LID SPN TRAP

1459807_x_at

P4htm prolyl 4-hydroxylase, transmembrane (endoplasmic reticulum)

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
0.775 0.389 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.690
Model: OLS Adj. R-squared: 0.641
Method: Least Squares F-statistic: 14.11
Date: Fri, 31 Oct 2025 Prob (F-statistic): 4.48e-05
Time: 15:28:18 Log-Likelihood: -99.627
No. Observations: 23 AIC: 207.3
Df Residuals: 19 BIC: 211.8
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 199.8941 103.553 1.930 0.069 -16.845 416.634
C(dose)[T.1] -235.2051 220.143 -1.068 0.299 -695.971 225.560
expression -21.0898 14.967 -1.409 0.175 -52.415 10.236
expression:C(dose)[T.1] 41.7075 31.770 1.313 0.205 -24.788 108.203
Omnibus: 0.201 Durbin-Watson: 1.717
Prob(Omnibus): 0.905 Jarque-Bera (JB): 0.315
Skew: 0.189 Prob(JB): 0.854
Kurtosis: 2.568 Cond. No. 430.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.662
Model: OLS Adj. R-squared: 0.628
Method: Least Squares F-statistic: 19.60
Date: Fri, 31 Oct 2025 Prob (F-statistic): 1.94e-05
Time: 15:28:18 Log-Likelihood: -100.63
No. Observations: 23 AIC: 207.3
Df Residuals: 20 BIC: 210.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 135.9538 93.022 1.462 0.159 -58.087 329.995
C(dose)[T.1] 53.5847 8.609 6.224 0.000 35.626 71.543
expression -11.8336 13.438 -0.881 0.389 -39.866 16.199
Omnibus: 0.187 Durbin-Watson: 1.676
Prob(Omnibus): 0.911 Jarque-Bera (JB): 0.384
Skew: 0.128 Prob(JB): 0.825
Kurtosis: 2.421 Cond. No. 153.

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: Fri, 31 Oct 2025 Prob (F-statistic): 3.51e-06
Time: 15:28:18 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: 0.008
Model: OLS Adj. R-squared: -0.039
Method: Least Squares F-statistic: 0.1642
Date: Fri, 31 Oct 2025 Prob (F-statistic): 0.689
Time: 15:28:18 Log-Likelihood: -113.02
No. Observations: 23 AIC: 230.0
Df Residuals: 21 BIC: 232.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 142.6853 155.565 0.917 0.369 -180.830 466.201
expression -9.1022 22.463 -0.405 0.689 -55.817 37.613
Omnibus: 3.926 Durbin-Watson: 2.470
Prob(Omnibus): 0.140 Jarque-Bera (JB): 1.861
Skew: 0.380 Prob(JB): 0.394
Kurtosis: 1.832 Cond. No. 153.

CP101

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

F-statistic p-value df difference
0.803 0.388 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.499
Model: OLS Adj. R-squared: 0.363
Method: Least Squares F-statistic: 3.659
Date: Fri, 31 Oct 2025 Prob (F-statistic): 0.0475
Time: 15:28:18 Log-Likelihood: -70.109
No. Observations: 15 AIC: 148.2
Df Residuals: 11 BIC: 151.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -158.4416 215.049 -0.737 0.477 -631.761 314.877
C(dose)[T.1] 250.7243 340.768 0.736 0.477 -499.301 1000.749
expression 30.7854 29.269 1.052 0.315 -33.635 95.206
expression:C(dose)[T.1] -27.4952 46.164 -0.596 0.563 -129.102 74.111
Omnibus: 2.136 Durbin-Watson: 0.783
Prob(Omnibus): 0.344 Jarque-Bera (JB): 1.509
Skew: -0.744 Prob(JB): 0.470
Kurtosis: 2.556 Cond. No. 417.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.483
Model: OLS Adj. R-squared: 0.397
Method: Least Squares F-statistic: 5.613
Date: Fri, 31 Oct 2025 Prob (F-statistic): 0.0190
Time: 15:28:18 Log-Likelihood: -70.347
No. Observations: 15 AIC: 146.7
Df Residuals: 12 BIC: 148.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -77.3496 161.922 -0.478 0.641 -430.147 275.447
C(dose)[T.1] 47.9798 15.298 3.136 0.009 14.648 81.312
expression 19.7328 22.017 0.896 0.388 -28.239 67.704
Omnibus: 2.085 Durbin-Watson: 0.648
Prob(Omnibus): 0.353 Jarque-Bera (JB): 1.582
Skew: -0.735 Prob(JB): 0.453
Kurtosis: 2.393 Cond. No. 160.

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: Fri, 31 Oct 2025 Prob (F-statistic): 0.00629
Time: 15:28:18 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: 0.060
Model: OLS Adj. R-squared: -0.012
Method: Least Squares F-statistic: 0.8278
Date: Fri, 31 Oct 2025 Prob (F-statistic): 0.379
Time: 15:28:18 Log-Likelihood: -74.837
No. Observations: 15 AIC: 153.7
Df Residuals: 13 BIC: 155.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -96.9179 209.701 -0.462 0.652 -549.949 356.113
expression 25.8602 28.423 0.910 0.379 -35.543 87.263
Omnibus: 3.273 Durbin-Watson: 1.577
Prob(Omnibus): 0.195 Jarque-Bera (JB): 1.317
Skew: 0.286 Prob(JB): 0.518
Kurtosis: 1.666 Cond. No. 160.