LID SPN TRAP

1449773_s_at

Gadd45b growth arrest and DNA-damage-inducible 45 beta

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
2.628 0.121 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: 14.09
Date: Thu, 21 Nov 2024 Prob (F-statistic): 4.53e-05
Time: 03:56:02 Log-Likelihood: -99.641
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 -86.4811 104.655 -0.826 0.419 -305.526 132.564
C(dose)[T.1] 53.8908 198.827 0.271 0.789 -362.259 470.041
expression 15.1080 11.221 1.346 0.194 -8.377 38.593
expression:C(dose)[T.1] -1.1755 20.200 -0.058 0.954 -43.454 41.103
Omnibus: 0.503 Durbin-Watson: 1.822
Prob(Omnibus): 0.778 Jarque-Bera (JB): 0.590
Skew: 0.107 Prob(JB): 0.745
Kurtosis: 2.245 Cond. No. 555.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.690
Model: OLS Adj. R-squared: 0.659
Method: Least Squares F-statistic: 22.24
Date: Thu, 21 Nov 2024 Prob (F-statistic): 8.24e-06
Time: 03:56:02 Log-Likelihood: -99.643
No. Observations: 23 AIC: 205.3
Df Residuals: 20 BIC: 208.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -83.1033 84.886 -0.979 0.339 -260.173 93.966
C(dose)[T.1] 42.3378 10.677 3.965 0.001 20.065 64.610
expression 14.7453 9.095 1.621 0.121 -4.226 33.717
Omnibus: 0.487 Durbin-Watson: 1.834
Prob(Omnibus): 0.784 Jarque-Bera (JB): 0.585
Skew: 0.123 Prob(JB): 0.746
Kurtosis: 2.259 Cond. No. 203.

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: 03:56:02 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: 0.446
Model: OLS Adj. R-squared: 0.420
Method: Least Squares F-statistic: 16.90
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000498
Time: 03:56:02 Log-Likelihood: -106.31
No. Observations: 23 AIC: 216.6
Df Residuals: 21 BIC: 218.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -284.4195 88.728 -3.206 0.004 -468.940 -99.899
expression 37.6602 9.160 4.111 0.000 18.611 56.709
Omnibus: 1.422 Durbin-Watson: 2.317
Prob(Omnibus): 0.491 Jarque-Bera (JB): 0.987
Skew: 0.498 Prob(JB): 0.611
Kurtosis: 2.810 Cond. No. 162.

CP101

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

F-statistic p-value df difference
0.871 0.369 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.496
Model: OLS Adj. R-squared: 0.358
Method: Least Squares F-statistic: 3.602
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0495
Time: 03:56:02 Log-Likelihood: -70.168
No. Observations: 15 AIC: 148.3
Df Residuals: 11 BIC: 151.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 17.9367 210.285 0.085 0.934 -444.897 480.770
C(dose)[T.1] -76.8990 276.226 -0.278 0.786 -684.869 531.071
expression 5.9972 25.443 0.236 0.818 -50.003 61.998
expression:C(dose)[T.1] 15.1712 33.346 0.455 0.658 -58.223 88.566
Omnibus: 2.588 Durbin-Watson: 0.865
Prob(Omnibus): 0.274 Jarque-Bera (JB): 1.809
Skew: -0.684 Prob(JB): 0.405
Kurtosis: 1.989 Cond. No. 408.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.486
Model: OLS Adj. R-squared: 0.400
Method: Least Squares F-statistic: 5.674
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0184
Time: 03:56:02 Log-Likelihood: -70.308
No. Observations: 15 AIC: 146.6
Df Residuals: 12 BIC: 148.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -54.9525 131.630 -0.417 0.684 -341.750 231.845
C(dose)[T.1] 48.5689 15.213 3.193 0.008 15.423 81.715
expression 14.8297 15.894 0.933 0.369 -19.800 49.459
Omnibus: 2.796 Durbin-Watson: 1.001
Prob(Omnibus): 0.247 Jarque-Bera (JB): 2.052
Skew: -0.778 Prob(JB): 0.358
Kurtosis: 2.073 Cond. No. 146.

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: 03:56:02 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: 0.050
Model: OLS Adj. R-squared: -0.024
Method: Least Squares F-statistic: 0.6773
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.425
Time: 03:56:02 Log-Likelihood: -74.919
No. Observations: 15 AIC: 153.8
Df Residuals: 13 BIC: 155.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -47.6123 171.958 -0.277 0.786 -419.106 323.881
expression 17.0729 20.746 0.823 0.425 -27.746 61.892
Omnibus: 0.361 Durbin-Watson: 1.892
Prob(Omnibus): 0.835 Jarque-Bera (JB): 0.480
Skew: 0.019 Prob(JB): 0.787
Kurtosis: 2.124 Cond. No. 146.