LID SPN TRAP

1460728_s_at

Ing4 inhibitor of growth family, member 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
0.273 0.607 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.654
Model: OLS Adj. R-squared: 0.599
Method: Least Squares F-statistic: 11.98
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000125
Time: 04:56:27 Log-Likelihood: -100.90
No. Observations: 23 AIC: 209.8
Df Residuals: 19 BIC: 214.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 25.3831 119.453 0.212 0.834 -224.635 275.401
C(dose)[T.1] 34.6994 156.817 0.221 0.827 -293.521 362.920
expression 4.9959 20.675 0.242 0.812 -38.278 48.270
expression:C(dose)[T.1] 3.6740 27.758 0.132 0.896 -54.424 61.772
Omnibus: 0.557 Durbin-Watson: 1.984
Prob(Omnibus): 0.757 Jarque-Bera (JB): 0.645
Skew: 0.203 Prob(JB): 0.724
Kurtosis: 2.288 Cond. No. 272.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.654
Model: OLS Adj. R-squared: 0.619
Method: Least Squares F-statistic: 18.88
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.47e-05
Time: 04:56:28 Log-Likelihood: -100.91
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 13.6225 77.851 0.175 0.863 -148.771 176.016
C(dose)[T.1] 55.4146 9.574 5.788 0.000 35.444 75.385
expression 7.0342 13.452 0.523 0.607 -21.027 35.095
Omnibus: 0.540 Durbin-Watson: 1.998
Prob(Omnibus): 0.763 Jarque-Bera (JB): 0.633
Skew: 0.195 Prob(JB): 0.729
Kurtosis: 2.287 Cond. No. 104.

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: 04:56:28 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: 0.074
Model: OLS Adj. R-squared: 0.030
Method: Least Squares F-statistic: 1.674
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.210
Time: 04:56:28 Log-Likelihood: -112.22
No. Observations: 23 AIC: 228.4
Df Residuals: 21 BIC: 230.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 221.9963 110.179 2.015 0.057 -7.133 451.126
expression -25.2780 19.536 -1.294 0.210 -65.905 15.349
Omnibus: 1.091 Durbin-Watson: 2.263
Prob(Omnibus): 0.580 Jarque-Bera (JB): 0.888
Skew: 0.208 Prob(JB): 0.641
Kurtosis: 2.132 Cond. No. 92.3

CP101

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

F-statistic p-value df difference
0.006 0.939 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.450
Model: OLS Adj. R-squared: 0.300
Method: Least Squares F-statistic: 3.000
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0769
Time: 04:56:28 Log-Likelihood: -70.817
No. Observations: 15 AIC: 149.6
Df Residuals: 11 BIC: 152.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 69.0145 218.552 0.316 0.758 -412.015 550.044
C(dose)[T.1] -4.2868 401.299 -0.011 0.992 -887.539 878.966
expression -0.2489 34.251 -0.007 0.994 -75.636 75.138
expression:C(dose)[T.1] 8.7655 64.964 0.135 0.895 -134.219 151.750
Omnibus: 2.419 Durbin-Watson: 0.842
Prob(Omnibus): 0.298 Jarque-Bera (JB): 1.655
Skew: -0.791 Prob(JB): 0.437
Kurtosis: 2.620 Cond. No. 379.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.449
Model: OLS Adj. R-squared: 0.357
Method: Least Squares F-statistic: 4.890
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0280
Time: 04:56:28 Log-Likelihood: -70.829
No. Observations: 15 AIC: 147.7
Df Residuals: 12 BIC: 149.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 53.4902 178.051 0.300 0.769 -334.450 441.431
C(dose)[T.1] 49.8035 17.536 2.840 0.015 11.597 88.010
expression 2.1877 27.888 0.078 0.939 -58.575 62.951
Omnibus: 2.757 Durbin-Watson: 0.831
Prob(Omnibus): 0.252 Jarque-Bera (JB): 1.890
Skew: -0.850 Prob(JB): 0.389
Kurtosis: 2.631 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: 04:56:28 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: 0.079
Model: OLS Adj. R-squared: 0.008
Method: Least Squares F-statistic: 1.111
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.311
Time: 04:56:28 Log-Likelihood: -74.685
No. Observations: 15 AIC: 153.4
Df Residuals: 13 BIC: 154.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 297.5787 193.737 1.536 0.149 -120.964 716.121
expression -32.7664 31.092 -1.054 0.311 -99.936 34.404
Omnibus: 3.724 Durbin-Watson: 1.345
Prob(Omnibus): 0.155 Jarque-Bera (JB): 1.239
Skew: 0.073 Prob(JB): 0.538
Kurtosis: 1.599 Cond. No. 127.