LID SPN TRAP

1426037_a_at

Rgs16 regulator of G-protein signaling 16

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
0.004 0.947 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.667
Model: OLS Adj. R-squared: 0.614
Method: Least Squares F-statistic: 12.66
Date: Thu, 21 Nov 2024 Prob (F-statistic): 8.89e-05
Time: 05:00:17 Log-Likelihood: -100.48
No. Observations: 23 AIC: 209.0
Df Residuals: 19 BIC: 213.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 99.1861 55.830 1.777 0.092 -17.667 216.039
C(dose)[T.1] -20.8603 75.380 -0.277 0.785 -178.632 136.912
expression -8.1986 10.117 -0.810 0.428 -29.373 12.976
expression:C(dose)[T.1] 13.0505 13.114 0.995 0.332 -14.396 40.497
Omnibus: 0.366 Durbin-Watson: 2.088
Prob(Omnibus): 0.833 Jarque-Bera (JB): 0.508
Skew: 0.008 Prob(JB): 0.776
Kurtosis: 2.272 Cond. No. 139.

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: 18.50
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.83e-05
Time: 05:00:17 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 56.5765 35.822 1.579 0.130 -18.146 131.299
C(dose)[T.1] 53.5686 9.424 5.684 0.000 33.911 73.226
expression -0.4317 6.435 -0.067 0.947 -13.856 12.992
Omnibus: 0.290 Durbin-Watson: 1.910
Prob(Omnibus): 0.865 Jarque-Bera (JB): 0.466
Skew: 0.067 Prob(JB): 0.792
Kurtosis: 2.316 Cond. No. 49.1

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: 05:00:17 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: 0.082
Model: OLS Adj. R-squared: 0.039
Method: Least Squares F-statistic: 1.882
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.185
Time: 05:00:17 Log-Likelihood: -112.12
No. Observations: 23 AIC: 228.2
Df Residuals: 21 BIC: 230.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 5.2539 54.713 0.096 0.924 -108.528 119.035
expression 12.9670 9.451 1.372 0.185 -6.688 32.622
Omnibus: 3.083 Durbin-Watson: 2.210
Prob(Omnibus): 0.214 Jarque-Bera (JB): 1.362
Skew: 0.156 Prob(JB): 0.506
Kurtosis: 1.849 Cond. No. 47.2

CP101

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

F-statistic p-value df difference
1.159 0.303 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.554
Model: OLS Adj. R-squared: 0.432
Method: Least Squares F-statistic: 4.554
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0262
Time: 05:00:17 Log-Likelihood: -69.245
No. Observations: 15 AIC: 146.5
Df Residuals: 11 BIC: 149.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 234.6339 104.371 2.248 0.046 4.914 464.353
C(dose)[T.1] -118.0841 144.111 -0.819 0.430 -435.271 199.103
expression -24.4528 15.182 -1.611 0.136 -57.868 8.962
expression:C(dose)[T.1] 24.4635 20.699 1.182 0.262 -21.095 70.022
Omnibus: 3.578 Durbin-Watson: 1.564
Prob(Omnibus): 0.167 Jarque-Bera (JB): 1.882
Skew: -0.862 Prob(JB): 0.390
Kurtosis: 3.189 Cond. No. 188.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.497
Model: OLS Adj. R-squared: 0.414
Method: Least Squares F-statistic: 5.936
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0161
Time: 05:00:17 Log-Likelihood: -70.141
No. Observations: 15 AIC: 146.3
Df Residuals: 12 BIC: 148.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 144.6455 72.557 1.994 0.069 -13.442 302.733
C(dose)[T.1] 51.3223 15.160 3.385 0.005 18.292 84.352
expression -11.2925 10.489 -1.077 0.303 -34.146 11.561
Omnibus: 9.935 Durbin-Watson: 0.983
Prob(Omnibus): 0.007 Jarque-Bera (JB): 6.155
Skew: -1.381 Prob(JB): 0.0461
Kurtosis: 4.490 Cond. No. 69.1

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, 21 Nov 2024 Prob (F-statistic): 0.00629
Time: 05:00:17 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: 0.017
Model: OLS Adj. R-squared: -0.058
Method: Least Squares F-statistic: 0.2278
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.641
Time: 05:00:17 Log-Likelihood: -75.170
No. Observations: 15 AIC: 154.3
Df Residuals: 13 BIC: 155.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 139.9267 97.455 1.436 0.175 -70.611 350.465
expression -6.6674 13.971 -0.477 0.641 -36.849 23.514
Omnibus: 1.613 Durbin-Watson: 1.803
Prob(Omnibus): 0.446 Jarque-Bera (JB): 0.878
Skew: 0.102 Prob(JB): 0.645
Kurtosis: 1.832 Cond. No. 68.9