LID SPN TRAP

1450928_at

Id4 inhibitor of DNA binding 4

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
2.194 0.154 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.685
Model: OLS Adj. R-squared: 0.635
Method: Least Squares F-statistic: 13.75
Date: Thu, 21 Nov 2024 Prob (F-statistic): 5.30e-05
Time: 04:52:31 Log-Likelihood: -99.835
No. Observations: 23 AIC: 207.7
Df Residuals: 19 BIC: 212.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 393.8653 285.412 1.380 0.184 -203.509 991.240
C(dose)[T.1] -49.7401 398.470 -0.125 0.902 -883.747 784.267
expression -32.4059 27.225 -1.190 0.249 -89.388 24.576
expression:C(dose)[T.1] 8.8164 38.853 0.227 0.823 -72.503 90.136
Omnibus: 0.032 Durbin-Watson: 2.164
Prob(Omnibus): 0.984 Jarque-Bera (JB): 0.203
Skew: -0.072 Prob(JB): 0.903
Kurtosis: 2.562 Cond. No. 1.25e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.684
Model: OLS Adj. R-squared: 0.652
Method: Least Squares F-statistic: 21.62
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.00e-05
Time: 04:52:31 Log-Likelihood: -99.866
No. Observations: 23 AIC: 205.7
Df Residuals: 20 BIC: 209.1
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 348.4925 198.778 1.753 0.095 -66.150 763.135
C(dose)[T.1] 40.6378 11.951 3.400 0.003 15.709 65.567
expression -28.0770 18.957 -1.481 0.154 -67.620 11.467
Omnibus: 0.014 Durbin-Watson: 2.195
Prob(Omnibus): 0.993 Jarque-Bera (JB): 0.143
Skew: -0.047 Prob(JB): 0.931
Kurtosis: 2.625 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: 04:52:31 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: 0.501
Model: OLS Adj. R-squared: 0.477
Method: Least Squares F-statistic: 21.08
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000158
Time: 04:52:31 Log-Likelihood: -105.11
No. Observations: 23 AIC: 214.2
Df Residuals: 21 BIC: 216.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 842.6586 166.264 5.068 0.000 496.894 1188.423
expression -74.3244 16.189 -4.591 0.000 -107.992 -40.656
Omnibus: 0.073 Durbin-Watson: 2.530
Prob(Omnibus): 0.964 Jarque-Bera (JB): 0.169
Skew: 0.110 Prob(JB): 0.919
Kurtosis: 2.642 Cond. No. 338.

CP101

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

F-statistic p-value df difference
5.595 0.036 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.664
Model: OLS Adj. R-squared: 0.572
Method: Least Squares F-statistic: 7.234
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00596
Time: 04:52:31 Log-Likelihood: -67.129
No. Observations: 15 AIC: 142.3
Df Residuals: 11 BIC: 145.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 457.7832 220.037 2.080 0.062 -26.516 942.082
C(dose)[T.1] 719.6847 582.324 1.236 0.242 -562.002 2001.371
expression -37.3244 21.020 -1.776 0.103 -83.589 8.941
expression:C(dose)[T.1] -62.3950 54.860 -1.137 0.280 -183.142 58.352
Omnibus: 0.767 Durbin-Watson: 1.796
Prob(Omnibus): 0.682 Jarque-Bera (JB): 0.692
Skew: -0.434 Prob(JB): 0.708
Kurtosis: 2.405 Cond. No. 1.14e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.624
Model: OLS Adj. R-squared: 0.561
Method: Least Squares F-statistic: 9.960
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00282
Time: 04:52:31 Log-Likelihood: -67.963
No. Observations: 15 AIC: 141.9
Df Residuals: 12 BIC: 144.0
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 553.5839 205.748 2.691 0.020 105.298 1001.870
C(dose)[T.1] 57.5571 13.470 4.273 0.001 28.208 86.907
expression -46.4846 19.652 -2.365 0.036 -89.303 -3.667
Omnibus: 1.089 Durbin-Watson: 1.806
Prob(Omnibus): 0.580 Jarque-Bera (JB): 0.870
Skew: -0.524 Prob(JB): 0.647
Kurtosis: 2.456 Cond. No. 339.

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: 04:52:31 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: 0.052
Model: OLS Adj. R-squared: -0.021
Method: Least Squares F-statistic: 0.7143
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.413
Time: 04:52:31 Log-Likelihood: -74.899
No. Observations: 15 AIC: 153.8
Df Residuals: 13 BIC: 155.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 351.7313 305.505 1.151 0.270 -308.272 1011.735
expression -24.4510 28.931 -0.845 0.413 -86.952 38.050
Omnibus: 2.568 Durbin-Watson: 2.012
Prob(Omnibus): 0.277 Jarque-Bera (JB): 1.246
Skew: 0.334 Prob(JB): 0.536
Kurtosis: 1.756 Cond. No. 329.