LID SPN TRAP

1460566_at

Mtap1a microtubule-associated protein 1 A

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
0.615 0.442 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.663
Model: OLS Adj. R-squared: 0.609
Method: Least Squares F-statistic: 12.43
Date: Tue, 04 Nov 2025 Prob (F-statistic): 9.93e-05
Time: 09:35:18 Log-Likelihood: -100.61
No. Observations: 23 AIC: 209.2
Df Residuals: 19 BIC: 213.8
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 184.4529 172.501 1.069 0.298 -176.595 545.501
C(dose)[T.1] -33.1120 199.911 -0.166 0.870 -451.531 385.307
expression -14.0961 18.658 -0.756 0.459 -53.147 24.955
expression:C(dose)[T.1] 9.0522 21.977 0.412 0.685 -36.945 55.050
Omnibus: 0.490 Durbin-Watson: 1.890
Prob(Omnibus): 0.783 Jarque-Bera (JB): 0.576
Skew: -0.065 Prob(JB): 0.750
Kurtosis: 2.236 Cond. No. 589.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.660
Model: OLS Adj. R-squared: 0.625
Method: Least Squares F-statistic: 19.37
Date: Tue, 04 Nov 2025 Prob (F-statistic): 2.09e-05
Time: 09:35:18 Log-Likelihood: -100.71
No. Observations: 23 AIC: 207.4
Df Residuals: 20 BIC: 210.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 124.1669 89.384 1.389 0.180 -62.285 310.619
C(dose)[T.1] 49.1211 10.174 4.828 0.000 27.899 70.343
expression -7.5715 9.652 -0.784 0.442 -27.706 12.563
Omnibus: 0.459 Durbin-Watson: 1.944
Prob(Omnibus): 0.795 Jarque-Bera (JB): 0.558
Skew: 0.041 Prob(JB): 0.757
Kurtosis: 2.242 Cond. No. 189.

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: 09:35:18 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: 0.263
Model: OLS Adj. R-squared: 0.228
Method: Least Squares F-statistic: 7.481
Date: Tue, 04 Nov 2025 Prob (F-statistic): 0.0124
Time: 09:35:18 Log-Likelihood: -109.60
No. Observations: 23 AIC: 223.2
Df Residuals: 21 BIC: 225.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 368.5924 105.796 3.484 0.002 148.578 588.607
expression -32.1922 11.770 -2.735 0.012 -56.669 -7.716
Omnibus: 1.635 Durbin-Watson: 2.446
Prob(Omnibus): 0.442 Jarque-Bera (JB): 1.033
Skew: 0.518 Prob(JB): 0.597
Kurtosis: 2.918 Cond. No. 155.

CP101

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

F-statistic p-value df difference
0.690 0.422 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.519
Model: OLS Adj. R-squared: 0.388
Method: Least Squares F-statistic: 3.955
Date: Tue, 04 Nov 2025 Prob (F-statistic): 0.0388
Time: 09:35:18 Log-Likelihood: -69.813
No. Observations: 15 AIC: 147.6
Df Residuals: 11 BIC: 150.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 468.0861 318.178 1.471 0.169 -232.219 1168.391
C(dose)[T.1] -324.6291 384.002 -0.845 0.416 -1169.811 520.553
expression -41.9331 33.280 -1.260 0.234 -115.182 31.316
expression:C(dose)[T.1] 39.0016 40.718 0.958 0.359 -50.617 128.621
Omnibus: 2.089 Durbin-Watson: 0.990
Prob(Omnibus): 0.352 Jarque-Bera (JB): 1.384
Skew: -0.724 Prob(JB): 0.501
Kurtosis: 2.660 Cond. No. 678.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.479
Model: OLS Adj. R-squared: 0.392
Method: Least Squares F-statistic: 5.511
Date: Tue, 04 Nov 2025 Prob (F-statistic): 0.0201
Time: 09:35:18 Log-Likelihood: -70.413
No. Observations: 15 AIC: 146.8
Df Residuals: 12 BIC: 149.0
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 219.1434 182.919 1.198 0.254 -179.403 617.690
C(dose)[T.1] 42.8200 17.121 2.501 0.028 5.516 80.124
expression -15.8786 19.109 -0.831 0.422 -57.513 25.756
Omnibus: 2.010 Durbin-Watson: 0.725
Prob(Omnibus): 0.366 Jarque-Bera (JB): 1.568
Skew: -0.689 Prob(JB): 0.457
Kurtosis: 2.218 Cond. No. 227.

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: 09:35:18 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: 0.207
Model: OLS Adj. R-squared: 0.146
Method: Least Squares F-statistic: 3.395
Date: Tue, 04 Nov 2025 Prob (F-statistic): 0.0883
Time: 09:35:18 Log-Likelihood: -73.560
No. Observations: 15 AIC: 151.1
Df Residuals: 13 BIC: 152.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 442.0451 189.288 2.335 0.036 33.113 850.977
expression -37.2975 20.242 -1.843 0.088 -81.028 6.433
Omnibus: 1.991 Durbin-Watson: 1.447
Prob(Omnibus): 0.370 Jarque-Bera (JB): 1.340
Skew: 0.507 Prob(JB): 0.512
Kurtosis: 1.943 Cond. No. 198.