LID SPN TRAP

1453851_a_at

Gadd45g growth arrest and DNA-damage-inducible 45 gamma

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
0.326 0.574 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.691
Model: OLS Adj. R-squared: 0.642
Method: Least Squares F-statistic: 14.16
Date: Thu, 21 Nov 2024 Prob (F-statistic): 4.39e-05
Time: 04:19:50 Log-Likelihood: -99.603
No. Observations: 23 AIC: 207.2
Df Residuals: 19 BIC: 211.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 76.5961 157.243 0.487 0.632 -252.518 405.710
C(dose)[T.1] 913.7625 574.744 1.590 0.128 -289.190 2116.715
expression -2.4593 17.261 -0.142 0.888 -38.587 33.669
expression:C(dose)[T.1] -91.0015 61.014 -1.491 0.152 -218.705 36.702
Omnibus: 1.654 Durbin-Watson: 1.691
Prob(Omnibus): 0.437 Jarque-Bera (JB): 1.243
Skew: 0.351 Prob(JB): 0.537
Kurtosis: 2.104 Cond. No. 1.46e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.655
Model: OLS Adj. R-squared: 0.620
Method: Least Squares F-statistic: 18.96
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.41e-05
Time: 04:19:50 Log-Likelihood: -100.88
No. Observations: 23 AIC: 207.8
Df Residuals: 20 BIC: 211.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 142.8989 155.378 0.920 0.369 -181.213 467.011
C(dose)[T.1] 56.6730 10.478 5.409 0.000 34.817 78.529
expression -9.7426 17.055 -0.571 0.574 -45.319 25.834
Omnibus: 0.668 Durbin-Watson: 1.991
Prob(Omnibus): 0.716 Jarque-Bera (JB): 0.657
Skew: 0.066 Prob(JB): 0.720
Kurtosis: 2.183 Cond. No. 336.

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: 04:19:50 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: 0.150
Model: OLS Adj. R-squared: 0.109
Method: Least Squares F-statistic: 3.693
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0683
Time: 04:19:50 Log-Likelihood: -111.24
No. Observations: 23 AIC: 226.5
Df Residuals: 21 BIC: 228.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -306.4925 201.090 -1.524 0.142 -724.681 111.696
expression 41.6750 21.687 1.922 0.068 -3.426 86.776
Omnibus: 2.008 Durbin-Watson: 2.173
Prob(Omnibus): 0.366 Jarque-Bera (JB): 1.097
Skew: 0.114 Prob(JB): 0.578
Kurtosis: 1.954 Cond. No. 283.

CP101

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

F-statistic p-value df difference
12.934 0.004 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.735
Model: OLS Adj. R-squared: 0.663
Method: Least Squares F-statistic: 10.17
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00167
Time: 04:19:50 Log-Likelihood: -65.337
No. Observations: 15 AIC: 138.7
Df Residuals: 11 BIC: 141.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -497.3708 279.089 -1.782 0.102 -1111.641 116.899
C(dose)[T.1] 65.6508 341.391 0.192 0.851 -685.746 817.048
expression 63.6520 31.439 2.025 0.068 -5.545 132.849
expression:C(dose)[T.1] -4.8075 37.854 -0.127 0.901 -88.122 78.508
Omnibus: 0.622 Durbin-Watson: 1.613
Prob(Omnibus): 0.733 Jarque-Bera (JB): 0.473
Skew: -0.383 Prob(JB): 0.789
Kurtosis: 2.587 Cond. No. 803.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.735
Model: OLS Adj. R-squared: 0.690
Method: Least Squares F-statistic: 16.62
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000349
Time: 04:19:50 Log-Likelihood: -65.348
No. Observations: 15 AIC: 136.7
Df Residuals: 12 BIC: 138.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -467.9454 149.079 -3.139 0.009 -792.761 -143.129
C(dose)[T.1] 22.3288 13.230 1.688 0.117 -6.498 51.155
expression 60.3358 16.777 3.596 0.004 23.782 96.890
Omnibus: 0.710 Durbin-Watson: 1.579
Prob(Omnibus): 0.701 Jarque-Bera (JB): 0.504
Skew: -0.406 Prob(JB): 0.777
Kurtosis: 2.616 Cond. No. 253.

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: 04:19:50 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: 0.672
Model: OLS Adj. R-squared: 0.646
Method: Least Squares F-statistic: 26.60
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000184
Time: 04:19:50 Log-Likelihood: -66.945
No. Observations: 15 AIC: 137.9
Df Residuals: 13 BIC: 139.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -601.7020 134.945 -4.459 0.001 -893.234 -310.170
expression 76.3242 14.798 5.158 0.000 44.355 108.293
Omnibus: 0.617 Durbin-Watson: 1.935
Prob(Omnibus): 0.735 Jarque-Bera (JB): 0.589
Skew: 0.070 Prob(JB): 0.745
Kurtosis: 2.040 Cond. No. 214.