LID SPN TRAP

1456314_x_at

Cdk2ap1 CDK2 (cyclin-dependent kinase 2)-associated protein 1

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
1.030 0.322 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.690
Model: OLS Adj. R-squared: 0.641
Method: Least Squares F-statistic: 14.11
Date: Thu, 21 Nov 2024 Prob (F-statistic): 4.49e-05
Time: 04:34:33 Log-Likelihood: -99.631
No. Observations: 23 AIC: 207.3
Df Residuals: 19 BIC: 211.8
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -18.2595 270.542 -0.067 0.947 -584.511 547.992
C(dose)[T.1] -602.1967 538.290 -1.119 0.277 -1728.850 524.457
expression 7.0327 26.249 0.268 0.792 -47.907 61.972
expression:C(dose)[T.1] 62.4257 51.575 1.210 0.241 -45.523 170.374
Omnibus: 0.075 Durbin-Watson: 1.708
Prob(Omnibus): 0.963 Jarque-Bera (JB): 0.295
Skew: -0.045 Prob(JB): 0.863
Kurtosis: 2.452 Cond. No. 1.58e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.666
Model: OLS Adj. R-squared: 0.633
Method: Least Squares F-statistic: 19.96
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.71e-05
Time: 04:34:33 Log-Likelihood: -100.48
No. Observations: 23 AIC: 207.0
Df Residuals: 20 BIC: 210.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -184.8766 235.595 -0.785 0.442 -676.318 306.565
C(dose)[T.1] 49.2382 9.458 5.206 0.000 29.510 68.966
expression 23.2021 22.856 1.015 0.322 -24.475 70.879
Omnibus: 0.245 Durbin-Watson: 1.673
Prob(Omnibus): 0.885 Jarque-Bera (JB): 0.420
Skew: -0.171 Prob(JB): 0.811
Kurtosis: 2.433 Cond. No. 579.

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: 04:34:33 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: 0.214
Model: OLS Adj. R-squared: 0.177
Method: Least Squares F-statistic: 5.716
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0263
Time: 04:34:33 Log-Likelihood: -110.34
No. Observations: 23 AIC: 224.7
Df Residuals: 21 BIC: 226.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -689.1220 321.652 -2.142 0.044 -1358.034 -20.210
expression 74.0055 30.955 2.391 0.026 9.631 138.380
Omnibus: 0.391 Durbin-Watson: 2.254
Prob(Omnibus): 0.822 Jarque-Bera (JB): 0.464
Skew: -0.264 Prob(JB): 0.793
Kurtosis: 2.547 Cond. No. 527.

CP101

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

F-statistic p-value df difference
8.629 0.012 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.711
Model: OLS Adj. R-squared: 0.632
Method: Least Squares F-statistic: 9.016
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00266
Time: 04:34:33 Log-Likelihood: -65.993
No. Observations: 15 AIC: 140.0
Df Residuals: 11 BIC: 142.8
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 276.4460 353.991 0.781 0.451 -502.683 1055.575
C(dose)[T.1] 467.7736 407.802 1.147 0.276 -429.793 1365.341
expression -22.1624 37.523 -0.591 0.567 -104.749 60.425
expression:C(dose)[T.1] -47.9942 43.811 -1.095 0.297 -144.421 48.433
Omnibus: 3.663 Durbin-Watson: 1.006
Prob(Omnibus): 0.160 Jarque-Bera (JB): 1.997
Skew: -0.891 Prob(JB): 0.368
Kurtosis: 3.142 Cond. No. 937.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.679
Model: OLS Adj. R-squared: 0.626
Method: Least Squares F-statistic: 12.71
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00109
Time: 04:34:33 Log-Likelihood: -66.770
No. Observations: 15 AIC: 139.5
Df Residuals: 12 BIC: 141.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 608.4793 184.398 3.300 0.006 206.711 1010.247
C(dose)[T.1] 21.3410 15.298 1.395 0.188 -11.991 54.673
expression -57.3683 19.530 -2.937 0.012 -99.920 -14.816
Omnibus: 0.490 Durbin-Watson: 0.933
Prob(Omnibus): 0.783 Jarque-Bera (JB): 0.520
Skew: -0.343 Prob(JB): 0.771
Kurtosis: 2.398 Cond. No. 287.

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: 04:34:33 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: 0.627
Model: OLS Adj. R-squared: 0.599
Method: Least Squares F-statistic: 21.88
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000432
Time: 04:34:33 Log-Likelihood: -67.897
No. Observations: 15 AIC: 139.8
Df Residuals: 13 BIC: 141.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 774.7581 145.723 5.317 0.000 459.942 1089.574
expression -74.2559 15.873 -4.678 0.000 -108.548 -39.964
Omnibus: 0.288 Durbin-Watson: 1.417
Prob(Omnibus): 0.866 Jarque-Bera (JB): 0.424
Skew: -0.243 Prob(JB): 0.809
Kurtosis: 2.335 Cond. No. 218.