LID SPN TRAP

1426875_s_at

Srxn1 sulfiredoxin 1 homolog (S. cerevisiae)

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
0.643 0.432 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.696
Model: OLS Adj. R-squared: 0.648
Method: Least Squares F-statistic: 14.48
Date: Thu, 03 Apr 2025 Prob (F-statistic): 3.80e-05
Time: 23:00:41 Log-Likelihood: -99.424
No. Observations: 23 AIC: 206.8
Df Residuals: 19 BIC: 211.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -255.3696 202.936 -1.258 0.223 -680.119 169.380
C(dose)[T.1] 598.2878 369.448 1.619 0.122 -174.976 1371.551
expression 31.4853 20.631 1.526 0.143 -11.696 74.666
expression:C(dose)[T.1] -54.3791 36.428 -1.493 0.152 -130.623 21.865
Omnibus: 0.515 Durbin-Watson: 1.991
Prob(Omnibus): 0.773 Jarque-Bera (JB): 0.583
Skew: -0.010 Prob(JB): 0.747
Kurtosis: 2.220 Cond. No. 1.08e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.660
Model: OLS Adj. R-squared: 0.626
Method: Least Squares F-statistic: 19.41
Date: Thu, 03 Apr 2025 Prob (F-statistic): 2.07e-05
Time: 23:00:41 Log-Likelihood: -100.70
No. Observations: 23 AIC: 207.4
Df Residuals: 20 BIC: 210.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -83.8682 172.345 -0.487 0.632 -443.374 275.638
C(dose)[T.1] 47.0379 11.673 4.030 0.001 22.688 71.388
expression 14.0429 17.518 0.802 0.432 -22.498 50.584
Omnibus: 0.127 Durbin-Watson: 2.054
Prob(Omnibus): 0.938 Jarque-Bera (JB): 0.349
Skew: -0.027 Prob(JB): 0.840
Kurtosis: 2.399 Cond. No. 407.

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: 23:00:41 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: 0.384
Model: OLS Adj. R-squared: 0.355
Method: Least Squares F-statistic: 13.09
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.00161
Time: 23:00:41 Log-Likelihood: -107.53
No. Observations: 23 AIC: 219.1
Df Residuals: 21 BIC: 221.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -538.7810 171.063 -3.150 0.005 -894.525 -183.037
expression 61.5605 17.017 3.618 0.002 26.172 96.949
Omnibus: 3.366 Durbin-Watson: 2.511
Prob(Omnibus): 0.186 Jarque-Bera (JB): 1.677
Skew: 0.344 Prob(JB): 0.432
Kurtosis: 1.870 Cond. No. 307.

CP101

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

F-statistic p-value df difference
6.079 0.030 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.655
Model: OLS Adj. R-squared: 0.560
Method: Least Squares F-statistic: 6.948
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.00686
Time: 23:00:41 Log-Likelihood: -67.328
No. Observations: 15 AIC: 142.7
Df Residuals: 11 BIC: 145.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -256.0593 366.933 -0.698 0.500 -1063.673 551.555
C(dose)[T.1] -317.3764 466.020 -0.681 0.510 -1343.079 708.326
expression 34.0708 38.634 0.882 0.397 -50.961 119.103
expression:C(dose)[T.1] 39.8277 49.378 0.807 0.437 -68.852 148.508
Omnibus: 0.644 Durbin-Watson: 1.275
Prob(Omnibus): 0.725 Jarque-Bera (JB): 0.669
Skew: -0.338 Prob(JB): 0.716
Kurtosis: 2.216 Cond. No. 955.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.634
Model: OLS Adj. R-squared: 0.573
Method: Least Squares F-statistic: 10.40
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.00240
Time: 23:00:41 Log-Likelihood: -67.759
No. Observations: 15 AIC: 141.5
Df Residuals: 12 BIC: 143.6
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -487.5462 225.281 -2.164 0.051 -978.392 3.300
C(dose)[T.1] 58.3512 13.350 4.371 0.001 29.264 87.438
expression 58.4518 23.707 2.466 0.030 6.799 110.105
Omnibus: 1.150 Durbin-Watson: 1.564
Prob(Omnibus): 0.563 Jarque-Bera (JB): 0.776
Skew: -0.142 Prob(JB): 0.678
Kurtosis: 1.922 Cond. No. 336.

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: 23:00:42 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: 0.052
Model: OLS Adj. R-squared: -0.021
Method: Least Squares F-statistic: 0.7077
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.415
Time: 23:00:42 Log-Likelihood: -74.903
No. Observations: 15 AIC: 153.8
Df Residuals: 13 BIC: 155.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -185.2002 331.638 -0.558 0.586 -901.661 531.261
expression 29.6319 35.224 0.841 0.415 -46.464 105.728
Omnibus: 0.230 Durbin-Watson: 1.964
Prob(Omnibus): 0.891 Jarque-Bera (JB): 0.414
Skew: 0.106 Prob(JB): 0.813
Kurtosis: 2.214 Cond. No. 319.