LID SPN TRAP

1448285_at

Rgs4 regulator of G-protein signaling 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
0.003 0.955 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.666
Model: OLS Adj. R-squared: 0.613
Method: Least Squares F-statistic: 12.63
Date: Thu, 21 Nov 2024 Prob (F-statistic): 9.02e-05
Time: 05:25:44 Log-Likelihood: -100.49
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 183.5268 266.083 0.690 0.499 -373.391 740.445
C(dose)[T.1] -546.2103 612.235 -0.892 0.383 -1827.634 735.213
expression -11.5435 23.746 -0.486 0.632 -61.243 38.156
expression:C(dose)[T.1] 52.3772 53.443 0.980 0.339 -59.481 164.236
Omnibus: 0.083 Durbin-Watson: 1.967
Prob(Omnibus): 0.960 Jarque-Bera (JB): 0.069
Skew: -0.061 Prob(JB): 0.966
Kurtosis: 2.760 Cond. No. 1.86e+03

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: 05:25:44 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 67.6919 238.156 0.284 0.779 -429.093 564.477
C(dose)[T.1] 53.7139 11.007 4.880 0.000 30.754 76.674
expression -1.2036 21.252 -0.057 0.955 -45.534 43.127
Omnibus: 0.329 Durbin-Watson: 1.881
Prob(Omnibus): 0.849 Jarque-Bera (JB): 0.489
Skew: 0.058 Prob(JB): 0.783
Kurtosis: 2.295 Cond. No. 623.

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: 05:25:45 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: 0.231
Model: OLS Adj. R-squared: 0.195
Method: Least Squares F-statistic: 6.319
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0202
Time: 05:25:45 Log-Likelihood: -110.08
No. Observations: 23 AIC: 224.2
Df Residuals: 21 BIC: 226.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -618.1896 277.710 -2.226 0.037 -1195.719 -40.660
expression 61.4766 24.456 2.514 0.020 10.617 112.336
Omnibus: 3.475 Durbin-Watson: 2.806
Prob(Omnibus): 0.176 Jarque-Bera (JB): 1.372
Skew: 0.040 Prob(JB): 0.504
Kurtosis: 1.806 Cond. No. 502.

CP101

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

F-statistic p-value df difference
4.793 0.049 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.606
Model: OLS Adj. R-squared: 0.499
Method: Least Squares F-statistic: 5.642
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0137
Time: 05:25:45 Log-Likelihood: -68.312
No. Observations: 15 AIC: 144.6
Df Residuals: 11 BIC: 147.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -675.0191 502.086 -1.344 0.206 -1780.103 430.064
C(dose)[T.1] 46.2368 709.928 0.065 0.949 -1516.305 1608.778
expression 63.4649 42.910 1.479 0.167 -30.979 157.909
expression:C(dose)[T.1] -0.1917 60.462 -0.003 0.998 -133.268 132.885
Omnibus: 1.834 Durbin-Watson: 1.213
Prob(Omnibus): 0.400 Jarque-Bera (JB): 1.439
Skew: -0.675 Prob(JB): 0.487
Kurtosis: 2.308 Cond. No. 1.61e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.606
Model: OLS Adj. R-squared: 0.540
Method: Least Squares F-statistic: 9.233
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00373
Time: 05:25:45 Log-Likelihood: -68.312
No. Observations: 15 AIC: 142.6
Df Residuals: 12 BIC: 144.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -673.8898 338.734 -1.989 0.070 -1411.929 64.149
C(dose)[T.1] 43.9868 13.516 3.254 0.007 14.538 73.436
expression 63.3684 28.943 2.189 0.049 0.306 126.430
Omnibus: 1.836 Durbin-Watson: 1.212
Prob(Omnibus): 0.399 Jarque-Bera (JB): 1.439
Skew: -0.676 Prob(JB): 0.487
Kurtosis: 2.310 Cond. No. 604.

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: 05:25:45 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: 0.258
Model: OLS Adj. R-squared: 0.201
Method: Least Squares F-statistic: 4.531
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0530
Time: 05:25:45 Log-Likelihood: -73.057
No. Observations: 15 AIC: 150.1
Df Residuals: 13 BIC: 151.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -845.1472 441.115 -1.916 0.078 -1798.117 107.823
expression 79.9508 37.559 2.129 0.053 -1.190 161.091
Omnibus: 3.377 Durbin-Watson: 1.853
Prob(Omnibus): 0.185 Jarque-Bera (JB): 1.316
Skew: 0.270 Prob(JB): 0.518
Kurtosis: 1.653 Cond. No. 596.