LID SPN TRAP

1459274_at

Gpr135 G protein-coupled receptor 135

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
0.050 0.826 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.662
Model: OLS Adj. R-squared: 0.608
Method: Least Squares F-statistic: 12.38
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.000102
Time: 22:53:55 Log-Likelihood: -100.65
No. Observations: 23 AIC: 209.3
Df Residuals: 19 BIC: 213.8
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -17.2763 97.251 -0.178 0.861 -220.825 186.272
C(dose)[T.1] 164.6799 140.279 1.174 0.255 -128.928 458.288
expression 11.5870 15.732 0.737 0.470 -21.341 44.515
expression:C(dose)[T.1] -17.4949 21.706 -0.806 0.430 -62.926 27.937
Omnibus: 0.564 Durbin-Watson: 1.904
Prob(Omnibus): 0.754 Jarque-Bera (JB): 0.621
Skew: -0.112 Prob(JB): 0.733
Kurtosis: 2.227 Cond. No. 276.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.650
Model: OLS Adj. R-squared: 0.615
Method: Least Squares F-statistic: 18.57
Date: Thu, 03 Apr 2025 Prob (F-statistic): 2.76e-05
Time: 22:53:55 Log-Likelihood: -101.03
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 39.4225 66.559 0.592 0.560 -99.416 178.261
C(dose)[T.1] 51.9537 10.732 4.841 0.000 29.566 74.341
expression 2.3967 10.744 0.223 0.826 -20.014 24.808
Omnibus: 0.121 Durbin-Watson: 1.934
Prob(Omnibus): 0.941 Jarque-Bera (JB): 0.344
Skew: 0.022 Prob(JB): 0.842
Kurtosis: 2.402 Cond. No. 101.

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: 22:53:55 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: 0.240
Model: OLS Adj. R-squared: 0.204
Method: Least Squares F-statistic: 6.622
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0177
Time: 22:53:55 Log-Likelihood: -109.95
No. Observations: 23 AIC: 223.9
Df Residuals: 21 BIC: 226.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -129.4445 81.522 -1.588 0.127 -298.978 40.089
expression 32.4510 12.610 2.573 0.018 6.227 58.675
Omnibus: 1.088 Durbin-Watson: 2.398
Prob(Omnibus): 0.580 Jarque-Bera (JB): 0.454
Skew: 0.342 Prob(JB): 0.797
Kurtosis: 3.081 Cond. No. 85.7

CP101

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

F-statistic p-value df difference
0.397 0.541 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.472
Model: OLS Adj. R-squared: 0.328
Method: Least Squares F-statistic: 3.278
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0625
Time: 22:53:55 Log-Likelihood: -70.510
No. Observations: 15 AIC: 149.0
Df Residuals: 11 BIC: 151.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 45.9432 133.122 0.345 0.737 -247.056 338.942
C(dose)[T.1] -11.8622 180.919 -0.066 0.949 -410.063 386.339
expression 3.9439 24.341 0.162 0.874 -49.630 57.517
expression:C(dose)[T.1] 11.3174 33.188 0.341 0.740 -61.729 84.364
Omnibus: 2.028 Durbin-Watson: 0.740
Prob(Omnibus): 0.363 Jarque-Bera (JB): 1.568
Skew: -0.717 Prob(JB): 0.457
Kurtosis: 2.327 Cond. No. 172.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.466
Model: OLS Adj. R-squared: 0.377
Method: Least Squares F-statistic: 5.245
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0231
Time: 22:53:55 Log-Likelihood: -70.589
No. Observations: 15 AIC: 147.2
Df Residuals: 12 BIC: 149.3
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 12.7792 87.491 0.146 0.886 -177.848 203.407
C(dose)[T.1] 49.5880 15.498 3.200 0.008 15.820 83.356
expression 10.0316 15.925 0.630 0.541 -24.667 44.730
Omnibus: 2.527 Durbin-Watson: 0.823
Prob(Omnibus): 0.283 Jarque-Bera (JB): 1.896
Skew: -0.817 Prob(JB): 0.388
Kurtosis: 2.395 Cond. No. 64.1

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: 22:53:55 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: 0.011
Model: OLS Adj. R-squared: -0.065
Method: Least Squares F-statistic: 0.1473
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.707
Time: 22:53:55 Log-Likelihood: -75.216
No. Observations: 15 AIC: 154.4
Df Residuals: 13 BIC: 155.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 50.3187 113.396 0.444 0.665 -194.657 295.295
expression 7.9876 20.812 0.384 0.707 -36.974 52.949
Omnibus: 0.422 Durbin-Watson: 1.675
Prob(Omnibus): 0.810 Jarque-Bera (JB): 0.508
Skew: 0.034 Prob(JB): 0.776
Kurtosis: 2.101 Cond. No. 63.2