LID SPN TRAP

1452890_at

Ttll5 tubulin tyrosine ligase-like family, member 5

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
0.039 0.845 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: 11.88
Date: Fri, 31 Oct 2025 Prob (F-statistic): 0.000131
Time: 18:59:38 Log-Likelihood: -100.96
No. Observations: 23 AIC: 209.9
Df Residuals: 19 BIC: 214.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 17.3517 110.488 0.157 0.877 -213.903 248.606
C(dose)[T.1] 148.6280 261.017 0.569 0.576 -397.686 694.942
expression 4.3429 12.999 0.334 0.742 -22.864 31.549
expression:C(dose)[T.1] -11.0026 29.913 -0.368 0.717 -73.611 51.606
Omnibus: 0.652 Durbin-Watson: 1.779
Prob(Omnibus): 0.722 Jarque-Bera (JB): 0.654
Skew: 0.085 Prob(JB): 0.721
Kurtosis: 2.192 Cond. No. 593.

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: 18.55
Date: Fri, 31 Oct 2025 Prob (F-statistic): 2.78e-05
Time: 18:59:38 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 34.9839 97.372 0.359 0.723 -168.130 238.098
C(dose)[T.1] 52.6854 9.360 5.629 0.000 33.160 72.211
expression 2.2653 11.451 0.198 0.845 -21.622 26.152
Omnibus: 0.217 Durbin-Watson: 1.812
Prob(Omnibus): 0.897 Jarque-Bera (JB): 0.418
Skew: 0.041 Prob(JB): 0.811
Kurtosis: 2.345 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: Fri, 31 Oct 2025 Prob (F-statistic): 3.51e-06
Time: 18:59:38 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: 0.095
Model: OLS Adj. R-squared: 0.052
Method: Least Squares F-statistic: 2.202
Date: Fri, 31 Oct 2025 Prob (F-statistic): 0.153
Time: 18:59:38 Log-Likelihood: -111.96
No. Observations: 23 AIC: 227.9
Df Residuals: 21 BIC: 230.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -135.4782 145.177 -0.933 0.361 -437.390 166.434
expression 24.9525 16.815 1.484 0.153 -10.016 59.921
Omnibus: 2.502 Durbin-Watson: 1.974
Prob(Omnibus): 0.286 Jarque-Bera (JB): 1.285
Skew: 0.202 Prob(JB): 0.526
Kurtosis: 1.915 Cond. No. 185.

CP101

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

F-statistic p-value df difference
3.902 0.072 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.605
Model: OLS Adj. R-squared: 0.498
Method: Least Squares F-statistic: 5.620
Date: Fri, 31 Oct 2025 Prob (F-statistic): 0.0139
Time: 18:59:38 Log-Likelihood: -68.330
No. Observations: 15 AIC: 144.7
Df Residuals: 11 BIC: 147.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -86.8522 141.851 -0.612 0.553 -399.063 225.359
C(dose)[T.1] -135.3255 237.562 -0.570 0.580 -658.195 387.544
expression 19.4083 17.799 1.090 0.299 -19.767 58.583
expression:C(dose)[T.1] 22.7331 29.618 0.768 0.459 -42.455 87.922
Omnibus: 2.665 Durbin-Watson: 1.558
Prob(Omnibus): 0.264 Jarque-Bera (JB): 1.689
Skew: -0.813 Prob(JB): 0.430
Kurtosis: 2.764 Cond. No. 349.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.584
Model: OLS Adj. R-squared: 0.515
Method: Least Squares F-statistic: 8.425
Date: Fri, 31 Oct 2025 Prob (F-statistic): 0.00518
Time: 18:59:38 Log-Likelihood: -68.721
No. Observations: 15 AIC: 143.4
Df Residuals: 12 BIC: 145.6
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -152.1133 111.583 -1.363 0.198 -395.232 91.005
C(dose)[T.1] 46.6981 13.731 3.401 0.005 16.781 76.616
expression 27.6181 13.981 1.975 0.072 -2.843 58.079
Omnibus: 2.443 Durbin-Watson: 1.616
Prob(Omnibus): 0.295 Jarque-Bera (JB): 1.494
Skew: -0.766 Prob(JB): 0.474
Kurtosis: 2.798 Cond. No. 133.

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: 18:59:38 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: 0.183
Model: OLS Adj. R-squared: 0.120
Method: Least Squares F-statistic: 2.914
Date: Fri, 31 Oct 2025 Prob (F-statistic): 0.112
Time: 18:59:38 Log-Likelihood: -73.783
No. Observations: 15 AIC: 151.6
Df Residuals: 13 BIC: 153.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -162.2309 150.182 -1.080 0.300 -486.679 162.217
expression 31.9974 18.744 1.707 0.112 -8.496 72.490
Omnibus: 2.054 Durbin-Watson: 1.991
Prob(Omnibus): 0.358 Jarque-Bera (JB): 0.960
Skew: 0.055 Prob(JB): 0.619
Kurtosis: 1.766 Cond. No. 133.