LID SPN TRAP

1435674_at

Rhobtb2 Rho-related BTB domain containing 2

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
0.763 0.393 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.680
Model: OLS Adj. R-squared: 0.629
Method: Least Squares F-statistic: 13.43
Date: Tue, 04 Nov 2025 Prob (F-statistic): 6.14e-05
Time: 09:37:16 Log-Likelihood: -100.02
No. Observations: 23 AIC: 208.0
Df Residuals: 19 BIC: 212.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 318.9237 197.368 1.616 0.123 -94.172 732.020
C(dose)[T.1] -200.0292 243.720 -0.821 0.422 -710.142 310.083
expression -29.6597 22.104 -1.342 0.195 -75.924 16.604
expression:C(dose)[T.1] 28.3277 27.744 1.021 0.320 -29.741 86.396
Omnibus: 0.515 Durbin-Watson: 1.579
Prob(Omnibus): 0.773 Jarque-Bera (JB): 0.607
Skew: -0.150 Prob(JB): 0.738
Kurtosis: 2.263 Cond. No. 692.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.662
Model: OLS Adj. R-squared: 0.628
Method: Least Squares F-statistic: 19.58
Date: Tue, 04 Nov 2025 Prob (F-statistic): 1.95e-05
Time: 09:37:16 Log-Likelihood: -100.63
No. Observations: 23 AIC: 207.3
Df Residuals: 20 BIC: 210.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 158.4405 119.501 1.326 0.200 -90.834 407.715
C(dose)[T.1] 48.6048 10.171 4.779 0.000 27.389 69.821
expression -11.6786 13.373 -0.873 0.393 -39.574 16.216
Omnibus: 0.332 Durbin-Watson: 1.889
Prob(Omnibus): 0.847 Jarque-Bera (JB): 0.495
Skew: -0.105 Prob(JB): 0.781
Kurtosis: 2.313 Cond. No. 247.

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: Tue, 04 Nov 2025 Prob (F-statistic): 3.51e-06
Time: 09:37:16 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: 0.276
Model: OLS Adj. R-squared: 0.241
Method: Least Squares F-statistic: 8.003
Date: Tue, 04 Nov 2025 Prob (F-statistic): 0.0101
Time: 09:37:16 Log-Likelihood: -109.39
No. Observations: 23 AIC: 222.8
Df Residuals: 21 BIC: 225.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 478.9597 141.258 3.391 0.003 185.198 772.721
expression -45.7255 16.163 -2.829 0.010 -79.338 -12.113
Omnibus: 2.094 Durbin-Watson: 2.111
Prob(Omnibus): 0.351 Jarque-Bera (JB): 1.224
Skew: 0.236 Prob(JB): 0.542
Kurtosis: 1.974 Cond. No. 204.

CP101

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

F-statistic p-value df difference
1.228 0.289 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.502
Model: OLS Adj. R-squared: 0.366
Method: Least Squares F-statistic: 3.691
Date: Tue, 04 Nov 2025 Prob (F-statistic): 0.0465
Time: 09:37:16 Log-Likelihood: -70.077
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 -232.6967 312.273 -0.745 0.472 -920.005 454.612
C(dose)[T.1] 148.4642 514.464 0.289 0.778 -983.864 1280.793
expression 31.6730 32.933 0.962 0.357 -40.812 104.158
expression:C(dose)[T.1] -10.4323 54.338 -0.192 0.851 -130.030 109.166
Omnibus: 3.330 Durbin-Watson: 1.024
Prob(Omnibus): 0.189 Jarque-Bera (JB): 1.439
Skew: -0.724 Prob(JB): 0.487
Kurtosis: 3.453 Cond. No. 793.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.500
Model: OLS Adj. R-squared: 0.417
Method: Least Squares F-statistic: 5.999
Date: Tue, 04 Nov 2025 Prob (F-statistic): 0.0156
Time: 09:37:16 Log-Likelihood: -70.102
No. Observations: 15 AIC: 146.2
Df Residuals: 12 BIC: 148.3
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -196.3855 238.301 -0.824 0.426 -715.599 322.828
C(dose)[T.1] 49.7393 14.999 3.316 0.006 17.059 82.420
expression 27.8410 25.122 1.108 0.289 -26.895 82.577
Omnibus: 4.208 Durbin-Watson: 0.994
Prob(Omnibus): 0.122 Jarque-Bera (JB): 1.887
Skew: -0.802 Prob(JB): 0.389
Kurtosis: 3.669 Cond. No. 306.

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: Tue, 04 Nov 2025 Prob (F-statistic): 0.00629
Time: 09:37:16 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: 0.042
Model: OLS Adj. R-squared: -0.032
Method: Least Squares F-statistic: 0.5658
Date: Tue, 04 Nov 2025 Prob (F-statistic): 0.465
Time: 09:37:16 Log-Likelihood: -74.981
No. Observations: 15 AIC: 154.0
Df Residuals: 13 BIC: 155.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -144.1038 316.254 -0.456 0.656 -827.328 539.121
expression 25.1201 33.395 0.752 0.465 -47.026 97.266
Omnibus: 2.490 Durbin-Watson: 1.728
Prob(Omnibus): 0.288 Jarque-Bera (JB): 1.097
Skew: 0.186 Prob(JB): 0.578
Kurtosis: 1.728 Cond. No. 304.