LID SPN TRAP

1460639_a_at

Atox1 ATX1 (antioxidant protein 1) homolog 1 (yeast)

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
1.057 0.316 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.669
Model: OLS Adj. R-squared: 0.617
Method: Least Squares F-statistic: 12.80
Date: Thu, 21 Nov 2024 Prob (F-statistic): 8.30e-05
Time: 05:00:23 Log-Likelihood: -100.39
No. Observations: 23 AIC: 208.8
Df Residuals: 19 BIC: 213.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -49.7714 143.408 -0.347 0.732 -349.928 250.385
C(dose)[T.1] -65.7673 318.335 -0.207 0.839 -732.049 600.515
expression 10.8782 14.990 0.726 0.477 -20.496 42.252
expression:C(dose)[T.1] 11.8251 32.571 0.363 0.721 -56.346 79.996
Omnibus: 0.328 Durbin-Watson: 1.747
Prob(Omnibus): 0.849 Jarque-Bera (JB): 0.484
Skew: -0.201 Prob(JB): 0.785
Kurtosis: 2.414 Cond. No. 834.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.667
Model: OLS Adj. R-squared: 0.633
Method: Least Squares F-statistic: 20.00
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.69e-05
Time: 05:00:23 Log-Likelihood: -100.47
No. Observations: 23 AIC: 206.9
Df Residuals: 20 BIC: 210.3
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -73.7123 124.554 -0.592 0.561 -333.527 186.102
C(dose)[T.1] 49.7568 9.229 5.391 0.000 30.505 69.008
expression 13.3829 13.016 1.028 0.316 -13.768 40.534
Omnibus: 0.480 Durbin-Watson: 1.711
Prob(Omnibus): 0.787 Jarque-Bera (JB): 0.590
Skew: -0.159 Prob(JB): 0.745
Kurtosis: 2.283 Cond. No. 286.

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: 05:00:23 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: 0.182
Model: OLS Adj. R-squared: 0.143
Method: Least Squares F-statistic: 4.680
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0422
Time: 05:00:23 Log-Likelihood: -110.79
No. Observations: 23 AIC: 225.6
Df Residuals: 21 BIC: 227.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -306.3841 178.595 -1.716 0.101 -677.792 65.024
expression 39.8599 18.425 2.163 0.042 1.543 78.177
Omnibus: 1.786 Durbin-Watson: 2.241
Prob(Omnibus): 0.409 Jarque-Bera (JB): 1.010
Skew: 0.003 Prob(JB): 0.604
Kurtosis: 1.973 Cond. No. 268.

CP101

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

F-statistic p-value df difference
4.842 0.048 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.612
Model: OLS Adj. R-squared: 0.506
Method: Least Squares F-statistic: 5.789
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0126
Time: 05:00:23 Log-Likelihood: -68.195
No. Observations: 15 AIC: 144.4
Df Residuals: 11 BIC: 147.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -408.6159 267.938 -1.525 0.155 -998.343 181.111
C(dose)[T.1] 184.2517 388.135 0.475 0.644 -670.028 1038.531
expression 49.7096 27.959 1.778 0.103 -11.827 111.247
expression:C(dose)[T.1] -15.0388 39.950 -0.376 0.714 -102.969 72.891
Omnibus: 1.166 Durbin-Watson: 1.665
Prob(Omnibus): 0.558 Jarque-Bera (JB): 0.767
Skew: -0.526 Prob(JB): 0.681
Kurtosis: 2.651 Cond. No. 733.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.607
Model: OLS Adj. R-squared: 0.542
Method: Least Squares F-statistic: 9.277
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00367
Time: 05:00:23 Log-Likelihood: -68.291
No. Observations: 15 AIC: 142.6
Df Residuals: 12 BIC: 144.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -338.0785 184.541 -1.832 0.092 -740.160 64.003
C(dose)[T.1] 38.2480 14.187 2.696 0.019 7.337 69.159
expression 42.3439 19.244 2.200 0.048 0.416 84.272
Omnibus: 0.984 Durbin-Watson: 1.550
Prob(Omnibus): 0.611 Jarque-Bera (JB): 0.654
Skew: -0.480 Prob(JB): 0.721
Kurtosis: 2.645 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: 05:00:23 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: 0.369
Model: OLS Adj. R-squared: 0.321
Method: Least Squares F-statistic: 7.614
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0162
Time: 05:00:23 Log-Likelihood: -71.842
No. Observations: 15 AIC: 147.7
Df Residuals: 13 BIC: 149.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -494.4358 213.286 -2.318 0.037 -955.212 -33.659
expression 60.5392 21.940 2.759 0.016 13.141 107.937
Omnibus: 1.806 Durbin-Watson: 2.237
Prob(Omnibus): 0.405 Jarque-Bera (JB): 1.427
Skew: 0.643 Prob(JB): 0.490
Kurtosis: 2.207 Cond. No. 260.