LID SPN TRAP

1460568_at

Trim46 tripartite motif-containing 46

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
0.000 0.988 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.652
Model: OLS Adj. R-squared: 0.597
Method: Least Squares F-statistic: 11.85
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.000133
Time: 22:56:41 Log-Likelihood: -100.98
No. Observations: 23 AIC: 210.0
Df Residuals: 19 BIC: 214.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 21.0822 139.571 0.151 0.882 -271.044 313.208
C(dose)[T.1] 139.8665 228.680 0.612 0.548 -338.766 618.499
expression 4.2554 17.912 0.238 0.815 -33.234 41.745
expression:C(dose)[T.1] -11.3815 30.073 -0.378 0.709 -74.325 51.562
Omnibus: 0.129 Durbin-Watson: 1.924
Prob(Omnibus): 0.938 Jarque-Bera (JB): 0.339
Skew: 0.094 Prob(JB): 0.844
Kurtosis: 2.436 Cond. No. 484.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.649
Model: OLS Adj. R-squared: 0.614
Method: Least Squares F-statistic: 18.49
Date: Thu, 03 Apr 2025 Prob (F-statistic): 2.83e-05
Time: 22:56:41 Log-Likelihood: -101.06
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 52.5128 109.746 0.478 0.637 -176.413 281.439
C(dose)[T.1] 53.4004 9.676 5.519 0.000 33.216 73.585
expression 0.2178 14.076 0.015 0.988 -29.145 29.581
Omnibus: 0.321 Durbin-Watson: 1.887
Prob(Omnibus): 0.852 Jarque-Bera (JB): 0.485
Skew: 0.058 Prob(JB): 0.785
Kurtosis: 2.298 Cond. No. 195.

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: 22:56:41 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: 0.115
Model: OLS Adj. R-squared: 0.072
Method: Least Squares F-statistic: 2.719
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.114
Time: 22:56:41 Log-Likelihood: -111.70
No. Observations: 23 AIC: 227.4
Df Residuals: 21 BIC: 229.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 329.0378 151.347 2.174 0.041 14.294 643.782
expression -32.6097 19.775 -1.649 0.114 -73.735 8.516
Omnibus: 0.463 Durbin-Watson: 2.436
Prob(Omnibus): 0.793 Jarque-Bera (JB): 0.585
Skew: 0.239 Prob(JB): 0.746
Kurtosis: 2.381 Cond. No. 173.

CP101

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

F-statistic p-value df difference
0.817 0.384 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.509
Model: OLS Adj. R-squared: 0.376
Method: Least Squares F-statistic: 3.808
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0429
Time: 22:56:41 Log-Likelihood: -69.958
No. Observations: 15 AIC: 147.9
Df Residuals: 11 BIC: 150.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -223.5473 249.806 -0.895 0.390 -773.367 326.273
C(dose)[T.1] 351.4071 406.475 0.865 0.406 -543.239 1246.053
expression 37.7420 32.369 1.166 0.268 -33.501 108.985
expression:C(dose)[T.1] -39.1576 51.751 -0.757 0.465 -153.061 74.746
Omnibus: 2.305 Durbin-Watson: 0.916
Prob(Omnibus): 0.316 Jarque-Bera (JB): 1.458
Skew: -0.752 Prob(JB): 0.482
Kurtosis: 2.739 Cond. No. 531.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.484
Model: OLS Adj. R-squared: 0.398
Method: Least Squares F-statistic: 5.626
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0189
Time: 22:56:41 Log-Likelihood: -70.339
No. Observations: 15 AIC: 146.7
Df Residuals: 12 BIC: 148.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -105.4460 191.535 -0.551 0.592 -522.765 311.873
C(dose)[T.1] 44.1025 16.238 2.716 0.019 8.722 79.483
expression 22.4233 24.802 0.904 0.384 -31.615 76.462
Omnibus: 1.198 Durbin-Watson: 0.832
Prob(Omnibus): 0.549 Jarque-Bera (JB): 1.008
Skew: -0.536 Prob(JB): 0.604
Kurtosis: 2.321 Cond. No. 201.

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: 22:56:41 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: 0.167
Model: OLS Adj. R-squared: 0.103
Method: Least Squares F-statistic: 2.601
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.131
Time: 22:56:41 Log-Likelihood: -73.932
No. Observations: 15 AIC: 151.9
Df Residuals: 13 BIC: 153.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -264.9481 222.573 -1.190 0.255 -745.789 215.893
expression 45.7957 28.398 1.613 0.131 -15.555 107.146
Omnibus: 2.118 Durbin-Watson: 1.297
Prob(Omnibus): 0.347 Jarque-Bera (JB): 1.455
Skew: 0.560 Prob(JB): 0.483
Kurtosis: 1.964 Cond. No. 191.