LID SPN TRAP

1449519_at

Gadd45a growth arrest and DNA-damage-inducible 45 alpha

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
0.050 0.825 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: 0.601
Method: Least Squares F-statistic: 12.04
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.000121
Time: 19:43:08 Log-Likelihood: -100.85
No. Observations: 23 AIC: 209.7
Df Residuals: 19 BIC: 214.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 16.3577 96.690 0.169 0.867 -186.017 218.733
C(dose)[T.1] 220.6865 309.246 0.714 0.484 -426.572 867.945
expression 4.4920 11.452 0.392 0.699 -19.476 28.460
expression:C(dose)[T.1] -18.5862 33.952 -0.547 0.590 -89.648 52.475
Omnibus: 0.777 Durbin-Watson: 1.952
Prob(Omnibus): 0.678 Jarque-Bera (JB): 0.808
Skew: 0.308 Prob(JB): 0.668
Kurtosis: 2.320 Cond. No. 719.

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: 18.57
Date: Tue, 28 Jan 2025 Prob (F-statistic): 2.76e-05
Time: 19:43:08 Log-Likelihood: -101.03
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 34.1746 89.440 0.382 0.706 -152.394 220.743
C(dose)[T.1] 51.5258 11.908 4.327 0.000 26.685 76.366
expression 2.3775 10.590 0.225 0.825 -19.713 24.468
Omnibus: 0.243 Durbin-Watson: 1.855
Prob(Omnibus): 0.886 Jarque-Bera (JB): 0.435
Skew: 0.073 Prob(JB): 0.804
Kurtosis: 2.342 Cond. No. 183.

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, 28 Jan 2025 Prob (F-statistic): 3.51e-06
Time: 19:43:08 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: 0.322
Model: OLS Adj. R-squared: 0.290
Method: Least Squares F-statistic: 9.985
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.00472
Time: 19:43:08 Log-Likelihood: -108.63
No. Observations: 23 AIC: 221.3
Df Residuals: 21 BIC: 223.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -214.0820 93.167 -2.298 0.032 -407.834 -20.330
expression 33.4219 10.577 3.160 0.005 11.426 55.418
Omnibus: 1.530 Durbin-Watson: 1.944
Prob(Omnibus): 0.465 Jarque-Bera (JB): 0.943
Skew: -0.031 Prob(JB): 0.624
Kurtosis: 2.010 Cond. No. 140.

CP101

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

F-statistic p-value df difference
8.028 0.015 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.670
Model: OLS Adj. R-squared: 0.581
Method: Least Squares F-statistic: 7.460
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.00534
Time: 19:43:08 Log-Likelihood: -66.974
No. Observations: 15 AIC: 141.9
Df Residuals: 11 BIC: 144.8
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -93.3230 116.568 -0.801 0.440 -349.887 163.241
C(dose)[T.1] 84.4633 128.582 0.657 0.525 -198.543 367.470
expression 27.3510 19.770 1.383 0.194 -16.163 70.865
expression:C(dose)[T.1] -3.5251 22.234 -0.159 0.877 -52.461 45.411
Omnibus: 4.021 Durbin-Watson: 1.552
Prob(Omnibus): 0.134 Jarque-Bera (JB): 2.167
Skew: -0.924 Prob(JB): 0.338
Kurtosis: 3.229 Cond. No. 175.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.670
Model: OLS Adj. R-squared: 0.615
Method: Least Squares F-statistic: 12.17
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.00130
Time: 19:43:08 Log-Likelihood: -66.991
No. Observations: 15 AIC: 140.0
Df Residuals: 12 BIC: 142.1
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -76.9412 51.725 -1.487 0.163 -189.641 35.758
C(dose)[T.1] 64.1957 13.284 4.833 0.000 35.253 93.139
expression 24.5637 8.670 2.833 0.015 5.674 43.453
Omnibus: 4.090 Durbin-Watson: 1.511
Prob(Omnibus): 0.129 Jarque-Bera (JB): 2.228
Skew: -0.937 Prob(JB): 0.328
Kurtosis: 3.222 Cond. No. 49.7

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, 28 Jan 2025 Prob (F-statistic): 0.00629
Time: 19:43:08 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: 0.027
Model: OLS Adj. R-squared: -0.048
Method: Least Squares F-statistic: 0.3600
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.559
Time: 19:43:08 Log-Likelihood: -75.095
No. Observations: 15 AIC: 154.2
Df Residuals: 13 BIC: 155.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 49.9907 73.484 0.680 0.508 -108.762 208.744
expression 7.8672 13.113 0.600 0.559 -20.461 36.195
Omnibus: 0.833 Durbin-Watson: 1.890
Prob(Omnibus): 0.659 Jarque-Bera (JB): 0.657
Skew: 0.012 Prob(JB): 0.720
Kurtosis: 1.975 Cond. No. 42.4