LID SPN TRAP

1434172_at

Cnr1 cannabinoid receptor 1 (brain)

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
0.005 0.943 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.651
Model: OLS Adj. R-squared: 0.596
Method: Least Squares F-statistic: 11.80
Date: Sun, 09 Nov 2025 Prob (F-statistic): 0.000137
Time: 20:12:31 Log-Likelihood: -101.01
No. Observations: 23 AIC: 210.0
Df Residuals: 19 BIC: 214.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 58.4671 52.441 1.115 0.279 -51.293 168.227
C(dose)[T.1] 21.2095 110.106 0.193 0.849 -209.246 251.665
expression -0.7563 9.247 -0.082 0.936 -20.111 18.598
expression:C(dose)[T.1] 5.4874 18.826 0.291 0.774 -33.917 44.892
Omnibus: 0.272 Durbin-Watson: 1.901
Prob(Omnibus): 0.873 Jarque-Bera (JB): 0.454
Skew: 0.065 Prob(JB): 0.797
Kurtosis: 2.324 Cond. No. 172.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.649
Model: OLS Adj. R-squared: 0.614
Method: Least Squares F-statistic: 18.50
Date: Sun, 09 Nov 2025 Prob (F-statistic): 2.83e-05
Time: 20:12:31 Log-Likelihood: -101.06
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 51.0122 44.721 1.141 0.267 -42.275 144.299
C(dose)[T.1] 53.1899 9.003 5.908 0.000 34.410 71.970
expression 0.5676 7.868 0.072 0.943 -15.846 16.981
Omnibus: 0.329 Durbin-Watson: 1.916
Prob(Omnibus): 0.848 Jarque-Bera (JB): 0.489
Skew: 0.054 Prob(JB): 0.783
Kurtosis: 2.294 Cond. No. 61.1

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: Sun, 09 Nov 2025 Prob (F-statistic): 3.51e-06
Time: 20:12:31 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: 0.037
Model: OLS Adj. R-squared: -0.009
Method: Least Squares F-statistic: 0.8032
Date: Sun, 09 Nov 2025 Prob (F-statistic): 0.380
Time: 20:12:31 Log-Likelihood: -112.67
No. Observations: 23 AIC: 229.3
Df Residuals: 21 BIC: 231.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 15.8029 71.666 0.221 0.828 -133.235 164.841
expression 11.1055 12.391 0.896 0.380 -14.664 36.875
Omnibus: 1.933 Durbin-Watson: 2.688
Prob(Omnibus): 0.380 Jarque-Bera (JB): 1.125
Skew: 0.180 Prob(JB): 0.570
Kurtosis: 1.978 Cond. No. 60.3

CP101

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

F-statistic p-value df difference
3.423 0.089 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.575
Model: OLS Adj. R-squared: 0.459
Method: Least Squares F-statistic: 4.956
Date: Sun, 09 Nov 2025 Prob (F-statistic): 0.0205
Time: 20:12:31 Log-Likelihood: -68.887
No. Observations: 15 AIC: 145.8
Df Residuals: 11 BIC: 148.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -148.3659 197.066 -0.753 0.467 -582.104 285.373
C(dose)[T.1] -35.9167 288.021 -0.125 0.903 -669.847 598.014
expression 29.1113 26.547 1.097 0.296 -29.317 87.540
expression:C(dose)[T.1] 12.0145 39.078 0.307 0.764 -73.995 98.024
Omnibus: 2.145 Durbin-Watson: 0.770
Prob(Omnibus): 0.342 Jarque-Bera (JB): 1.657
Skew: -0.735 Prob(JB): 0.437
Kurtosis: 2.298 Cond. No. 392.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.571
Model: OLS Adj. R-squared: 0.500
Method: Least Squares F-statistic: 7.990
Date: Sun, 09 Nov 2025 Prob (F-statistic): 0.00622
Time: 20:12:31 Log-Likelihood: -68.951
No. Observations: 15 AIC: 143.9
Df Residuals: 12 BIC: 146.0
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -189.4665 139.221 -1.361 0.199 -492.803 113.870
C(dose)[T.1] 52.5231 13.999 3.752 0.003 22.021 83.025
expression 34.6558 18.731 1.850 0.089 -6.156 75.468
Omnibus: 2.298 Durbin-Watson: 0.845
Prob(Omnibus): 0.317 Jarque-Bera (JB): 1.756
Skew: -0.726 Prob(JB): 0.416
Kurtosis: 2.163 Cond. No. 151.

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: Sun, 09 Nov 2025 Prob (F-statistic): 0.00629
Time: 20:12:31 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: 0.068
Model: OLS Adj. R-squared: -0.004
Method: Least Squares F-statistic: 0.9490
Date: Sun, 09 Nov 2025 Prob (F-statistic): 0.348
Time: 20:12:31 Log-Likelihood: -74.772
No. Observations: 15 AIC: 153.5
Df Residuals: 13 BIC: 155.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -95.0088 193.924 -0.490 0.632 -513.957 323.939
expression 25.6298 26.309 0.974 0.348 -31.208 82.467
Omnibus: 0.926 Durbin-Watson: 1.811
Prob(Omnibus): 0.629 Jarque-Bera (JB): 0.713
Skew: 0.151 Prob(JB): 0.700
Kurtosis: 1.975 Cond. No. 148.