LID SPN TRAP

1424638_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
0.031 0.862 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: 11.78
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000138
Time: 05:05:39 Log-Likelihood: -101.02
No. Observations: 23 AIC: 210.0
Df Residuals: 19 BIC: 214.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 44.6520 213.904 0.209 0.837 -403.055 492.359
C(dose)[T.1] -32.3783 426.167 -0.076 0.940 -924.355 859.599
expression 0.8623 19.294 0.045 0.965 -39.520 41.245
expression:C(dose)[T.1] 7.3575 37.192 0.198 0.845 -70.487 85.202
Omnibus: 0.373 Durbin-Watson: 1.906
Prob(Omnibus): 0.830 Jarque-Bera (JB): 0.513
Skew: 0.043 Prob(JB): 0.774
Kurtosis: 2.274 Cond. No. 1.29e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.650
Model: OLS Adj. R-squared: 0.615
Method: Least Squares F-statistic: 18.54
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.79e-05
Time: 05:05:39 Log-Likelihood: -101.04
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 22.7100 178.452 0.127 0.900 -349.534 394.954
C(dose)[T.1] 51.8914 11.992 4.327 0.000 26.878 76.905
expression 2.8423 16.094 0.177 0.862 -30.729 36.413
Omnibus: 0.331 Durbin-Watson: 1.897
Prob(Omnibus): 0.848 Jarque-Bera (JB): 0.490
Skew: 0.053 Prob(JB): 0.783
Kurtosis: 2.293 Cond. No. 467.

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: 05:05:39 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: 0.322
Model: OLS Adj. R-squared: 0.289
Method: Least Squares F-statistic: 9.952
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00478
Time: 05:05:39 Log-Likelihood: -108.64
No. Observations: 23 AIC: 221.3
Df Residuals: 21 BIC: 223.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -490.8714 180.969 -2.712 0.013 -867.218 -114.525
expression 50.3824 15.971 3.155 0.005 17.169 83.595
Omnibus: 2.826 Durbin-Watson: 2.307
Prob(Omnibus): 0.243 Jarque-Bera (JB): 1.276
Skew: -0.107 Prob(JB): 0.528
Kurtosis: 1.866 Cond. No. 348.

CP101

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

F-statistic p-value df difference
3.220 0.098 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.596
Model: OLS Adj. R-squared: 0.485
Method: Least Squares F-statistic: 5.402
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0157
Time: 05:05:39 Log-Likelihood: -68.508
No. Observations: 15 AIC: 145.0
Df Residuals: 11 BIC: 147.8
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 129.5934 381.595 0.340 0.741 -710.292 969.479
C(dose)[T.1] -323.7349 412.345 -0.785 0.449 -1231.300 583.830
expression -5.9020 36.216 -0.163 0.873 -85.613 73.809
expression:C(dose)[T.1] 35.5632 39.156 0.908 0.383 -50.618 121.744
Omnibus: 0.494 Durbin-Watson: 1.400
Prob(Omnibus): 0.781 Jarque-Bera (JB): 0.000
Skew: 0.013 Prob(JB): 1.00
Kurtosis: 3.008 Cond. No. 966.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.565
Model: OLS Adj. R-squared: 0.493
Method: Least Squares F-statistic: 7.805
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00674
Time: 05:05:39 Log-Likelihood: -69.050
No. Observations: 15 AIC: 144.1
Df Residuals: 12 BIC: 146.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -190.8562 144.309 -1.323 0.211 -505.278 123.565
C(dose)[T.1] 50.5597 13.997 3.612 0.004 20.063 81.056
expression 24.5219 13.667 1.794 0.098 -5.255 54.299
Omnibus: 0.130 Durbin-Watson: 1.173
Prob(Omnibus): 0.937 Jarque-Bera (JB): 0.339
Skew: 0.114 Prob(JB): 0.844
Kurtosis: 2.300 Cond. No. 220.

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: 05:05:39 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: 0.093
Model: OLS Adj. R-squared: 0.023
Method: Least Squares F-statistic: 1.330
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.270
Time: 05:05:39 Log-Likelihood: -74.570
No. Observations: 15 AIC: 153.1
Df Residuals: 13 BIC: 154.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -135.7455 199.191 -0.681 0.508 -566.071 294.580
expression 21.8422 18.942 1.153 0.270 -19.081 62.765
Omnibus: 0.067 Durbin-Watson: 1.742
Prob(Omnibus): 0.967 Jarque-Bera (JB): 0.150
Skew: 0.109 Prob(JB): 0.928
Kurtosis: 2.561 Cond. No. 218.