LID SPN TRAP

1449125_at

Tnfaip8l1 tumor necrosis factor, alpha-induced protein 8-like 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
0.044 0.836 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: 12.13
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.000115
Time: 22:55:52 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 12.1411 84.536 0.144 0.887 -164.796 189.078
C(dose)[T.1] 160.0359 169.907 0.942 0.358 -195.583 515.655
expression 6.0934 12.212 0.499 0.624 -19.468 31.654
expression:C(dose)[T.1] -15.1547 23.985 -0.632 0.535 -65.357 35.047
Omnibus: 0.130 Durbin-Watson: 1.710
Prob(Omnibus): 0.937 Jarque-Bera (JB): 0.351
Skew: -0.031 Prob(JB): 0.839
Kurtosis: 2.398 Cond. No. 324.

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: 18.56
Date: Thu, 03 Apr 2025 Prob (F-statistic): 2.77e-05
Time: 22:55:52 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 39.2647 71.723 0.547 0.590 -110.347 188.877
C(dose)[T.1] 52.8417 9.075 5.823 0.000 33.911 71.772
expression 2.1646 10.352 0.209 0.836 -19.429 23.758
Omnibus: 0.478 Durbin-Watson: 1.806
Prob(Omnibus): 0.787 Jarque-Bera (JB): 0.569
Skew: 0.061 Prob(JB): 0.752
Kurtosis: 2.239 Cond. No. 118.

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: 22:55:52 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: 0.056
Model: OLS Adj. R-squared: 0.011
Method: Least Squares F-statistic: 1.250
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.276
Time: 22:55:52 Log-Likelihood: -112.44
No. Observations: 23 AIC: 228.9
Df Residuals: 21 BIC: 231.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -45.8329 112.500 -0.407 0.688 -279.790 188.124
expression 17.9019 16.010 1.118 0.276 -15.393 51.196
Omnibus: 2.213 Durbin-Watson: 2.376
Prob(Omnibus): 0.331 Jarque-Bera (JB): 1.405
Skew: 0.344 Prob(JB): 0.495
Kurtosis: 2.004 Cond. No. 115.

CP101

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

F-statistic p-value df difference
1.204 0.294 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.500
Model: OLS Adj. R-squared: 0.363
Method: Least Squares F-statistic: 3.662
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0474
Time: 22:55:52 Log-Likelihood: -70.106
No. Observations: 15 AIC: 148.2
Df Residuals: 11 BIC: 151.0
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -171.9077 294.705 -0.583 0.571 -820.548 476.733
C(dose)[T.1] 92.9768 412.726 0.225 0.826 -815.428 1001.382
expression 34.1658 42.038 0.813 0.434 -58.359 126.691
expression:C(dose)[T.1] -6.8914 58.216 -0.118 0.908 -135.024 121.241
Omnibus: 1.847 Durbin-Watson: 1.033
Prob(Omnibus): 0.397 Jarque-Bera (JB): 1.191
Skew: -0.425 Prob(JB): 0.551
Kurtosis: 1.911 Cond. No. 513.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.499
Model: OLS Adj. R-squared: 0.416
Method: Least Squares F-statistic: 5.977
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0158
Time: 22:55:53 Log-Likelihood: -70.116
No. Observations: 15 AIC: 146.2
Df Residuals: 12 BIC: 148.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -146.7354 195.476 -0.751 0.467 -572.642 279.171
C(dose)[T.1] 44.1585 15.691 2.814 0.016 9.970 78.347
expression 30.5724 27.861 1.097 0.294 -30.131 91.276
Omnibus: 1.964 Durbin-Watson: 1.000
Prob(Omnibus): 0.375 Jarque-Bera (JB): 1.210
Skew: -0.415 Prob(JB): 0.546
Kurtosis: 1.883 Cond. No. 190.

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: 22:55:53 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: 0.168
Model: OLS Adj. R-squared: 0.104
Method: Least Squares F-statistic: 2.633
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.129
Time: 22:55:53 Log-Likelihood: -73.917
No. Observations: 15 AIC: 151.8
Df Residuals: 13 BIC: 153.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -285.9003 234.099 -1.221 0.244 -791.641 219.841
expression 53.5127 32.978 1.623 0.129 -17.733 124.758
Omnibus: 2.082 Durbin-Watson: 1.692
Prob(Omnibus): 0.353 Jarque-Bera (JB): 1.239
Skew: 0.415 Prob(JB): 0.538
Kurtosis: 1.862 Cond. No. 183.