LID SPN TRAP

1460725_at

Xpa xeroderma pigmentosum, complementation group A

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
0.032 0.860 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.673
Model: OLS Adj. R-squared: 0.621
Method: Least Squares F-statistic: 13.02
Date: Thu, 21 Nov 2024 Prob (F-statistic): 7.45e-05
Time: 04:33:58 Log-Likelihood: -100.26
No. Observations: 23 AIC: 208.5
Df Residuals: 19 BIC: 213.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -26.4343 99.923 -0.265 0.794 -235.575 182.706
C(dose)[T.1] 252.1958 171.065 1.474 0.157 -105.848 610.240
expression 10.3015 12.741 0.809 0.429 -16.366 36.969
expression:C(dose)[T.1] -25.9919 22.389 -1.161 0.260 -72.853 20.869
Omnibus: 0.323 Durbin-Watson: 1.657
Prob(Omnibus): 0.851 Jarque-Bera (JB): 0.233
Skew: -0.216 Prob(JB): 0.890
Kurtosis: 2.763 Cond. No. 371.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.650
Model: OLS Adj. R-squared: 0.615
Method: Least Squares F-statistic: 18.54
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.79e-05
Time: 04:33:58 Log-Likelihood: -101.04
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 39.4613 82.947 0.476 0.639 -133.564 212.486
C(dose)[T.1] 53.8910 9.297 5.796 0.000 34.497 73.285
expression 1.8838 10.568 0.178 0.860 -20.160 23.927
Omnibus: 0.411 Durbin-Watson: 1.819
Prob(Omnibus): 0.814 Jarque-Bera (JB): 0.534
Skew: 0.048 Prob(JB): 0.766
Kurtosis: 2.260 Cond. No. 149.

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: 04:33:58 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: 0.061
Model: OLS Adj. R-squared: 0.016
Method: Least Squares F-statistic: 1.364
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.256
Time: 04:33:58 Log-Likelihood: -112.38
No. Observations: 23 AIC: 228.8
Df Residuals: 21 BIC: 231.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 222.5942 122.526 1.817 0.084 -32.213 477.401
expression -18.5852 15.912 -1.168 0.256 -51.676 14.506
Omnibus: 3.588 Durbin-Watson: 2.760
Prob(Omnibus): 0.166 Jarque-Bera (JB): 1.414
Skew: 0.097 Prob(JB): 0.493
Kurtosis: 1.801 Cond. No. 137.

CP101

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

F-statistic p-value df difference
1.640 0.225 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.517
Model: OLS Adj. R-squared: 0.385
Method: Least Squares F-statistic: 3.920
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0397
Time: 04:33:58 Log-Likelihood: -69.847
No. Observations: 15 AIC: 147.7
Df Residuals: 11 BIC: 150.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -118.3441 163.691 -0.723 0.485 -478.626 241.938
C(dose)[T.1] 105.2299 306.449 0.343 0.738 -569.260 779.720
expression 24.8100 21.809 1.138 0.279 -23.192 72.812
expression:C(dose)[T.1] -7.7759 40.382 -0.193 0.851 -96.657 81.105
Omnibus: 1.189 Durbin-Watson: 0.999
Prob(Omnibus): 0.552 Jarque-Bera (JB): 0.665
Skew: -0.503 Prob(JB): 0.717
Kurtosis: 2.775 Cond. No. 376.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.515
Model: OLS Adj. R-squared: 0.434
Method: Least Squares F-statistic: 6.372
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0130
Time: 04:33:58 Log-Likelihood: -69.872
No. Observations: 15 AIC: 145.7
Df Residuals: 12 BIC: 147.9
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -101.3610 132.251 -0.766 0.458 -389.510 186.788
C(dose)[T.1] 46.2967 14.936 3.100 0.009 13.754 78.839
expression 22.5419 17.603 1.281 0.225 -15.812 60.896
Omnibus: 0.940 Durbin-Watson: 1.050
Prob(Omnibus): 0.625 Jarque-Bera (JB): 0.426
Skew: -0.406 Prob(JB): 0.808
Kurtosis: 2.855 Cond. No. 139.

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: 04:33:58 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: 0.127
Model: OLS Adj. R-squared: 0.060
Method: Least Squares F-statistic: 1.887
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.193
Time: 04:33:58 Log-Likelihood: -74.284
No. Observations: 15 AIC: 152.6
Df Residuals: 13 BIC: 154.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -139.1818 169.777 -0.820 0.427 -505.962 227.598
expression 30.8147 22.433 1.374 0.193 -17.648 79.278
Omnibus: 1.016 Durbin-Watson: 1.817
Prob(Omnibus): 0.602 Jarque-Bera (JB): 0.898
Skew: 0.483 Prob(JB): 0.638
Kurtosis: 2.290 Cond. No. 138.