LID SPN TRAP

1457440_at

Sstr4 somatostatin receptor 4

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
0.013 0.910 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.660
Model: OLS Adj. R-squared: 0.607
Method: Least Squares F-statistic: 12.31
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000106
Time: 03:51:13 Log-Likelihood: -100.69
No. Observations: 23 AIC: 209.4
Df Residuals: 19 BIC: 213.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 83.2574 139.025 0.599 0.556 -207.725 374.240
C(dose)[T.1] -243.5683 377.239 -0.646 0.526 -1033.138 546.002
expression -3.3517 16.025 -0.209 0.837 -36.893 30.190
expression:C(dose)[T.1] 31.3943 40.054 0.784 0.443 -52.439 115.228
Omnibus: 0.199 Durbin-Watson: 1.837
Prob(Omnibus): 0.905 Jarque-Bera (JB): 0.405
Skew: 0.005 Prob(JB): 0.817
Kurtosis: 2.350 Cond. No. 919.

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: 18.51
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.82e-05
Time: 03:51:13 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 39.7019 126.202 0.315 0.756 -223.550 302.954
C(dose)[T.1] 51.8560 15.573 3.330 0.003 19.372 84.340
expression 1.6738 14.545 0.115 0.910 -28.666 32.013
Omnibus: 0.270 Durbin-Watson: 1.915
Prob(Omnibus): 0.874 Jarque-Bera (JB): 0.453
Skew: 0.052 Prob(JB): 0.797
Kurtosis: 2.320 Cond. No. 267.

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: 03:51:13 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: 0.455
Model: OLS Adj. R-squared: 0.429
Method: Least Squares F-statistic: 17.52
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000417
Time: 03:51:13 Log-Likelihood: -106.13
No. Observations: 23 AIC: 216.3
Df Residuals: 21 BIC: 218.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -299.3595 90.719 -3.300 0.003 -488.020 -110.699
expression 41.7022 9.963 4.186 0.000 20.984 62.421
Omnibus: 1.232 Durbin-Watson: 2.512
Prob(Omnibus): 0.540 Jarque-Bera (JB): 0.866
Skew: -0.079 Prob(JB): 0.649
Kurtosis: 2.063 Cond. No. 157.

CP101

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

F-statistic p-value df difference
0.008 0.932 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.572
Model: OLS Adj. R-squared: 0.456
Method: Least Squares F-statistic: 4.909
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0210
Time: 03:51:13 Log-Likelihood: -68.928
No. Observations: 15 AIC: 145.9
Df Residuals: 11 BIC: 148.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 145.8417 69.201 2.108 0.059 -6.468 298.151
C(dose)[T.1] -132.5840 103.014 -1.287 0.225 -359.316 94.148
expression -14.3001 12.472 -1.147 0.276 -41.751 13.150
expression:C(dose)[T.1] 32.5410 18.271 1.781 0.103 -7.674 72.756
Omnibus: 2.510 Durbin-Watson: 1.091
Prob(Omnibus): 0.285 Jarque-Bera (JB): 1.170
Skew: -0.680 Prob(JB): 0.557
Kurtosis: 3.143 Cond. No. 108.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.449
Model: OLS Adj. R-squared: 0.357
Method: Least Squares F-statistic: 4.892
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0279
Time: 03:51:13 Log-Likelihood: -70.828
No. Observations: 15 AIC: 147.7
Df Residuals: 12 BIC: 149.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 62.7022 55.515 1.129 0.281 -58.254 183.659
C(dose)[T.1] 49.0383 15.839 3.096 0.009 14.528 83.549
expression 0.8619 9.905 0.087 0.932 -20.719 22.443
Omnibus: 2.716 Durbin-Watson: 0.828
Prob(Omnibus): 0.257 Jarque-Bera (JB): 1.844
Skew: -0.840 Prob(JB): 0.398
Kurtosis: 2.647 Cond. No. 41.3

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: 03:51:13 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: 0.009
Model: OLS Adj. R-squared: -0.067
Method: Least Squares F-statistic: 0.1193
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.735
Time: 03:51:13 Log-Likelihood: -75.232
No. Observations: 15 AIC: 154.5
Df Residuals: 13 BIC: 155.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 69.2260 71.483 0.968 0.351 -85.204 223.656
expression 4.3791 12.679 0.345 0.735 -23.012 31.770
Omnibus: 0.166 Durbin-Watson: 1.632
Prob(Omnibus): 0.921 Jarque-Bera (JB): 0.372
Skew: -0.086 Prob(JB): 0.830
Kurtosis: 2.248 Cond. No. 41.1