LID SPN TRAP

1451747_a_at

Atg12 autophagy-related 12 (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
1.970 0.176 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.688
Model: OLS Adj. R-squared: 0.639
Method: Least Squares F-statistic: 13.97
Date: Tue, 04 Nov 2025 Prob (F-statistic): 4.77e-05
Time: 03:30:51 Log-Likelihood: -99.706
No. Observations: 23 AIC: 207.4
Df Residuals: 19 BIC: 212.0
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -318.1702 255.915 -1.243 0.229 -853.806 217.465
C(dose)[T.1] 298.4767 357.093 0.836 0.414 -448.928 1045.881
expression 41.8930 28.783 1.455 0.162 -18.351 102.137
expression:C(dose)[T.1] -27.4403 40.351 -0.680 0.505 -111.896 57.015
Omnibus: 0.703 Durbin-Watson: 1.358
Prob(Omnibus): 0.704 Jarque-Bera (JB): 0.745
Skew: -0.256 Prob(JB): 0.689
Kurtosis: 2.282 Cond. No. 983.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.681
Model: OLS Adj. R-squared: 0.649
Method: Least Squares F-statistic: 21.30
Date: Tue, 04 Nov 2025 Prob (F-statistic): 1.11e-05
Time: 03:30:51 Log-Likelihood: -99.982
No. Observations: 23 AIC: 206.0
Df Residuals: 20 BIC: 209.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -194.0619 176.976 -1.097 0.286 -563.228 175.105
C(dose)[T.1] 55.7096 8.536 6.526 0.000 37.903 73.516
expression 27.9307 19.899 1.404 0.176 -13.579 69.440
Omnibus: 0.972 Durbin-Watson: 1.580
Prob(Omnibus): 0.615 Jarque-Bera (JB): 0.813
Skew: -0.161 Prob(JB): 0.666
Kurtosis: 2.137 Cond. No. 380.

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, 04 Nov 2025 Prob (F-statistic): 3.51e-06
Time: 03:30:51 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: 0.000
Model: OLS Adj. R-squared: -0.047
Method: Least Squares F-statistic: 0.004332
Date: Tue, 04 Nov 2025 Prob (F-statistic): 0.948
Time: 03:30:51 Log-Likelihood: -113.10
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 60.1077 298.043 0.202 0.842 -559.706 679.922
expression 2.2162 33.674 0.066 0.948 -67.813 72.246
Omnibus: 3.275 Durbin-Watson: 2.486
Prob(Omnibus): 0.194 Jarque-Bera (JB): 1.569
Skew: 0.292 Prob(JB): 0.456
Kurtosis: 1.862 Cond. No. 370.

CP101

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

F-statistic p-value df difference
2.903 0.114 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.558
Model: OLS Adj. R-squared: 0.437
Method: Least Squares F-statistic: 4.624
Date: Tue, 04 Nov 2025 Prob (F-statistic): 0.0251
Time: 03:30:51 Log-Likelihood: -69.181
No. Observations: 15 AIC: 146.4
Df Residuals: 11 BIC: 149.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -374.0787 332.470 -1.125 0.284 -1105.841 357.683
C(dose)[T.1] -97.9801 690.986 -0.142 0.890 -1618.831 1422.871
expression 50.0934 37.702 1.329 0.211 -32.889 133.076
expression:C(dose)[T.1] 15.2800 77.105 0.198 0.847 -154.427 184.987
Omnibus: 1.248 Durbin-Watson: 0.931
Prob(Omnibus): 0.536 Jarque-Bera (JB): 0.982
Skew: -0.562 Prob(JB): 0.612
Kurtosis: 2.444 Cond. No. 1.02e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.556
Model: OLS Adj. R-squared: 0.482
Method: Least Squares F-statistic: 7.518
Date: Tue, 04 Nov 2025 Prob (F-statistic): 0.00765
Time: 03:30:51 Log-Likelihood: -69.208
No. Observations: 15 AIC: 144.4
Df Residuals: 12 BIC: 146.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -406.2784 278.208 -1.460 0.170 -1012.441 199.884
C(dose)[T.1] 38.9168 15.358 2.534 0.026 5.454 72.379
expression 53.7468 31.544 1.704 0.114 -14.981 122.475
Omnibus: 1.484 Durbin-Watson: 0.932
Prob(Omnibus): 0.476 Jarque-Bera (JB): 1.122
Skew: -0.616 Prob(JB): 0.571
Kurtosis: 2.472 Cond. No. 357.

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, 04 Nov 2025 Prob (F-statistic): 0.00629
Time: 03:30:51 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: 0.319
Model: OLS Adj. R-squared: 0.266
Method: Least Squares F-statistic: 6.080
Date: Tue, 04 Nov 2025 Prob (F-statistic): 0.0284
Time: 03:30:51 Log-Likelihood: -72.422
No. Observations: 15 AIC: 148.8
Df Residuals: 13 BIC: 150.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -665.4610 307.977 -2.161 0.050 -1330.804 -0.118
expression 85.1451 34.530 2.466 0.028 10.547 159.744
Omnibus: 1.617 Durbin-Watson: 1.962
Prob(Omnibus): 0.446 Jarque-Bera (JB): 1.029
Skew: 0.332 Prob(JB): 0.598
Kurtosis: 1.902 Cond. No. 332.