LID SPN TRAP

1459894_at

Iqgap2 IQ motif containing GTPase activating protein 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
0.662 0.425 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.664
Model: OLS Adj. R-squared: 0.611
Method: Least Squares F-statistic: 12.54
Date: Thu, 21 Nov 2024 Prob (F-statistic): 9.41e-05
Time: 04:30:26 Log-Likelihood: -100.55
No. Observations: 23 AIC: 209.1
Df Residuals: 19 BIC: 213.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 80.8918 113.900 0.710 0.486 -157.504 319.287
C(dose)[T.1] 126.9337 159.121 0.798 0.435 -206.111 459.978
expression -3.6611 15.605 -0.235 0.817 -36.324 29.001
expression:C(dose)[T.1] -10.8702 22.404 -0.485 0.633 -57.762 36.022
Omnibus: 0.008 Durbin-Watson: 1.996
Prob(Omnibus): 0.996 Jarque-Bera (JB): 0.125
Skew: 0.025 Prob(JB): 0.939
Kurtosis: 2.642 Cond. No. 338.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.660
Model: OLS Adj. R-squared: 0.626
Method: Least Squares F-statistic: 19.44
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.05e-05
Time: 04:30:26 Log-Likelihood: -100.69
No. Observations: 23 AIC: 207.4
Df Residuals: 20 BIC: 210.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 119.3307 80.254 1.487 0.153 -48.077 286.739
C(dose)[T.1] 49.8758 9.620 5.185 0.000 29.809 69.942
expression -8.9352 10.981 -0.814 0.425 -31.841 13.971
Omnibus: 0.045 Durbin-Watson: 2.116
Prob(Omnibus): 0.978 Jarque-Bera (JB): 0.260
Skew: -0.035 Prob(JB): 0.878
Kurtosis: 2.484 Cond. No. 136.

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: 04:30:26 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: 0.204
Model: OLS Adj. R-squared: 0.166
Method: Least Squares F-statistic: 5.373
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0306
Time: 04:30:26 Log-Likelihood: -110.48
No. Observations: 23 AIC: 225.0
Df Residuals: 21 BIC: 227.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 322.0044 104.725 3.075 0.006 104.217 539.792
expression -34.1102 14.716 -2.318 0.031 -64.713 -3.507
Omnibus: 0.012 Durbin-Watson: 2.686
Prob(Omnibus): 0.994 Jarque-Bera (JB): 0.120
Skew: -0.030 Prob(JB): 0.942
Kurtosis: 2.650 Cond. No. 118.

CP101

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

F-statistic p-value df difference
0.566 0.467 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.481
Model: OLS Adj. R-squared: 0.340
Method: Least Squares F-statistic: 3.404
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0570
Time: 04:30:26 Log-Likelihood: -70.375
No. Observations: 15 AIC: 148.8
Df Residuals: 11 BIC: 151.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 188.6518 151.306 1.247 0.238 -144.371 521.675
C(dose)[T.1] -39.4168 213.864 -0.184 0.857 -510.128 431.295
expression -17.7052 22.033 -0.804 0.439 -66.201 30.790
expression:C(dose)[T.1] 12.8381 31.491 0.408 0.691 -56.474 82.150
Omnibus: 1.946 Durbin-Watson: 0.980
Prob(Omnibus): 0.378 Jarque-Bera (JB): 1.459
Skew: -0.711 Prob(JB): 0.482
Kurtosis: 2.440 Cond. No. 247.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.474
Model: OLS Adj. R-squared: 0.386
Method: Least Squares F-statistic: 5.398
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0213
Time: 04:30:26 Log-Likelihood: -70.488
No. Observations: 15 AIC: 147.0
Df Residuals: 12 BIC: 149.1
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 145.6220 104.576 1.392 0.189 -82.230 373.474
C(dose)[T.1] 47.5217 15.542 3.058 0.010 13.659 81.384
expression -11.4205 15.185 -0.752 0.467 -44.507 21.666
Omnibus: 2.213 Durbin-Watson: 0.860
Prob(Omnibus): 0.331 Jarque-Bera (JB): 1.691
Skew: -0.755 Prob(JB): 0.429
Kurtosis: 2.347 Cond. No. 94.8

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: 04:30:27 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: 0.063
Model: OLS Adj. R-squared: -0.009
Method: Least Squares F-statistic: 0.8806
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.365
Time: 04:30:27 Log-Likelihood: -74.808
No. Observations: 15 AIC: 153.6
Df Residuals: 13 BIC: 155.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 215.9977 130.729 1.652 0.122 -66.426 498.421
expression -18.0735 19.260 -0.938 0.365 -59.681 23.534
Omnibus: 3.329 Durbin-Watson: 1.576
Prob(Omnibus): 0.189 Jarque-Bera (JB): 1.273
Skew: 0.232 Prob(JB): 0.529
Kurtosis: 1.650 Cond. No. 92.2