LID SPN TRAP

1460685_at

Timm17b translocase of inner mitochondrial membrane 17b

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
1.275 0.272 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.671
Model: OLS Adj. R-squared: 0.619
Method: Least Squares F-statistic: 12.89
Date: Fri, 31 Oct 2025 Prob (F-statistic): 7.94e-05
Time: 00:36:52 Log-Likelihood: -100.34
No. Observations: 23 AIC: 208.7
Df Residuals: 19 BIC: 213.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -51.3340 184.938 -0.278 0.784 -438.414 335.746
C(dose)[T.1] 13.3976 239.597 0.056 0.956 -488.085 514.880
expression 12.4488 21.802 0.571 0.575 -33.183 58.081
expression:C(dose)[T.1] 4.5639 28.144 0.162 0.873 -54.343 63.471
Omnibus: 0.828 Durbin-Watson: 1.763
Prob(Omnibus): 0.661 Jarque-Bera (JB): 0.719
Skew: -0.053 Prob(JB): 0.698
Kurtosis: 2.140 Cond. No. 644.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.670
Model: OLS Adj. R-squared: 0.637
Method: Least Squares F-statistic: 20.31
Date: Fri, 31 Oct 2025 Prob (F-statistic): 1.53e-05
Time: 00:36:52 Log-Likelihood: -100.35
No. Observations: 23 AIC: 206.7
Df Residuals: 20 BIC: 210.1
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -74.5529 114.162 -0.653 0.521 -312.691 163.585
C(dose)[T.1] 52.2246 8.560 6.101 0.000 34.369 70.080
expression 15.1875 13.448 1.129 0.272 -12.864 43.239
Omnibus: 0.729 Durbin-Watson: 1.714
Prob(Omnibus): 0.695 Jarque-Bera (JB): 0.685
Skew: -0.078 Prob(JB): 0.710
Kurtosis: 2.169 Cond. No. 233.

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: Fri, 31 Oct 2025 Prob (F-statistic): 3.51e-06
Time: 00:36:52 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: 1.248
Date: Fri, 31 Oct 2025 Prob (F-statistic): 0.277
Time: 00:36:52 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 -129.9570 187.856 -0.692 0.497 -520.625 260.711
expression 24.6295 22.051 1.117 0.277 -21.228 70.488
Omnibus: 2.094 Durbin-Watson: 2.244
Prob(Omnibus): 0.351 Jarque-Bera (JB): 1.207
Skew: 0.221 Prob(JB): 0.547
Kurtosis: 1.969 Cond. No. 231.

CP101

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

F-statistic p-value df difference
0.058 0.813 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.480
Model: OLS Adj. R-squared: 0.338
Method: Least Squares F-statistic: 3.385
Date: Fri, 31 Oct 2025 Prob (F-statistic): 0.0578
Time: 00:36:52 Log-Likelihood: -70.395
No. Observations: 15 AIC: 148.8
Df Residuals: 11 BIC: 151.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -156.8483 288.711 -0.543 0.598 -792.296 478.600
C(dose)[T.1] 320.1727 348.397 0.919 0.378 -446.644 1086.990
expression 28.3515 36.467 0.777 0.453 -51.912 108.615
expression:C(dose)[T.1] -34.2745 44.041 -0.778 0.453 -131.209 62.660
Omnibus: 2.306 Durbin-Watson: 0.903
Prob(Omnibus): 0.316 Jarque-Bera (JB): 1.543
Skew: -0.766 Prob(JB): 0.462
Kurtosis: 2.653 Cond. No. 507.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.451
Model: OLS Adj. R-squared: 0.360
Method: Least Squares F-statistic: 4.938
Date: Fri, 31 Oct 2025 Prob (F-statistic): 0.0273
Time: 00:36:52 Log-Likelihood: -70.797
No. Observations: 15 AIC: 147.6
Df Residuals: 12 BIC: 149.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 29.0418 159.483 0.182 0.859 -318.441 376.525
C(dose)[T.1] 49.3237 15.710 3.140 0.009 15.094 83.554
expression 4.8526 20.108 0.241 0.813 -38.960 48.665
Omnibus: 2.651 Durbin-Watson: 0.835
Prob(Omnibus): 0.266 Jarque-Bera (JB): 1.873
Skew: -0.837 Prob(JB): 0.392
Kurtosis: 2.562 Cond. No. 164.

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: Fri, 31 Oct 2025 Prob (F-statistic): 0.00629
Time: 00:36:52 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: 0.001
Model: OLS Adj. R-squared: -0.076
Method: Least Squares F-statistic: 0.01100
Date: Fri, 31 Oct 2025 Prob (F-statistic): 0.918
Time: 00:36:52 Log-Likelihood: -75.294
No. Observations: 15 AIC: 154.6
Df Residuals: 13 BIC: 156.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 72.0805 206.027 0.350 0.732 -373.014 517.175
expression 2.7336 26.059 0.105 0.918 -53.563 59.030
Omnibus: 0.694 Durbin-Watson: 1.643
Prob(Omnibus): 0.707 Jarque-Bera (JB): 0.616
Skew: 0.065 Prob(JB): 0.735
Kurtosis: 2.016 Cond. No. 163.