LID SPN TRAP

1452008_at

Ttc39b tetratricopeptide repeat domain 39B

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
0.070 0.794 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.666
Model: OLS Adj. R-squared: 0.613
Method: Least Squares F-statistic: 12.63
Date: Thu, 03 Apr 2025 Prob (F-statistic): 9.02e-05
Time: 22:45:17 Log-Likelihood: -100.49
No. Observations: 23 AIC: 209.0
Df Residuals: 19 BIC: 213.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -65.0167 150.216 -0.433 0.670 -379.422 249.389
C(dose)[T.1] 276.1849 234.183 1.179 0.253 -213.966 766.336
expression 15.2280 19.171 0.794 0.437 -24.897 55.352
expression:C(dose)[T.1] -29.1892 30.866 -0.946 0.356 -93.792 35.413
Omnibus: 0.227 Durbin-Watson: 1.900
Prob(Omnibus): 0.893 Jarque-Bera (JB): 0.422
Skew: 0.116 Prob(JB): 0.810
Kurtosis: 2.378 Cond. No. 512.

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: 18.59
Date: Thu, 03 Apr 2025 Prob (F-statistic): 2.74e-05
Time: 22:45:18 Log-Likelihood: -101.02
No. Observations: 23 AIC: 208.0
Df Residuals: 20 BIC: 211.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 23.1423 117.478 0.197 0.846 -221.912 268.197
C(dose)[T.1] 54.9527 10.671 5.150 0.000 32.694 77.212
expression 3.9679 14.985 0.265 0.794 -27.290 35.226
Omnibus: 0.235 Durbin-Watson: 1.844
Prob(Omnibus): 0.889 Jarque-Bera (JB): 0.430
Skew: 0.004 Prob(JB): 0.807
Kurtosis: 2.330 Cond. No. 209.

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: 22:45:18 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: 0.187
Model: OLS Adj. R-squared: 0.148
Method: Least Squares F-statistic: 4.816
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0396
Time: 22:45:18 Log-Likelihood: -110.73
No. Observations: 23 AIC: 225.5
Df Residuals: 21 BIC: 227.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 386.2872 139.847 2.762 0.012 95.459 677.116
expression -40.1552 18.298 -2.195 0.040 -78.207 -2.103
Omnibus: 0.222 Durbin-Watson: 2.495
Prob(Omnibus): 0.895 Jarque-Bera (JB): 0.417
Skew: 0.117 Prob(JB): 0.812
Kurtosis: 2.383 Cond. No. 167.

CP101

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

F-statistic p-value df difference
0.012 0.915 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.452
Model: OLS Adj. R-squared: 0.303
Method: Least Squares F-statistic: 3.028
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0752
Time: 22:45:18 Log-Likelihood: -70.785
No. Observations: 15 AIC: 149.6
Df Residuals: 11 BIC: 152.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 10.0260 252.792 0.040 0.969 -546.364 566.416
C(dose)[T.1] 154.5790 426.122 0.363 0.724 -783.310 1092.468
expression 7.2355 31.828 0.227 0.824 -62.818 77.289
expression:C(dose)[T.1] -13.5339 55.125 -0.246 0.811 -134.862 107.795
Omnibus: 2.360 Durbin-Watson: 0.800
Prob(Omnibus): 0.307 Jarque-Bera (JB): 1.641
Skew: -0.784 Prob(JB): 0.440
Kurtosis: 2.588 Cond. No. 509.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.449
Model: OLS Adj. R-squared: 0.358
Method: Least Squares F-statistic: 4.896
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0279
Time: 22:45:18 Log-Likelihood: -70.826
No. Observations: 15 AIC: 147.7
Df Residuals: 12 BIC: 149.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 45.8208 198.262 0.231 0.821 -386.155 477.796
C(dose)[T.1] 50.0563 17.594 2.845 0.015 11.723 88.390
expression 2.7236 24.949 0.109 0.915 -51.635 57.082
Omnibus: 2.781 Durbin-Watson: 0.789
Prob(Omnibus): 0.249 Jarque-Bera (JB): 1.923
Skew: -0.855 Prob(JB): 0.382
Kurtosis: 2.614 Cond. No. 200.

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: 22:45:18 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: 0.078
Model: OLS Adj. R-squared: 0.007
Method: Least Squares F-statistic: 1.097
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.314
Time: 22:45:18 Log-Likelihood: -74.692
No. Observations: 15 AIC: 153.4
Df Residuals: 13 BIC: 154.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 319.2888 215.591 1.481 0.162 -146.468 785.045
expression -29.0560 27.736 -1.048 0.314 -88.975 30.863
Omnibus: 0.018 Durbin-Watson: 1.551
Prob(Omnibus): 0.991 Jarque-Bera (JB): 0.237
Skew: -0.001 Prob(JB): 0.888
Kurtosis: 2.384 Cond. No. 175.