LID SPN TRAP

1419248_at

Rgs2 regulator of G-protein signaling 2

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
0.151 0.701 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.652
Model: OLS Adj. R-squared: 0.597
Method: Least Squares F-statistic: 11.88
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000131
Time: 03:49:46 Log-Likelihood: -100.96
No. Observations: 23 AIC: 209.9
Df Residuals: 19 BIC: 214.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 173.1932 510.895 0.339 0.738 -896.123 1242.509
C(dose)[T.1] 229.7568 986.452 0.233 0.818 -1834.911 2294.424
expression -10.3264 44.336 -0.233 0.818 -103.123 82.470
expression:C(dose)[T.1] -15.6071 86.333 -0.181 0.858 -196.303 165.089
Omnibus: 0.352 Durbin-Watson: 1.910
Prob(Omnibus): 0.838 Jarque-Bera (JB): 0.501
Skew: -0.034 Prob(JB): 0.778
Kurtosis: 2.280 Cond. No. 3.00e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.652
Model: OLS Adj. R-squared: 0.617
Method: Least Squares F-statistic: 18.71
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.63e-05
Time: 03:49:46 Log-Likelihood: -100.98
No. Observations: 23 AIC: 208.0
Df Residuals: 20 BIC: 211.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 220.6204 427.658 0.516 0.612 -671.458 1112.699
C(dose)[T.1] 51.4370 10.009 5.139 0.000 30.559 72.315
expression -14.4425 37.112 -0.389 0.701 -91.856 62.971
Omnibus: 0.487 Durbin-Watson: 1.902
Prob(Omnibus): 0.784 Jarque-Bera (JB): 0.570
Skew: -0.014 Prob(JB): 0.752
Kurtosis: 2.229 Cond. No. 1.13e+03

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: 03:49:46 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: 0.192
Model: OLS Adj. R-squared: 0.153
Method: Least Squares F-statistic: 4.981
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0367
Time: 03:49:47 Log-Likelihood: -110.66
No. Observations: 23 AIC: 225.3
Df Residuals: 21 BIC: 227.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 1311.4572 551.942 2.376 0.027 163.631 2459.284
expression -107.4864 48.161 -2.232 0.037 -207.643 -7.330
Omnibus: 3.924 Durbin-Watson: 2.566
Prob(Omnibus): 0.141 Jarque-Bera (JB): 1.556
Skew: 0.197 Prob(JB): 0.459
Kurtosis: 1.788 Cond. No. 982.

CP101

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

F-statistic p-value df difference
8.392 0.013 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.678
Model: OLS Adj. R-squared: 0.590
Method: Least Squares F-statistic: 7.707
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00476
Time: 03:49:47 Log-Likelihood: -66.810
No. Observations: 15 AIC: 141.6
Df Residuals: 11 BIC: 144.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 1083.3595 536.825 2.018 0.069 -98.184 2264.903
C(dose)[T.1] -130.1834 673.670 -0.193 0.850 -1612.921 1352.554
expression -90.4418 47.783 -1.893 0.085 -195.612 14.728
expression:C(dose)[T.1] 15.7172 60.036 0.262 0.798 -116.421 147.856
Omnibus: 1.130 Durbin-Watson: 1.471
Prob(Omnibus): 0.568 Jarque-Bera (JB): 0.648
Skew: -0.494 Prob(JB): 0.723
Kurtosis: 2.752 Cond. No. 1.71e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.676
Model: OLS Adj. R-squared: 0.622
Method: Least Squares F-statistic: 12.50
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00116
Time: 03:49:47 Log-Likelihood: -66.856
No. Observations: 15 AIC: 139.7
Df Residuals: 12 BIC: 141.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 971.5207 312.216 3.112 0.009 291.261 1651.780
C(dose)[T.1] 46.1494 12.120 3.808 0.002 19.742 72.556
expression -80.4855 27.783 -2.897 0.013 -141.020 -19.951
Omnibus: 1.590 Durbin-Watson: 1.364
Prob(Omnibus): 0.452 Jarque-Bera (JB): 0.756
Skew: -0.549 Prob(JB): 0.685
Kurtosis: 2.945 Cond. No. 587.

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: 03:49:47 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: 0.284
Model: OLS Adj. R-squared: 0.229
Method: Least Squares F-statistic: 5.149
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0409
Time: 03:49:47 Log-Likelihood: -72.798
No. Observations: 15 AIC: 149.6
Df Residuals: 13 BIC: 151.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 1099.0814 443.183 2.480 0.028 141.644 2056.519
expression -89.6668 39.517 -2.269 0.041 -175.039 -4.295
Omnibus: 2.343 Durbin-Watson: 2.043
Prob(Omnibus): 0.310 Jarque-Bera (JB): 1.311
Skew: 0.422 Prob(JB): 0.519
Kurtosis: 1.824 Cond. No. 583.