LID SPN TRAP

1450749_a_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
0.153 0.700 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.652
Model: OLS Adj. R-squared: 0.597
Method: Least Squares F-statistic: 11.85
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000133
Time: 06:19:41 Log-Likelihood: -100.97
No. Observations: 23 AIC: 209.9
Df Residuals: 19 BIC: 214.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 25.5074 76.805 0.332 0.743 -135.248 186.263
C(dose)[T.1] 61.8415 231.871 0.267 0.793 -423.469 547.152
expression 3.4555 9.217 0.375 0.712 -15.836 22.747
expression:C(dose)[T.1] -1.4495 23.595 -0.061 0.952 -50.833 47.934
Omnibus: 0.217 Durbin-Watson: 1.910
Prob(Omnibus): 0.897 Jarque-Bera (JB): 0.413
Skew: 0.120 Prob(JB): 0.814
Kurtosis: 2.390 Cond. No. 573.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.652
Model: OLS Adj. R-squared: 0.617
Method: Least Squares F-statistic: 18.71
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.63e-05
Time: 06:19:41 Log-Likelihood: -100.98
No. Observations: 23 AIC: 208.0
Df Residuals: 20 BIC: 211.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 27.3446 68.960 0.397 0.696 -116.503 171.192
C(dose)[T.1] 47.6369 16.994 2.803 0.011 12.188 83.086
expression 3.2343 8.271 0.391 0.700 -14.018 20.486
Omnibus: 0.228 Durbin-Watson: 1.922
Prob(Omnibus): 0.892 Jarque-Bera (JB): 0.421
Skew: 0.120 Prob(JB): 0.810
Kurtosis: 2.382 Cond. No. 151.

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, 21 Nov 2024 Prob (F-statistic): 3.51e-06
Time: 06:19:41 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: 0.515
Model: OLS Adj. R-squared: 0.492
Method: Least Squares F-statistic: 22.29
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000116
Time: 06:19:41 Log-Likelihood: -104.79
No. Observations: 23 AIC: 213.6
Df Residuals: 21 BIC: 215.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -131.8019 45.084 -2.923 0.008 -225.560 -38.044
expression 23.1197 4.897 4.721 0.000 12.936 33.304
Omnibus: 1.275 Durbin-Watson: 2.116
Prob(Omnibus): 0.529 Jarque-Bera (JB): 1.080
Skew: 0.336 Prob(JB): 0.583
Kurtosis: 2.179 Cond. No. 83.5

CP101

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

F-statistic p-value df difference
2.248 0.160 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.537
Model: OLS Adj. R-squared: 0.410
Method: Least Squares F-statistic: 4.248
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0319
Time: 06:19:41 Log-Likelihood: -69.530
No. Observations: 15 AIC: 147.1
Df Residuals: 11 BIC: 149.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -61.0971 120.020 -0.509 0.621 -325.260 203.066
C(dose)[T.1] 66.2649 166.891 0.397 0.699 -301.061 433.590
expression 20.4028 18.972 1.075 0.305 -21.355 62.161
expression:C(dose)[T.1] -3.8390 25.585 -0.150 0.883 -60.152 52.474
Omnibus: 0.979 Durbin-Watson: 1.373
Prob(Omnibus): 0.613 Jarque-Bera (JB): 0.339
Skew: -0.368 Prob(JB): 0.844
Kurtosis: 2.968 Cond. No. 202.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.536
Model: OLS Adj. R-squared: 0.458
Method: Least Squares F-statistic: 6.924
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0100
Time: 06:19:41 Log-Likelihood: -69.545
No. Observations: 15 AIC: 145.1
Df Residuals: 12 BIC: 147.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -47.7995 77.570 -0.616 0.549 -216.810 121.211
C(dose)[T.1] 41.3395 15.366 2.690 0.020 7.861 74.818
expression 18.2919 12.199 1.499 0.160 -8.288 44.872
Omnibus: 0.937 Durbin-Watson: 1.351
Prob(Omnibus): 0.626 Jarque-Bera (JB): 0.255
Skew: -0.319 Prob(JB): 0.880
Kurtosis: 3.027 Cond. No. 72.6

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, 21 Nov 2024 Prob (F-statistic): 0.00629
Time: 06:19:41 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: 0.256
Model: OLS Adj. R-squared: 0.198
Method: Least Squares F-statistic: 4.467
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0545
Time: 06:19:41 Log-Likelihood: -73.085
No. Observations: 15 AIC: 150.2
Df Residuals: 13 BIC: 151.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -98.8233 91.500 -1.080 0.300 -296.496 98.850
expression 29.4846 13.951 2.113 0.054 -0.655 59.624
Omnibus: 1.985 Durbin-Watson: 1.768
Prob(Omnibus): 0.371 Jarque-Bera (JB): 0.983
Skew: 0.627 Prob(JB): 0.612
Kurtosis: 2.980 Cond. No. 70.0