LID SPN TRAP

1455034_at

Nr4a2 nuclear receptor subfamily 4, group A, member 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
0.001 0.970 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.649
Model: OLS Adj. R-squared: 0.594
Method: Least Squares F-statistic: 11.73
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.000141
Time: 22:53:04 Log-Likelihood: -101.05
No. Observations: 23 AIC: 210.1
Df Residuals: 19 BIC: 214.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 55.7087 88.455 0.630 0.536 -129.430 240.847
C(dose)[T.1] 12.2187 284.265 0.043 0.966 -582.755 607.193
expression -0.1810 10.648 -0.017 0.987 -22.467 22.105
expression:C(dose)[T.1] 4.2323 29.599 0.143 0.888 -57.719 66.183
Omnibus: 0.265 Durbin-Watson: 1.899
Prob(Omnibus): 0.876 Jarque-Bera (JB): 0.450
Skew: 0.049 Prob(JB): 0.799
Kurtosis: 2.322 Cond. No. 683.

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: Thu, 03 Apr 2025 Prob (F-statistic): 2.83e-05
Time: 22:53:04 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 51.1700 80.517 0.636 0.532 -116.785 219.125
C(dose)[T.1] 52.7900 16.909 3.122 0.005 17.519 88.061
expression 0.3666 9.688 0.038 0.970 -19.843 20.576
Omnibus: 0.298 Durbin-Watson: 1.892
Prob(Omnibus): 0.861 Jarque-Bera (JB): 0.471
Skew: 0.066 Prob(JB): 0.790
Kurtosis: 2.311 Cond. No. 171.

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: 22:53:04 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: 0.478
Model: OLS Adj. R-squared: 0.453
Method: Least Squares F-statistic: 19.23
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.000258
Time: 22:53:05 Log-Likelihood: -105.63
No. Observations: 23 AIC: 215.3
Df Residuals: 21 BIC: 217.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -156.3528 54.079 -2.891 0.009 -268.816 -43.890
expression 26.2282 5.980 4.386 0.000 13.791 38.665
Omnibus: 1.545 Durbin-Watson: 2.185
Prob(Omnibus): 0.462 Jarque-Bera (JB): 1.219
Skew: 0.363 Prob(JB): 0.544
Kurtosis: 2.136 Cond. No. 94.9

CP101

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

F-statistic p-value df difference
0.875 0.368 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.521
Model: OLS Adj. R-squared: 0.391
Method: Least Squares F-statistic: 3.995
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0378
Time: 22:53:05 Log-Likelihood: -69.773
No. Observations: 15 AIC: 147.5
Df Residuals: 11 BIC: 150.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -91.4577 127.372 -0.718 0.488 -371.801 188.885
C(dose)[T.1] 180.4512 154.294 1.170 0.267 -159.147 520.049
expression 27.0395 21.593 1.252 0.236 -20.485 74.564
expression:C(dose)[T.1] -22.7993 25.342 -0.900 0.388 -78.577 32.978
Omnibus: 1.913 Durbin-Watson: 1.226
Prob(Omnibus): 0.384 Jarque-Bera (JB): 1.057
Skew: -0.647 Prob(JB): 0.590
Kurtosis: 2.864 Cond. No. 191.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.486
Model: OLS Adj. R-squared: 0.401
Method: Least Squares F-statistic: 5.678
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0184
Time: 22:53:05 Log-Likelihood: -70.305
No. Observations: 15 AIC: 146.6
Df Residuals: 12 BIC: 148.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 5.8009 66.817 0.087 0.932 -139.781 151.383
C(dose)[T.1] 42.4791 16.807 2.527 0.027 5.859 79.099
expression 10.4879 11.213 0.935 0.368 -13.943 34.919
Omnibus: 1.483 Durbin-Watson: 1.204
Prob(Omnibus): 0.476 Jarque-Bera (JB): 0.645
Skew: -0.508 Prob(JB): 0.724
Kurtosis: 2.985 Cond. No. 57.0

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: 22:53:05 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: 0.213
Model: OLS Adj. R-squared: 0.152
Method: Least Squares F-statistic: 3.513
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0835
Time: 22:53:05 Log-Likelihood: -73.506
No. Observations: 15 AIC: 151.0
Df Residuals: 13 BIC: 152.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -46.8400 75.507 -0.620 0.546 -209.963 116.283
expression 22.5979 12.057 1.874 0.084 -3.450 48.646
Omnibus: 4.401 Durbin-Watson: 1.771
Prob(Omnibus): 0.111 Jarque-Bera (JB): 2.072
Skew: 0.858 Prob(JB): 0.355
Kurtosis: 3.611 Cond. No. 53.8