LID SPN TRAP

1460702_at

Triap1 TP53 regulated inhibitor of apoptosis 1

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
0.466 0.503 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.657
Model: OLS Adj. R-squared: 0.603
Method: Least Squares F-statistic: 12.14
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.000115
Time: 22:53:52 Log-Likelihood: -100.80
No. Observations: 23 AIC: 209.6
Df Residuals: 19 BIC: 214.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -41.4314 264.510 -0.157 0.877 -595.057 512.194
C(dose)[T.1] 35.1028 333.415 0.105 0.917 -662.743 732.948
expression 10.6770 29.521 0.362 0.722 -51.112 72.465
expression:C(dose)[T.1] 2.0698 37.245 0.056 0.956 -75.886 80.025
Omnibus: 0.776 Durbin-Watson: 1.819
Prob(Omnibus): 0.678 Jarque-Bera (JB): 0.694
Skew: -0.010 Prob(JB): 0.707
Kurtosis: 2.149 Cond. No. 934.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.657
Model: OLS Adj. R-squared: 0.623
Method: Least Squares F-statistic: 19.16
Date: Thu, 03 Apr 2025 Prob (F-statistic): 2.25e-05
Time: 22:53:52 Log-Likelihood: -100.80
No. Observations: 23 AIC: 207.6
Df Residuals: 20 BIC: 211.0
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -53.0793 157.279 -0.337 0.739 -381.159 275.000
C(dose)[T.1] 53.6250 8.680 6.178 0.000 35.519 71.730
expression 11.9773 17.545 0.683 0.503 -24.622 48.577
Omnibus: 0.786 Durbin-Watson: 1.817
Prob(Omnibus): 0.675 Jarque-Bera (JB): 0.698
Skew: -0.013 Prob(JB): 0.705
Kurtosis: 2.147 Cond. No. 330.

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: 22:53:52 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: 0.003
Model: OLS Adj. R-squared: -0.045
Method: Least Squares F-statistic: 0.05294
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.820
Time: 22:53:52 Log-Likelihood: -113.08
No. Observations: 23 AIC: 230.2
Df Residuals: 21 BIC: 232.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 19.6813 261.031 0.075 0.941 -523.163 562.526
expression 6.7109 29.167 0.230 0.820 -53.945 67.367
Omnibus: 3.560 Durbin-Watson: 2.473
Prob(Omnibus): 0.169 Jarque-Bera (JB): 1.600
Skew: 0.277 Prob(JB): 0.449
Kurtosis: 1.833 Cond. No. 328.

CP101

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

F-statistic p-value df difference
0.482 0.501 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.576
Model: OLS Adj. R-squared: 0.460
Method: Least Squares F-statistic: 4.977
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0202
Time: 22:53:52 Log-Likelihood: -68.868
No. Observations: 15 AIC: 145.7
Df Residuals: 11 BIC: 148.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -884.4889 565.584 -1.564 0.146 -2129.332 360.354
C(dose)[T.1] 1404.0312 820.047 1.712 0.115 -400.881 3208.943
expression 103.5360 61.506 1.683 0.120 -31.837 238.909
expression:C(dose)[T.1] -146.9316 88.724 -1.656 0.126 -342.211 48.348
Omnibus: 7.432 Durbin-Watson: 1.294
Prob(Omnibus): 0.024 Jarque-Bera (JB): 4.045
Skew: -1.106 Prob(JB): 0.132
Kurtosis: 4.256 Cond. No. 1.40e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.470
Model: OLS Adj. R-squared: 0.382
Method: Least Squares F-statistic: 5.322
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0222
Time: 22:53:52 Log-Likelihood: -70.538
No. Observations: 15 AIC: 147.1
Df Residuals: 12 BIC: 149.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -235.2983 436.292 -0.539 0.600 -1185.896 715.299
C(dose)[T.1] 46.2112 16.021 2.884 0.014 11.304 81.118
expression 32.9263 47.438 0.694 0.501 -70.432 136.284
Omnibus: 1.591 Durbin-Watson: 1.001
Prob(Omnibus): 0.451 Jarque-Bera (JB): 1.235
Skew: -0.637 Prob(JB): 0.539
Kurtosis: 2.405 Cond. No. 531.

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: 22:53:52 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: 0.103
Model: OLS Adj. R-squared: 0.034
Method: Least Squares F-statistic: 1.487
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.244
Time: 22:53:52 Log-Likelihood: -74.488
No. Observations: 15 AIC: 153.0
Df Residuals: 13 BIC: 154.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -550.1546 528.115 -1.042 0.317 -1691.077 590.768
expression 69.6593 57.131 1.219 0.244 -53.764 193.083
Omnibus: 2.417 Durbin-Watson: 1.941
Prob(Omnibus): 0.299 Jarque-Bera (JB): 1.694
Skew: 0.650 Prob(JB): 0.429
Kurtosis: 1.990 Cond. No. 513.