LID SPN TRAP

1419247_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
0.434 0.517 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.672
Model: OLS Adj. R-squared: 0.620
Method: Least Squares F-statistic: 12.96
Date: Thu, 21 Nov 2024 Prob (F-statistic): 7.66e-05
Time: 04:28:49 Log-Likelihood: -100.29
No. Observations: 23 AIC: 208.6
Df Residuals: 19 BIC: 213.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 191.5497 645.444 0.297 0.770 -1159.380 1542.479
C(dose)[T.1] -789.2078 898.630 -0.878 0.391 -2670.063 1091.647
expression -11.3015 53.110 -0.213 0.834 -122.461 99.858
expression:C(dose)[T.1] 69.9325 74.315 0.941 0.358 -85.610 225.475
Omnibus: 0.518 Durbin-Watson: 1.880
Prob(Omnibus): 0.772 Jarque-Bera (JB): 0.593
Skew: 0.086 Prob(JB): 0.744
Kurtosis: 2.233 Cond. No. 3.28e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.657
Model: OLS Adj. R-squared: 0.622
Method: Least Squares F-statistic: 19.11
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.29e-05
Time: 04:28:49 Log-Likelihood: -100.82
No. Observations: 23 AIC: 207.6
Df Residuals: 20 BIC: 211.0
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -242.5037 450.197 -0.539 0.596 -1181.598 696.591
C(dose)[T.1] 56.3813 9.829 5.736 0.000 35.879 76.884
expression 24.4157 37.042 0.659 0.517 -52.853 101.685
Omnibus: 0.415 Durbin-Watson: 1.792
Prob(Omnibus): 0.812 Jarque-Bera (JB): 0.545
Skew: 0.118 Prob(JB): 0.761
Kurtosis: 2.283 Cond. No. 1.27e+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: 04:28:49 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: 0.091
Model: OLS Adj. R-squared: 0.048
Method: Least Squares F-statistic: 2.112
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.161
Time: 04:28:49 Log-Likelihood: -112.00
No. Observations: 23 AIC: 228.0
Df Residuals: 21 BIC: 230.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 991.8929 627.651 1.580 0.129 -313.379 2297.165
expression -75.4308 51.899 -1.453 0.161 -183.362 32.500
Omnibus: 1.693 Durbin-Watson: 2.578
Prob(Omnibus): 0.429 Jarque-Bera (JB): 1.336
Skew: 0.406 Prob(JB): 0.513
Kurtosis: 2.143 Cond. No. 1.11e+03

CP101

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

F-statistic p-value df difference
7.013 0.021 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: 0.567
Method: Least Squares F-statistic: 7.111
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00633
Time: 04:28:49 Log-Likelihood: -67.214
No. Observations: 15 AIC: 142.4
Df Residuals: 11 BIC: 145.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 477.3505 847.130 0.563 0.584 -1387.169 2341.870
C(dose)[T.1] 491.5018 909.910 0.540 0.600 -1511.197 2494.200
expression -35.5420 73.445 -0.484 0.638 -197.194 126.110
expression:C(dose)[T.1] -39.4394 79.041 -0.499 0.628 -213.408 134.529
Omnibus: 1.388 Durbin-Watson: 0.941
Prob(Omnibus): 0.500 Jarque-Bera (JB): 0.865
Skew: -0.569 Prob(JB): 0.649
Kurtosis: 2.703 Cond. No. 2.53e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.652
Model: OLS Adj. R-squared: 0.594
Method: Least Squares F-statistic: 11.25
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00177
Time: 04:28:49 Log-Likelihood: -67.381
No. Observations: 15 AIC: 140.8
Df Residuals: 12 BIC: 142.9
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 870.0960 303.236 2.869 0.014 209.402 1530.790
C(dose)[T.1] 37.5324 13.257 2.831 0.015 8.647 66.418
expression -69.5948 26.280 -2.648 0.021 -126.854 -12.336
Omnibus: 1.858 Durbin-Watson: 0.971
Prob(Omnibus): 0.395 Jarque-Bera (JB): 1.052
Skew: -0.643 Prob(JB): 0.591
Kurtosis: 2.833 Cond. No. 561.

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: 04:28:49 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: 0.420
Model: OLS Adj. R-squared: 0.375
Method: Least Squares F-statistic: 9.403
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00901
Time: 04:28:49 Log-Likelihood: -71.218
No. Observations: 15 AIC: 146.4
Df Residuals: 13 BIC: 147.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 1172.9879 352.060 3.332 0.005 412.408 1933.568
expression -94.3129 30.756 -3.066 0.009 -160.758 -27.868
Omnibus: 1.958 Durbin-Watson: 1.665
Prob(Omnibus): 0.376 Jarque-Bera (JB): 0.455
Skew: -0.325 Prob(JB): 0.796
Kurtosis: 3.554 Cond. No. 525.