LID SPN TRAP

1452160_at

Tiparp TCDD-inducible poly(ADP-ribose) polymerase

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
0.003 0.956 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.649
Model: OLS Adj. R-squared: 0.594
Method: Least Squares F-statistic: 11.72
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000142
Time: 03:39:30 Log-Likelihood: -101.06
No. Observations: 23 AIC: 210.1
Df Residuals: 19 BIC: 214.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 56.7171 160.432 0.354 0.728 -279.071 392.505
C(dose)[T.1] 70.1638 302.583 0.232 0.819 -563.149 703.477
expression -0.2875 18.373 -0.016 0.988 -38.743 38.168
expression:C(dose)[T.1] -1.8383 33.655 -0.055 0.957 -72.279 68.602
Omnibus: 0.265 Durbin-Watson: 1.886
Prob(Omnibus): 0.876 Jarque-Bera (JB): 0.450
Skew: 0.059 Prob(JB): 0.799
Kurtosis: 2.325 Cond. No. 727.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.649
Model: OLS Adj. R-squared: 0.614
Method: Least Squares F-statistic: 18.50
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.83e-05
Time: 03:39:30 Log-Likelihood: -101.06
No. Observations: 23 AIC: 208.1
Df Residuals: 20 BIC: 211.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 61.4975 131.063 0.469 0.644 -211.895 334.890
C(dose)[T.1] 53.6465 10.382 5.167 0.000 31.990 75.303
expression -0.8354 15.005 -0.056 0.956 -32.136 30.465
Omnibus: 0.300 Durbin-Watson: 1.895
Prob(Omnibus): 0.861 Jarque-Bera (JB): 0.472
Skew: 0.058 Prob(JB): 0.790
Kurtosis: 2.308 Cond. No. 271.

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: 03:39:30 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: 0.181
Model: OLS Adj. R-squared: 0.142
Method: Least Squares F-statistic: 4.630
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0432
Time: 03:39:30 Log-Likelihood: -110.81
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 -282.3420 168.385 -1.677 0.108 -632.519 67.835
expression 40.6702 18.901 2.152 0.043 1.364 79.976
Omnibus: 2.379 Durbin-Watson: 2.363
Prob(Omnibus): 0.304 Jarque-Bera (JB): 1.181
Skew: 0.107 Prob(JB): 0.554
Kurtosis: 1.911 Cond. No. 233.

CP101

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

F-statistic p-value df difference
1.134 0.308 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.520
Model: OLS Adj. R-squared: 0.389
Method: Least Squares F-statistic: 3.974
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0383
Time: 03:39:30 Log-Likelihood: -69.794
No. Observations: 15 AIC: 147.6
Df Residuals: 11 BIC: 150.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 239.9810 145.130 1.654 0.126 -79.449 559.411
C(dose)[T.1] -79.2898 174.059 -0.456 0.658 -462.392 303.812
expression -26.3501 22.096 -1.193 0.258 -74.984 22.284
expression:C(dose)[T.1] 19.5733 26.534 0.738 0.476 -38.827 77.974
Omnibus: 2.225 Durbin-Watson: 1.222
Prob(Omnibus): 0.329 Jarque-Bera (JB): 1.679
Skew: -0.762 Prob(JB): 0.432
Kurtosis: 2.396 Cond. No. 221.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.496
Model: OLS Adj. R-squared: 0.412
Method: Least Squares F-statistic: 5.913
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0163
Time: 03:39:30 Log-Likelihood: -70.156
No. Observations: 15 AIC: 146.3
Df Residuals: 12 BIC: 148.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 151.0921 79.336 1.904 0.081 -21.767 323.951
C(dose)[T.1] 48.6088 15.055 3.229 0.007 15.807 81.411
expression -12.7761 11.999 -1.065 0.308 -38.919 13.367
Omnibus: 2.306 Durbin-Watson: 0.982
Prob(Omnibus): 0.316 Jarque-Bera (JB): 1.773
Skew: -0.761 Prob(JB): 0.412
Kurtosis: 2.280 Cond. No. 71.1

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: 03:39:31 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: 0.059
Model: OLS Adj. R-squared: -0.014
Method: Least Squares F-statistic: 0.8126
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.384
Time: 03:39:31 Log-Likelihood: -74.845
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 186.2805 103.211 1.805 0.094 -36.694 409.255
expression -14.1960 15.748 -0.901 0.384 -48.218 19.826
Omnibus: 1.009 Durbin-Watson: 1.666
Prob(Omnibus): 0.604 Jarque-Bera (JB): 0.782
Skew: 0.240 Prob(JB): 0.676
Kurtosis: 1.990 Cond. No. 70.2