LID SPN TRAP

1421679_a_at

Cdkn1a cyclin-dependent kinase inhibitor 1A (P21)

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
0.143 0.709 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.743
Model: OLS Adj. R-squared: 0.702
Method: Least Squares F-statistic: 18.28
Date: Thu, 21 Nov 2024 Prob (F-statistic): 7.94e-06
Time: 04:47:34 Log-Likelihood: -97.495
No. Observations: 23 AIC: 203.0
Df Residuals: 19 BIC: 207.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -132.8405 190.930 -0.696 0.495 -532.461 266.780
C(dose)[T.1] 1048.1084 382.256 2.742 0.013 248.038 1848.178
expression 16.5774 16.915 0.980 0.339 -18.826 51.980
expression:C(dose)[T.1] -84.5134 32.583 -2.594 0.018 -152.711 -16.316
Omnibus: 1.097 Durbin-Watson: 1.601
Prob(Omnibus): 0.578 Jarque-Bera (JB): 1.012
Skew: 0.444 Prob(JB): 0.603
Kurtosis: 2.483 Cond. No. 1.38e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.652
Model: OLS Adj. R-squared: 0.617
Method: Least Squares F-statistic: 18.70
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.64e-05
Time: 04:47:34 Log-Likelihood: -100.98
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 124.1403 185.112 0.671 0.510 -261.996 510.277
C(dose)[T.1] 57.0934 13.233 4.314 0.000 29.489 84.698
expression -6.1978 16.397 -0.378 0.709 -40.401 28.006
Omnibus: 0.707 Durbin-Watson: 1.943
Prob(Omnibus): 0.702 Jarque-Bera (JB): 0.681
Skew: 0.096 Prob(JB): 0.712
Kurtosis: 2.180 Cond. No. 496.

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: 04:47:34 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: 0.327
Model: OLS Adj. R-squared: 0.295
Method: Least Squares F-statistic: 10.21
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00434
Time: 04:47:34 Log-Likelihood: -108.55
No. Observations: 23 AIC: 221.1
Df Residuals: 21 BIC: 223.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -463.3749 170.029 -2.725 0.013 -816.969 -109.781
expression 46.9265 14.683 3.196 0.004 16.392 77.461
Omnibus: 0.772 Durbin-Watson: 1.741
Prob(Omnibus): 0.680 Jarque-Bera (JB): 0.787
Skew: 0.265 Prob(JB): 0.675
Kurtosis: 2.265 Cond. No. 335.

CP101

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

F-statistic p-value df difference
2.950 0.112 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.574
Model: OLS Adj. R-squared: 0.458
Method: Least Squares F-statistic: 4.936
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0207
Time: 04:47:34 Log-Likelihood: -68.904
No. Observations: 15 AIC: 145.8
Df Residuals: 11 BIC: 148.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -461.1974 412.454 -1.118 0.287 -1369.002 446.607
C(dose)[T.1] 336.7011 454.936 0.740 0.475 -664.607 1338.009
expression 56.8256 44.323 1.282 0.226 -40.729 154.380
expression:C(dose)[T.1] -31.5071 48.680 -0.647 0.531 -138.651 75.637
Omnibus: 1.648 Durbin-Watson: 1.704
Prob(Omnibus): 0.439 Jarque-Bera (JB): 0.915
Skew: -0.169 Prob(JB): 0.633
Kurtosis: 1.839 Cond. No. 926.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.558
Model: OLS Adj. R-squared: 0.484
Method: Least Squares F-statistic: 7.560
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00750
Time: 04:47:34 Log-Likelihood: -69.185
No. Observations: 15 AIC: 144.4
Df Residuals: 12 BIC: 146.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -218.2180 166.639 -1.310 0.215 -581.293 144.857
C(dose)[T.1] 42.4136 14.644 2.896 0.013 10.507 74.321
expression 30.7061 17.879 1.717 0.112 -8.249 69.661
Omnibus: 1.116 Durbin-Watson: 1.457
Prob(Omnibus): 0.572 Jarque-Bera (JB): 0.783
Skew: -0.179 Prob(JB): 0.676
Kurtosis: 1.940 Cond. No. 226.

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: 04:47:34 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: 0.248
Model: OLS Adj. R-squared: 0.190
Method: Least Squares F-statistic: 4.293
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0587
Time: 04:47:34 Log-Likelihood: -73.160
No. Observations: 15 AIC: 150.3
Df Residuals: 13 BIC: 151.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -327.1522 203.302 -1.609 0.132 -766.359 112.055
expression 44.6710 21.561 2.072 0.059 -1.908 91.250
Omnibus: 1.615 Durbin-Watson: 2.064
Prob(Omnibus): 0.446 Jarque-Bera (JB): 1.231
Skew: 0.643 Prob(JB): 0.540
Kurtosis: 2.440 Cond. No. 220.