LID SPN TRAP

1432431_s_at

Macrod2 MACRO domain containing 2

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
0.245 0.626 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.694
Model: OLS Adj. R-squared: 0.646
Method: Least Squares F-statistic: 14.39
Date: Thu, 03 Apr 2025 Prob (F-statistic): 3.94e-05
Time: 22:59:11 Log-Likelihood: -99.470
No. Observations: 23 AIC: 206.9
Df Residuals: 19 BIC: 211.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -86.7165 175.467 -0.494 0.627 -453.973 280.540
C(dose)[T.1] 434.7777 239.586 1.815 0.085 -66.682 936.237
expression 16.0998 20.035 0.804 0.432 -25.834 58.034
expression:C(dose)[T.1] -44.2539 27.669 -1.599 0.126 -102.165 13.657
Omnibus: 0.283 Durbin-Watson: 1.991
Prob(Omnibus): 0.868 Jarque-Bera (JB): 0.461
Skew: 0.054 Prob(JB): 0.794
Kurtosis: 2.315 Cond. No. 657.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.653
Model: OLS Adj. R-squared: 0.619
Method: Least Squares F-statistic: 18.84
Date: Thu, 03 Apr 2025 Prob (F-statistic): 2.51e-05
Time: 22:59:11 Log-Likelihood: -100.92
No. Observations: 23 AIC: 207.8
Df Residuals: 20 BIC: 211.3
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 116.3886 125.719 0.926 0.366 -145.858 378.635
C(dose)[T.1] 51.8426 9.224 5.620 0.000 32.601 71.084
expression -7.1037 14.346 -0.495 0.626 -37.029 22.822
Omnibus: 0.684 Durbin-Watson: 1.791
Prob(Omnibus): 0.710 Jarque-Bera (JB): 0.665
Skew: 0.073 Prob(JB): 0.717
Kurtosis: 2.180 Cond. No. 254.

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: 22:59:11 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: 0.106
Model: OLS Adj. R-squared: 0.063
Method: Least Squares F-statistic: 2.484
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.130
Time: 22:59:11 Log-Likelihood: -111.82
No. Observations: 23 AIC: 227.6
Df Residuals: 21 BIC: 229.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 369.4535 183.971 2.008 0.058 -13.136 752.043
expression -33.4855 21.247 -1.576 0.130 -77.672 10.701
Omnibus: 1.320 Durbin-Watson: 2.400
Prob(Omnibus): 0.517 Jarque-Bera (JB): 0.884
Skew: -0.048 Prob(JB): 0.643
Kurtosis: 2.044 Cond. No. 237.

CP101

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

F-statistic p-value df difference
0.010 0.922 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.449
Model: OLS Adj. R-squared: 0.299
Method: Least Squares F-statistic: 2.991
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0774
Time: 22:59:11 Log-Likelihood: -70.826
No. Observations: 15 AIC: 149.7
Df Residuals: 11 BIC: 152.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 33.1732 376.416 0.088 0.931 -795.313 861.659
C(dose)[T.1] 65.3930 578.259 0.113 0.912 -1207.347 1338.133
expression 4.2470 46.645 0.091 0.929 -98.418 106.912
expression:C(dose)[T.1] -1.9549 72.650 -0.027 0.979 -161.858 157.948
Omnibus: 2.629 Durbin-Watson: 0.812
Prob(Omnibus): 0.269 Jarque-Bera (JB): 1.811
Skew: -0.829 Prob(JB): 0.404
Kurtosis: 2.615 Cond. No. 730.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.449
Model: OLS Adj. R-squared: 0.357
Method: Least Squares F-statistic: 4.894
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0279
Time: 22:59:11 Log-Likelihood: -70.827
No. Observations: 15 AIC: 147.7
Df Residuals: 12 BIC: 149.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 39.6730 276.407 0.144 0.888 -562.566 641.912
C(dose)[T.1] 49.8404 16.988 2.934 0.013 12.828 86.853
expression 3.4412 34.240 0.101 0.922 -71.161 78.043
Omnibus: 2.641 Durbin-Watson: 0.813
Prob(Omnibus): 0.267 Jarque-Bera (JB): 1.823
Skew: -0.832 Prob(JB): 0.402
Kurtosis: 2.611 Cond. No. 286.

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: 22:59:11 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: 0.054
Model: OLS Adj. R-squared: -0.019
Method: Least Squares F-statistic: 0.7444
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.404
Time: 22:59:11 Log-Likelihood: -74.882
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 368.0720 318.201 1.157 0.268 -319.359 1055.503
expression -34.4474 39.926 -0.863 0.404 -120.702 51.808
Omnibus: 0.241 Durbin-Watson: 1.492
Prob(Omnibus): 0.887 Jarque-Bera (JB): 0.420
Skew: -0.033 Prob(JB): 0.811
Kurtosis: 2.183 Cond. No. 261.