LID SPN TRAP

1450344_a_at

Ptger3 prostaglandin E receptor 3 (subtype EP3)

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
4.039 0.058 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.728
Model: OLS Adj. R-squared: 0.685
Method: Least Squares F-statistic: 16.95
Date: Tue, 28 Jan 2025 Prob (F-statistic): 1.33e-05
Time: 17:03:14 Log-Likelihood: -98.131
No. Observations: 23 AIC: 204.3
Df Residuals: 19 BIC: 208.8
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 66.7197 44.382 1.503 0.149 -26.174 159.613
C(dose)[T.1] 119.3556 56.009 2.131 0.046 2.127 236.584
expression -5.2686 18.547 -0.284 0.779 -44.087 33.550
expression:C(dose)[T.1] -27.5167 23.275 -1.182 0.252 -76.231 21.198
Omnibus: 0.441 Durbin-Watson: 2.120
Prob(Omnibus): 0.802 Jarque-Bera (JB): 0.572
Skew: 0.214 Prob(JB): 0.751
Kurtosis: 2.357 Cond. No. 55.0

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.708
Model: OLS Adj. R-squared: 0.679
Method: Least Squares F-statistic: 24.25
Date: Tue, 28 Jan 2025 Prob (F-statistic): 4.50e-06
Time: 17:03:14 Log-Likelihood: -98.948
No. Observations: 23 AIC: 203.9
Df Residuals: 20 BIC: 207.3
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 108.2123 27.436 3.944 0.001 50.982 165.443
C(dose)[T.1] 53.8049 8.003 6.723 0.000 37.112 70.498
expression -22.7413 11.316 -2.010 0.058 -46.346 0.864
Omnibus: 0.958 Durbin-Watson: 1.954
Prob(Omnibus): 0.619 Jarque-Bera (JB): 0.790
Skew: 0.124 Prob(JB): 0.674
Kurtosis: 2.127 Cond. No. 19.6

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: Tue, 28 Jan 2025 Prob (F-statistic): 3.51e-06
Time: 17:03:14 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: 0.048
Model: OLS Adj. R-squared: 0.003
Method: Least Squares F-statistic: 1.061
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.315
Time: 17:03:14 Log-Likelihood: -112.54
No. Observations: 23 AIC: 229.1
Df Residuals: 21 BIC: 231.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 128.6684 48.046 2.678 0.014 28.751 228.586
expression -20.5284 19.931 -1.030 0.315 -61.978 20.921
Omnibus: 2.228 Durbin-Watson: 2.520
Prob(Omnibus): 0.328 Jarque-Bera (JB): 1.156
Skew: 0.124 Prob(JB): 0.561
Kurtosis: 1.930 Cond. No. 19.2

CP101

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

F-statistic p-value df difference
2.467 0.142 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.546
Model: OLS Adj. R-squared: 0.422
Method: Least Squares F-statistic: 4.412
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.0287
Time: 17:03:14 Log-Likelihood: -69.376
No. Observations: 15 AIC: 146.8
Df Residuals: 11 BIC: 149.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 33.6165 49.202 0.683 0.509 -74.677 141.910
C(dose)[T.1] 9.5316 73.641 0.129 0.899 -152.551 171.614
expression 10.8865 15.448 0.705 0.496 -23.115 44.888
expression:C(dose)[T.1] 5.5759 19.599 0.284 0.781 -37.562 48.714
Omnibus: 2.138 Durbin-Watson: 0.984
Prob(Omnibus): 0.343 Jarque-Bera (JB): 1.110
Skew: -0.288 Prob(JB): 0.574
Kurtosis: 1.798 Cond. No. 58.4

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.543
Model: OLS Adj. R-squared: 0.467
Method: Least Squares F-statistic: 7.123
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.00914
Time: 17:03:14 Log-Likelihood: -69.431
No. Observations: 15 AIC: 144.9
Df Residuals: 12 BIC: 147.0
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 22.8573 30.244 0.756 0.464 -43.039 88.754
C(dose)[T.1] 29.7161 18.955 1.568 0.143 -11.583 71.015
expression 14.3506 9.136 1.571 0.142 -5.555 34.256
Omnibus: 2.457 Durbin-Watson: 0.966
Prob(Omnibus): 0.293 Jarque-Bera (JB): 1.167
Skew: -0.280 Prob(JB): 0.558
Kurtosis: 1.753 Cond. No. 19.0

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: Tue, 28 Jan 2025 Prob (F-statistic): 0.00629
Time: 17:03:14 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: 0.449
Model: OLS Adj. R-squared: 0.407
Method: Least Squares F-statistic: 10.60
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.00626
Time: 17:03:14 Log-Likelihood: -70.828
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 2.8170 28.906 0.097 0.924 -59.631 65.265
expression 23.7214 7.286 3.256 0.006 7.981 39.462
Omnibus: 2.732 Durbin-Watson: 1.359
Prob(Omnibus): 0.255 Jarque-Bera (JB): 1.229
Skew: 0.290 Prob(JB): 0.541
Kurtosis: 1.724 Cond. No. 16.1