LID SPN TRAP

1450700_at

Cdc42ep3 CDC42 effector protein (Rho GTPase binding) 3

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
0.022 0.884 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.650
Model: OLS Adj. R-squared: 0.595
Method: Least Squares F-statistic: 11.76
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000139
Time: 04:34:47 Log-Likelihood: -101.03
No. Observations: 23 AIC: 210.1
Df Residuals: 19 BIC: 214.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 61.0151 227.291 0.268 0.791 -414.710 536.740
C(dose)[T.1] 128.4577 433.172 0.297 0.770 -778.183 1035.098
expression -0.7096 23.687 -0.030 0.976 -50.287 48.868
expression:C(dose)[T.1] -7.6295 44.378 -0.172 0.865 -100.514 85.255
Omnibus: 0.133 Durbin-Watson: 1.837
Prob(Omnibus): 0.936 Jarque-Bera (JB): 0.354
Skew: -0.030 Prob(JB): 0.838
Kurtosis: 2.395 Cond. No. 1.13e+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: 18.53
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.80e-05
Time: 04:34:47 Log-Likelihood: -101.05
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 81.8641 187.513 0.437 0.667 -309.281 473.009
C(dose)[T.1] 54.0075 9.873 5.470 0.000 33.414 74.601
expression -2.8832 19.539 -0.148 0.884 -43.640 37.874
Omnibus: 0.304 Durbin-Watson: 1.884
Prob(Omnibus): 0.859 Jarque-Bera (JB): 0.473
Skew: 0.004 Prob(JB): 0.790
Kurtosis: 2.298 Cond. No. 421.

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: 04:34:47 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: 0.125
Model: OLS Adj. R-squared: 0.083
Method: Least Squares F-statistic: 2.997
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0981
Time: 04:34:47 Log-Likelihood: -111.57
No. Observations: 23 AIC: 227.1
Df Residuals: 21 BIC: 229.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -369.5739 259.622 -1.424 0.169 -909.487 170.339
expression 46.3035 26.747 1.731 0.098 -9.321 101.928
Omnibus: 1.570 Durbin-Watson: 2.554
Prob(Omnibus): 0.456 Jarque-Bera (JB): 1.319
Skew: 0.430 Prob(JB): 0.517
Kurtosis: 2.202 Cond. No. 377.

CP101

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

F-statistic p-value df difference
2.409 0.147 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.556
Model: OLS Adj. R-squared: 0.435
Method: Least Squares F-statistic: 4.597
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0255
Time: 04:34:47 Log-Likelihood: -69.206
No. Observations: 15 AIC: 146.4
Df Residuals: 11 BIC: 149.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 221.3178 269.197 0.822 0.428 -371.181 813.817
C(dose)[T.1] 273.4870 365.695 0.748 0.470 -531.402 1078.376
expression -17.4738 30.542 -0.572 0.579 -84.697 49.749
expression:C(dose)[T.1] -25.6757 41.582 -0.617 0.549 -117.198 65.846
Omnibus: 1.797 Durbin-Watson: 0.851
Prob(Omnibus): 0.407 Jarque-Bera (JB): 1.415
Skew: -0.630 Prob(JB): 0.493
Kurtosis: 2.176 Cond. No. 599.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.541
Model: OLS Adj. R-squared: 0.464
Method: Least Squares F-statistic: 7.070
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00936
Time: 04:34:47 Log-Likelihood: -69.461
No. Observations: 15 AIC: 144.9
Df Residuals: 12 BIC: 147.0
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 343.3094 178.074 1.928 0.078 -44.680 731.299
C(dose)[T.1] 47.8666 14.390 3.326 0.006 16.514 79.219
expression -31.3257 20.185 -1.552 0.147 -75.304 12.653
Omnibus: 2.067 Durbin-Watson: 0.898
Prob(Omnibus): 0.356 Jarque-Bera (JB): 1.481
Skew: -0.588 Prob(JB): 0.477
Kurtosis: 2.008 Cond. No. 222.

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: 04:34:47 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: 0.118
Model: OLS Adj. R-squared: 0.050
Method: Least Squares F-statistic: 1.732
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.211
Time: 04:34:47 Log-Likelihood: -74.362
No. Observations: 15 AIC: 152.7
Df Residuals: 13 BIC: 154.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 403.9605 235.951 1.712 0.111 -105.780 913.701
expression -35.3240 26.839 -1.316 0.211 -93.306 22.658
Omnibus: 0.095 Durbin-Watson: 1.673
Prob(Omnibus): 0.954 Jarque-Bera (JB): 0.320
Skew: 0.050 Prob(JB): 0.852
Kurtosis: 2.291 Cond. No. 220.