LID SPN TRAP

1452135_at

Gpx6 glutathione peroxidase 6

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
3.185 0.089 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.697
Model: OLS Adj. R-squared: 0.649
Method: Least Squares F-statistic: 14.59
Date: Thu, 21 Nov 2024 Prob (F-statistic): 3.62e-05
Time: 05:08:36 Log-Likelihood: -99.363
No. Observations: 23 AIC: 206.7
Df Residuals: 19 BIC: 211.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 103.0867 38.959 2.646 0.016 21.544 184.629
C(dose)[T.1] 51.2215 55.655 0.920 0.369 -65.266 167.709
expression -11.4241 9.005 -1.269 0.220 -30.272 7.424
expression:C(dose)[T.1] 0.3055 12.974 0.024 0.981 -26.850 27.461
Omnibus: 0.382 Durbin-Watson: 2.097
Prob(Omnibus): 0.826 Jarque-Bera (JB): 0.531
Skew: 0.180 Prob(JB): 0.767
Kurtosis: 2.348 Cond. No. 77.5

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.697
Model: OLS Adj. R-squared: 0.667
Method: Least Squares F-statistic: 23.03
Date: Thu, 21 Nov 2024 Prob (F-statistic): 6.47e-06
Time: 05:08:36 Log-Likelihood: -99.363
No. Observations: 23 AIC: 204.7
Df Residuals: 20 BIC: 208.1
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 102.4570 27.616 3.710 0.001 44.852 160.062
C(dose)[T.1] 52.5172 8.158 6.437 0.000 35.500 69.535
expression -11.2769 6.319 -1.785 0.089 -24.458 1.904
Omnibus: 0.388 Durbin-Watson: 2.095
Prob(Omnibus): 0.824 Jarque-Bera (JB): 0.535
Skew: 0.185 Prob(JB): 0.765
Kurtosis: 2.351 Cond. No. 30.9

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: 05:08:36 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: 0.070
Model: OLS Adj. R-squared: 0.026
Method: Least Squares F-statistic: 1.581
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.222
Time: 05:08:36 Log-Likelihood: -112.27
No. Observations: 23 AIC: 228.5
Df Residuals: 21 BIC: 230.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 137.2947 46.320 2.964 0.007 40.967 233.622
expression -13.5675 10.791 -1.257 0.222 -36.008 8.873
Omnibus: 2.188 Durbin-Watson: 2.947
Prob(Omnibus): 0.335 Jarque-Bera (JB): 1.398
Skew: 0.344 Prob(JB): 0.497
Kurtosis: 2.007 Cond. No. 30.1

CP101

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

F-statistic p-value df difference
0.972 0.344 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.546
Model: OLS Adj. R-squared: 0.422
Method: Least Squares F-statistic: 4.404
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0288
Time: 05:08:36 Log-Likelihood: -69.383
No. Observations: 15 AIC: 146.8
Df Residuals: 11 BIC: 149.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 45.9313 54.244 0.847 0.415 -73.458 165.321
C(dose)[T.1] 132.8246 69.387 1.914 0.082 -19.895 285.544
expression 6.3387 15.668 0.405 0.694 -28.147 40.824
expression:C(dose)[T.1] -21.9165 18.885 -1.161 0.270 -63.483 19.650
Omnibus: 1.178 Durbin-Watson: 0.839
Prob(Omnibus): 0.555 Jarque-Bera (JB): 0.837
Skew: -0.246 Prob(JB): 0.658
Kurtosis: 1.952 Cond. No. 55.1

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.490
Model: OLS Adj. R-squared: 0.405
Method: Least Squares F-statistic: 5.766
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0176
Time: 05:08:36 Log-Likelihood: -70.249
No. Observations: 15 AIC: 146.5
Df Residuals: 12 BIC: 148.6
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 97.0939 32.057 3.029 0.010 27.247 166.941
C(dose)[T.1] 54.4188 16.039 3.393 0.005 19.474 89.364
expression -8.7472 8.873 -0.986 0.344 -28.079 10.585
Omnibus: 2.563 Durbin-Watson: 0.859
Prob(Omnibus): 0.278 Jarque-Bera (JB): 1.914
Skew: -0.824 Prob(JB): 0.384
Kurtosis: 2.411 Cond. No. 17.5

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: 05:08:36 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: 0.001
Model: OLS Adj. R-squared: -0.076
Method: Least Squares F-statistic: 0.01127
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.917
Time: 05:08:36 Log-Likelihood: -75.294
No. Observations: 15 AIC: 154.6
Df Residuals: 13 BIC: 156.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 89.2301 43.000 2.075 0.058 -3.665 182.125
expression 1.1959 11.263 0.106 0.917 -23.136 25.528
Omnibus: 0.699 Durbin-Watson: 1.597
Prob(Omnibus): 0.705 Jarque-Bera (JB): 0.618
Skew: 0.066 Prob(JB): 0.734
Kurtosis: 2.015 Cond. No. 17.2