LID SPN TRAP

1447830_s_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
0.052 0.823 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.653
Model: OLS Adj. R-squared: 0.598
Method: Least Squares F-statistic: 11.90
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000130
Time: 05:01:45 Log-Likelihood: -100.94
No. Observations: 23 AIC: 209.9
Df Residuals: 19 BIC: 214.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 97.7561 538.553 0.182 0.858 -1029.449 1224.961
C(dose)[T.1] -261.8677 822.102 -0.319 0.754 -1982.547 1458.812
expression -3.7611 46.510 -0.081 0.936 -101.109 93.586
expression:C(dose)[T.1] 27.6127 71.673 0.385 0.704 -122.402 177.627
Omnibus: 0.389 Durbin-Watson: 1.831
Prob(Omnibus): 0.823 Jarque-Bera (JB): 0.521
Skew: 0.027 Prob(JB): 0.771
Kurtosis: 2.265 Cond. No. 2.66e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.650
Model: OLS Adj. R-squared: 0.615
Method: Least Squares F-statistic: 18.57
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.76e-05
Time: 05:01:45 Log-Likelihood: -101.03
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 -36.8740 400.962 -0.092 0.928 -873.266 799.518
C(dose)[T.1] 54.8235 10.932 5.015 0.000 32.019 77.628
expression 7.8666 34.626 0.227 0.823 -64.362 80.095
Omnibus: 0.269 Durbin-Watson: 1.866
Prob(Omnibus): 0.874 Jarque-Bera (JB): 0.453
Skew: 0.062 Prob(JB): 0.798
Kurtosis: 2.324 Cond. No. 1.06e+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: 05:01:45 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: 0.210
Model: OLS Adj. R-squared: 0.172
Method: Least Squares F-statistic: 5.576
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0279
Time: 05:01:45 Log-Likelihood: -110.40
No. Observations: 23 AIC: 224.8
Df Residuals: 21 BIC: 227.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 1183.1503 467.326 2.532 0.019 211.293 2155.008
expression -96.0504 40.675 -2.361 0.028 -180.640 -11.461
Omnibus: 1.831 Durbin-Watson: 2.529
Prob(Omnibus): 0.400 Jarque-Bera (JB): 1.393
Skew: 0.409 Prob(JB): 0.498
Kurtosis: 2.115 Cond. No. 843.

CP101

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

F-statistic p-value df difference
18.422 0.001 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.785
Model: OLS Adj. R-squared: 0.726
Method: Least Squares F-statistic: 13.38
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000545
Time: 05:01:45 Log-Likelihood: -63.774
No. Observations: 15 AIC: 135.5
Df Residuals: 11 BIC: 138.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 1201.1396 508.226 2.363 0.038 82.542 2319.737
C(dose)[T.1] 257.1517 636.952 0.404 0.694 -1144.770 1659.073
expression -100.5531 45.072 -2.231 0.047 -199.755 -1.351
expression:C(dose)[T.1] -19.6719 56.699 -0.347 0.735 -144.465 105.121
Omnibus: 2.760 Durbin-Watson: 1.490
Prob(Omnibus): 0.252 Jarque-Bera (JB): 0.925
Skew: -0.523 Prob(JB): 0.630
Kurtosis: 3.621 Cond. No. 1.98e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.783
Model: OLS Adj. R-squared: 0.746
Method: Least Squares F-statistic: 21.60
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000106
Time: 05:01:45 Log-Likelihood: -63.856
No. Observations: 15 AIC: 133.7
Df Residuals: 12 BIC: 135.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 1341.2956 296.880 4.518 0.001 694.449 1988.142
C(dose)[T.1] 36.1897 10.339 3.500 0.004 13.662 58.717
expression -112.9841 26.324 -4.292 0.001 -170.338 -55.630
Omnibus: 1.849 Durbin-Watson: 1.632
Prob(Omnibus): 0.397 Jarque-Bera (JB): 0.490
Skew: -0.392 Prob(JB): 0.783
Kurtosis: 3.409 Cond. No. 681.

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: 05:01:45 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: 0.561
Model: OLS Adj. R-squared: 0.527
Method: Least Squares F-statistic: 16.58
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00132
Time: 05:01:45 Log-Likelihood: -69.133
No. Observations: 15 AIC: 142.3
Df Residuals: 13 BIC: 143.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 1663.4106 385.514 4.315 0.001 830.559 2496.262
expression -139.9889 34.375 -4.072 0.001 -214.251 -65.727
Omnibus: 1.696 Durbin-Watson: 1.685
Prob(Omnibus): 0.428 Jarque-Bera (JB): 0.909
Skew: 0.134 Prob(JB): 0.635
Kurtosis: 1.824 Cond. No. 647.