LID SPN TRAP

1434528_at

Aard alanine and arginine rich domain containing protein

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
0.696 0.414 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.674
Model: OLS Adj. R-squared: 0.623
Method: Least Squares F-statistic: 13.11
Date: Wed, 05 Nov 2025 Prob (F-statistic): 7.15e-05
Time: 01:37:20 Log-Likelihood: -100.20
No. Observations: 23 AIC: 208.4
Df Residuals: 19 BIC: 213.0
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 89.9019 30.041 2.993 0.007 27.026 152.778
C(dose)[T.1] 18.7494 46.852 0.400 0.693 -79.313 116.812
expression -10.4208 8.594 -1.213 0.240 -28.408 7.567
expression:C(dose)[T.1] 10.1819 11.505 0.885 0.387 -13.899 34.263
Omnibus: 0.354 Durbin-Watson: 2.109
Prob(Omnibus): 0.838 Jarque-Bera (JB): 0.501
Skew: 0.007 Prob(JB): 0.778
Kurtosis: 2.277 Cond. No. 62.7

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.661
Model: OLS Adj. R-squared: 0.627
Method: Least Squares F-statistic: 19.49
Date: Wed, 05 Nov 2025 Prob (F-statistic): 2.01e-05
Time: 01:37:20 Log-Likelihood: -100.67
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 70.4434 20.358 3.460 0.002 27.978 112.909
C(dose)[T.1] 59.0394 11.003 5.366 0.000 36.087 81.991
expression -4.7398 5.683 -0.834 0.414 -16.594 7.114
Omnibus: 1.121 Durbin-Watson: 1.984
Prob(Omnibus): 0.571 Jarque-Bera (JB): 0.818
Skew: 0.020 Prob(JB): 0.664
Kurtosis: 2.077 Cond. No. 21.6

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: Wed, 05 Nov 2025 Prob (F-statistic): 3.51e-06
Time: 01:37:20 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: 0.173
Model: OLS Adj. R-squared: 0.133
Method: Least Squares F-statistic: 4.382
Date: Wed, 05 Nov 2025 Prob (F-statistic): 0.0486
Time: 01:37:20 Log-Likelihood: -110.93
No. Observations: 23 AIC: 225.9
Df Residuals: 21 BIC: 228.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 22.8806 27.935 0.819 0.422 -35.213 80.974
expression 14.2070 6.787 2.093 0.049 0.093 28.322
Omnibus: 0.067 Durbin-Watson: 1.991
Prob(Omnibus): 0.967 Jarque-Bera (JB): 0.053
Skew: -0.008 Prob(JB): 0.974
Kurtosis: 2.766 Cond. No. 18.5

CP101

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

F-statistic p-value df difference
1.133 0.308 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: 5.621
Date: Wed, 05 Nov 2025 Prob (F-statistic): 0.0139
Time: 01:37:20 Log-Likelihood: -68.329
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 122.3911 94.689 1.293 0.223 -86.019 330.801
C(dose)[T.1] -164.5519 123.816 -1.329 0.211 -437.069 107.966
expression -9.7085 16.629 -0.584 0.571 -46.309 26.892
expression:C(dose)[T.1] 37.9910 21.808 1.742 0.109 -10.008 85.990
Omnibus: 1.929 Durbin-Watson: 0.887
Prob(Omnibus): 0.381 Jarque-Bera (JB): 1.358
Skew: -0.704 Prob(JB): 0.507
Kurtosis: 2.564 Cond. No. 144.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.496
Model: OLS Adj. R-squared: 0.412
Method: Least Squares F-statistic: 5.912
Date: Wed, 05 Nov 2025 Prob (F-statistic): 0.0163
Time: 01:37:20 Log-Likelihood: -70.157
No. Observations: 15 AIC: 146.3
Df Residuals: 12 BIC: 148.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -2.6683 66.777 -0.040 0.969 -148.164 142.827
C(dose)[T.1] 49.7787 15.056 3.306 0.006 16.975 82.582
expression 12.3818 11.635 1.064 0.308 -12.968 37.731
Omnibus: 2.790 Durbin-Watson: 0.819
Prob(Omnibus): 0.248 Jarque-Bera (JB): 1.974
Skew: -0.737 Prob(JB): 0.373
Kurtosis: 2.007 Cond. No. 52.2

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: Wed, 05 Nov 2025 Prob (F-statistic): 0.00629
Time: 01:37:20 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: 0.037
Model: OLS Adj. R-squared: -0.037
Method: Least Squares F-statistic: 0.5059
Date: Wed, 05 Nov 2025 Prob (F-statistic): 0.489
Time: 01:37:20 Log-Likelihood: -75.014
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 31.7589 87.606 0.363 0.723 -157.501 221.019
expression 10.9839 15.442 0.711 0.489 -22.377 44.345
Omnibus: 0.107 Durbin-Watson: 1.708
Prob(Omnibus): 0.948 Jarque-Bera (JB): 0.294
Skew: -0.145 Prob(JB): 0.863
Kurtosis: 2.378 Cond. No. 51.4