LID SPN TRAP

1451452_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
1.111 0.304 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.673
Model: OLS Adj. R-squared: 0.621
Method: Least Squares F-statistic: 13.04
Date: Thu, 21 Nov 2024 Prob (F-statistic): 7.39e-05
Time: 03:47:05 Log-Likelihood: -100.25
No. Observations: 23 AIC: 208.5
Df Residuals: 19 BIC: 213.0
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 68.3111 113.465 0.602 0.554 -169.174 305.797
C(dose)[T.1] 136.4330 140.532 0.971 0.344 -157.703 430.569
expression -1.8847 15.142 -0.124 0.902 -33.577 29.808
expression:C(dose)[T.1] -10.5012 18.447 -0.569 0.576 -49.110 28.108
Omnibus: 0.128 Durbin-Watson: 1.683
Prob(Omnibus): 0.938 Jarque-Bera (JB): 0.297
Skew: -0.141 Prob(JB): 0.862
Kurtosis: 2.520 Cond. No. 355.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.668
Model: OLS Adj. R-squared: 0.634
Method: Least Squares F-statistic: 20.08
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.65e-05
Time: 03:47:05 Log-Likelihood: -100.44
No. Observations: 23 AIC: 206.9
Df Residuals: 20 BIC: 210.3
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 121.2589 63.883 1.898 0.072 -11.999 254.517
C(dose)[T.1] 56.6043 9.081 6.233 0.000 37.661 75.548
expression -8.9605 8.501 -1.054 0.304 -26.693 8.772
Omnibus: 0.112 Durbin-Watson: 1.712
Prob(Omnibus): 0.946 Jarque-Bera (JB): 0.319
Skew: -0.096 Prob(JB): 0.853
Kurtosis: 2.456 Cond. No. 117.

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: 03:47:05 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: 0.022
Model: OLS Adj. R-squared: -0.025
Method: Least Squares F-statistic: 0.4653
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.503
Time: 03:47:05 Log-Likelihood: -112.85
No. Observations: 23 AIC: 229.7
Df Residuals: 21 BIC: 232.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 9.8520 102.672 0.096 0.924 -203.667 223.371
expression 9.1240 13.376 0.682 0.503 -18.693 36.941
Omnibus: 2.527 Durbin-Watson: 2.487
Prob(Omnibus): 0.283 Jarque-Bera (JB): 1.524
Skew: 0.365 Prob(JB): 0.467
Kurtosis: 1.971 Cond. No. 112.

CP101

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

F-statistic p-value df difference
2.538 0.137 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.545
Model: OLS Adj. R-squared: 0.421
Method: Least Squares F-statistic: 4.396
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0290
Time: 03:47:05 Log-Likelihood: -69.390
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 -62.6562 143.186 -0.438 0.670 -377.806 252.494
C(dose)[T.1] 17.9235 194.734 0.092 0.928 -410.683 446.530
expression 16.3143 17.905 0.911 0.382 -23.095 55.724
expression:C(dose)[T.1] 1.8176 23.222 0.078 0.939 -49.293 52.928
Omnibus: 2.295 Durbin-Watson: 0.621
Prob(Omnibus): 0.317 Jarque-Bera (JB): 1.427
Skew: -0.510 Prob(JB): 0.490
Kurtosis: 1.884 Cond. No. 313.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.545
Model: OLS Adj. R-squared: 0.469
Method: Least Squares F-statistic: 7.187
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00887
Time: 03:47:05 Log-Likelihood: -69.394
No. Observations: 15 AIC: 144.8
Df Residuals: 12 BIC: 146.9
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -71.2728 87.688 -0.813 0.432 -262.329 119.783
C(dose)[T.1] 33.0985 17.510 1.890 0.083 -5.052 71.249
expression 17.3950 10.919 1.593 0.137 -6.395 41.185
Omnibus: 2.475 Durbin-Watson: 0.623
Prob(Omnibus): 0.290 Jarque-Bera (JB): 1.464
Skew: -0.505 Prob(JB): 0.481
Kurtosis: 1.850 Cond. No. 107.

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: 03:47:05 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: 0.410
Model: OLS Adj. R-squared: 0.364
Method: Least Squares F-statistic: 9.016
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0102
Time: 03:47:05 Log-Likelihood: -71.349
No. Observations: 15 AIC: 146.7
Df Residuals: 13 BIC: 148.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -154.4745 83.008 -1.861 0.086 -333.803 24.854
expression 29.3061 9.760 3.003 0.010 8.221 50.391
Omnibus: 0.674 Durbin-Watson: 1.222
Prob(Omnibus): 0.714 Jarque-Bera (JB): 0.605
Skew: -0.023 Prob(JB): 0.739
Kurtosis: 2.017 Cond. No. 91.7