LID SPN TRAP

1460559_at

Kank2 KN motif and ankyrin repeat domains 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
0.086 0.772 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.659
Model: OLS Adj. R-squared: 0.606
Method: Least Squares F-statistic: 12.26
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000108
Time: 05:12:47 Log-Likelihood: -100.72
No. Observations: 23 AIC: 209.4
Df Residuals: 19 BIC: 214.0
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 72.3145 27.651 2.615 0.017 14.441 130.188
C(dose)[T.1] 24.5882 41.165 0.597 0.557 -61.571 110.747
expression -3.6738 5.471 -0.672 0.510 -15.125 7.777
expression:C(dose)[T.1] 6.0330 8.580 0.703 0.490 -11.924 23.990
Omnibus: 0.279 Durbin-Watson: 1.880
Prob(Omnibus): 0.870 Jarque-Bera (JB): 0.460
Skew: -0.110 Prob(JB): 0.794
Kurtosis: 2.343 Cond. No. 58.4

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.651
Model: OLS Adj. R-squared: 0.616
Method: Least Squares F-statistic: 18.62
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.71e-05
Time: 05:12:47 Log-Likelihood: -101.01
No. Observations: 23 AIC: 208.0
Df Residuals: 20 BIC: 211.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 60.2248 21.380 2.817 0.011 15.627 104.823
C(dose)[T.1] 52.8280 8.921 5.921 0.000 34.218 71.438
expression -1.2208 4.161 -0.293 0.772 -9.900 7.458
Omnibus: 0.693 Durbin-Watson: 1.943
Prob(Omnibus): 0.707 Jarque-Bera (JB): 0.678
Skew: 0.106 Prob(JB): 0.713
Kurtosis: 2.187 Cond. No. 25.0

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: 05:12:48 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: 0.038
Model: OLS Adj. R-squared: -0.008
Method: Least Squares F-statistic: 0.8277
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.373
Time: 05:12:48 Log-Likelihood: -112.66
No. Observations: 23 AIC: 229.3
Df Residuals: 21 BIC: 231.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 108.1506 32.044 3.375 0.003 41.511 174.790
expression -6.0126 6.609 -0.910 0.373 -19.756 7.731
Omnibus: 1.626 Durbin-Watson: 2.519
Prob(Omnibus): 0.444 Jarque-Bera (JB): 1.344
Skew: 0.430 Prob(JB): 0.511
Kurtosis: 2.186 Cond. No. 22.8

CP101

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

F-statistic p-value df difference
0.595 0.455 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.484
Model: OLS Adj. R-squared: 0.344
Method: Least Squares F-statistic: 3.444
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0554
Time: 05:12:48 Log-Likelihood: -70.333
No. Observations: 15 AIC: 148.7
Df Residuals: 11 BIC: 151.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 102.2430 41.727 2.450 0.032 10.403 194.083
C(dose)[T.1] 17.8456 68.845 0.259 0.800 -133.681 169.372
expression -6.0891 7.010 -0.869 0.404 -21.517 9.339
expression:C(dose)[T.1] 5.4526 12.100 0.451 0.661 -21.178 32.084
Omnibus: 2.381 Durbin-Watson: 0.997
Prob(Omnibus): 0.304 Jarque-Bera (JB): 1.628
Skew: -0.784 Prob(JB): 0.443
Kurtosis: 2.619 Cond. No. 63.4

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.475
Model: OLS Adj. R-squared: 0.387
Method: Least Squares F-statistic: 5.425
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0210
Time: 05:12:48 Log-Likelihood: -70.470
No. Observations: 15 AIC: 146.9
Df Residuals: 12 BIC: 149.1
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 91.7793 33.499 2.740 0.018 18.792 164.767
C(dose)[T.1] 48.0232 15.438 3.111 0.009 14.386 81.660
expression -4.2590 5.521 -0.771 0.455 -16.287 7.769
Omnibus: 2.461 Durbin-Watson: 0.889
Prob(Omnibus): 0.292 Jarque-Bera (JB): 1.645
Skew: -0.793 Prob(JB): 0.439
Kurtosis: 2.663 Cond. No. 26.2

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: 05:12:48 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: 0.051
Model: OLS Adj. R-squared: -0.022
Method: Least Squares F-statistic: 0.7036
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.417
Time: 05:12:48 Log-Likelihood: -74.905
No. Observations: 15 AIC: 153.8
Df Residuals: 13 BIC: 155.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 126.8148 40.738 3.113 0.008 38.806 214.824
expression -5.9506 7.094 -0.839 0.417 -21.276 9.375
Omnibus: 1.411 Durbin-Watson: 1.715
Prob(Omnibus): 0.494 Jarque-Bera (JB): 0.886
Skew: 0.220 Prob(JB): 0.642
Kurtosis: 1.894 Cond. No. 24.3