -0.579 0.574 -217.012 126.597
Omnibus: 4.237 Durbin-Watson: 0.930
Prob(Omnibus): 0.120 Jarque-Bera (JB): 2.555
Skew: -1.011 Prob(JB): 0.279
Kurtosis: 3.041 Cond. No. 1.03e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.449
Model: OLS Adj. R-squared: 0.357
Method: Least Squares F-statistic: 4.888
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0280
Time: 23:03:03 Log-Likelihood: -70.831
No. Observations: 15 AIC: 147.7
Df Residuals: 12 BIC: 149.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 49.2796 324.801 0.152 0.882 -658.400 756.960
C(dose)[T.1] 48.6222 18.792 2.587 0.024 7.677 89.567
expression 2.1010 37.577 0.056 0.956 -79.771 83.973
Omnibus: 2.732 Durbin-Watson: 0.806
Prob(Omnibus): 0.255 Jarque-Bera (JB): 1.894
Skew: -0.848 Prob(JB): 0.388
Kurtosis: 2.606 Cond. No. 369.

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: 23:03:04 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: 0.141
Model: OLS Adj. R-squared: 0.075
Method: Least Squares F-statistic: 2.142
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.167
Time: 23:03:04 Log-Likelihood: -74.156
No. Observations: 15 AIC: 152.3
Df Residuals: 13 BIC: 153.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -391.5245 331.610 -1.181 0.259 -1107.924 324.875
expression 55.2354 37.736 1.464 0.167 -26.288 136.759
Omnibus: 0.084 Durbin-Watson: 1.292
Prob(Omnibus): 0.959 Jarque-Bera (JB): 0.311
Skew: 0.042 Prob(JB): 0.856
Kurtosis: 2.300 Cond. No. 314.