LID SPN TRAP

1449472_at

Gpr12 G-protein coupled receptor 12

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
2.060 0.167 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.682
Model: OLS Adj. R-squared: 0.632
Method: Least Squares F-statistic: 13.57
Date: Thu, 21 Nov 2024 Prob (F-statistic): 5.75e-05
Time: 04:24:28 Log-Likelihood: -99.935
No. Observations: 23 AIC: 207.9
Df Residuals: 19 BIC: 212.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -182.7755 218.787 -0.835 0.414 -640.701 275.150
C(dose)[T.1] 51.3549 347.601 0.148 0.884 -676.182 778.892
expression 29.2710 27.013 1.084 0.292 -27.269 85.811
expression:C(dose)[T.1] 0.5681 43.205 0.013 0.990 -89.862 90.998
Omnibus: 0.340 Durbin-Watson: 2.093
Prob(Omnibus): 0.844 Jarque-Bera (JB): 0.500
Skew: 0.180 Prob(JB): 0.779
Kurtosis: 2.373 Cond. No. 822.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.682
Model: OLS Adj. R-squared: 0.650
Method: Least Squares F-statistic: 21.43
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.06e-05
Time: 04:24:28 Log-Likelihood: -99.935
No. Observations: 23 AIC: 205.9
Df Residuals: 20 BIC: 209.3
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -184.5735 166.465 -1.109 0.281 -531.814 162.667
C(dose)[T.1] 55.9239 8.543 6.546 0.000 38.104 73.744
expression 29.4931 20.549 1.435 0.167 -13.370 72.357
Omnibus: 0.333 Durbin-Watson: 2.094
Prob(Omnibus): 0.846 Jarque-Bera (JB): 0.496
Skew: 0.177 Prob(JB): 0.780
Kurtosis: 2.373 Cond. No. 327.

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: 04:24:28 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: 0.000
Model: OLS Adj. R-squared: -0.048
Method: Least Squares F-statistic: 0.001024
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.975
Time: 04:24:28 Log-Likelihood: -113.10
No. Observations: 23 AIC: 230.2
Df Residuals: 21 BIC: 232.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 70.7594 279.981 0.253 0.803 -511.492 653.011
expression 1.1122 34.750 0.032 0.975 -71.155 73.379
Omnibus: 3.259 Durbin-Watson: 2.494
Prob(Omnibus): 0.196 Jarque-Bera (JB): 1.565
Skew: 0.292 Prob(JB): 0.457
Kurtosis: 1.863 Cond. No. 317.

CP101

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

F-statistic p-value df difference
0.907 0.360 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.490
Model: OLS Adj. R-squared: 0.352
Method: Least Squares F-statistic: 3.530
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0521
Time: 04:24:28 Log-Likelihood: -70.243
No. Observations: 15 AIC: 148.5
Df Residuals: 11 BIC: 151.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -163.4814 256.350 -0.638 0.537 -727.704 400.741
C(dose)[T.1] 164.1122 469.171 0.350 0.733 -868.525 1196.750
expression 27.4013 30.389 0.902 0.387 -39.485 94.288
expression:C(dose)[T.1] -13.9170 54.851 -0.254 0.804 -134.642 106.809
Omnibus: 2.070 Durbin-Watson: 0.666
Prob(Omnibus): 0.355 Jarque-Bera (JB): 1.605
Skew: -0.720 Prob(JB): 0.448
Kurtosis: 2.295 Cond. No. 632.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.487
Model: OLS Adj. R-squared: 0.402
Method: Least Squares F-statistic: 5.707
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0181
Time: 04:24:28 Log-Likelihood: -70.287
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 -127.4821 205.013 -0.622 0.546 -574.166 319.202
C(dose)[T.1] 45.1449 15.762 2.864 0.014 10.802 79.488
expression 23.1294 24.293 0.952 0.360 -29.800 76.058
Omnibus: 2.111 Durbin-Watson: 0.661
Prob(Omnibus): 0.348 Jarque-Bera (JB): 1.627
Skew: -0.691 Prob(JB): 0.443
Kurtosis: 2.167 Cond. No. 235.

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: 04:24:28 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: 0.137
Model: OLS Adj. R-squared: 0.071
Method: Least Squares F-statistic: 2.066
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.174
Time: 04:24:28 Log-Likelihood: -74.194
No. Observations: 15 AIC: 152.4
Df Residuals: 13 BIC: 153.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -263.4494 248.630 -1.060 0.309 -800.582 273.684
expression 41.9131 29.160 1.437 0.174 -21.082 104.909
Omnibus: 2.567 Durbin-Watson: 1.617
Prob(Omnibus): 0.277 Jarque-Bera (JB): 1.163
Skew: 0.253 Prob(JB): 0.559
Kurtosis: 1.733 Cond. No. 228.