LID SPN TRAP

1437406_x_at

Igfbp4 insulin-like growth factor binding protein 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
2.795 0.110 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.698
Model: OLS Adj. R-squared: 0.650
Method: Least Squares F-statistic: 14.64
Date: Thu, 21 Nov 2024 Prob (F-statistic): 3.53e-05
Time: 05:16:43 Log-Likelihood: -99.332
No. Observations: 23 AIC: 206.7
Df Residuals: 19 BIC: 211.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 179.2058 86.847 2.063 0.053 -2.568 360.979
C(dose)[T.1] -19.1864 101.569 -0.189 0.852 -231.773 193.401
expression -16.8736 11.698 -1.442 0.165 -41.357 7.610
expression:C(dose)[T.1] 8.7499 14.228 0.615 0.546 -21.031 38.530
Omnibus: 0.026 Durbin-Watson: 2.145
Prob(Omnibus): 0.987 Jarque-Bera (JB): 0.087
Skew: 0.009 Prob(JB): 0.958
Kurtosis: 2.700 Cond. No. 243.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.692
Model: OLS Adj. R-squared: 0.661
Method: Least Squares F-statistic: 22.48
Date: Thu, 21 Nov 2024 Prob (F-statistic): 7.66e-06
Time: 05:16:43 Log-Likelihood: -99.558
No. Observations: 23 AIC: 205.1
Df Residuals: 20 BIC: 208.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 135.3946 48.890 2.769 0.012 33.412 237.377
C(dose)[T.1] 42.9419 10.302 4.168 0.000 21.452 64.432
expression -10.9595 6.555 -1.672 0.110 -24.633 2.714
Omnibus: 0.058 Durbin-Watson: 2.178
Prob(Omnibus): 0.971 Jarque-Bera (JB): 0.088
Skew: -0.061 Prob(JB): 0.957
Kurtosis: 2.722 Cond. No. 86.0

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: 05:16:43 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: 0.425
Model: OLS Adj. R-squared: 0.397
Method: Least Squares F-statistic: 15.50
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000756
Time: 05:16:43 Log-Likelihood: -106.75
No. Observations: 23 AIC: 217.5
Df Residuals: 21 BIC: 219.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 270.6043 48.798 5.545 0.000 169.124 372.084
expression -27.4490 6.973 -3.937 0.001 -41.950 -12.949
Omnibus: 3.439 Durbin-Watson: 2.590
Prob(Omnibus): 0.179 Jarque-Bera (JB): 1.931
Skew: 0.454 Prob(JB): 0.381
Kurtosis: 1.908 Cond. No. 63.6

CP101

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

F-statistic p-value df difference
0.483 0.500 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.527
Model: OLS Adj. R-squared: 0.398
Method: Least Squares F-statistic: 4.085
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0355
Time: 05:16:43 Log-Likelihood: -69.685
No. Observations: 15 AIC: 147.4
Df Residuals: 11 BIC: 150.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 396.1337 244.079 1.623 0.133 -141.081 933.348
C(dose)[T.1] -273.8942 276.669 -0.990 0.343 -882.839 335.051
expression -47.5896 35.301 -1.348 0.205 -125.286 30.107
expression:C(dose)[T.1] 46.7214 40.612 1.150 0.274 -42.665 136.107
Omnibus: 2.608 Durbin-Watson: 1.199
Prob(Omnibus): 0.271 Jarque-Bera (JB): 1.474
Skew: -0.767 Prob(JB): 0.479
Kurtosis: 2.925 Cond. No. 369.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.470
Model: OLS Adj. R-squared: 0.382
Method: Least Squares F-statistic: 5.323
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0221
Time: 05:16:43 Log-Likelihood: -70.537
No. Observations: 15 AIC: 147.1
Df Residuals: 12 BIC: 149.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 152.3101 122.684 1.241 0.238 -114.996 419.616
C(dose)[T.1] 43.7922 17.282 2.534 0.026 6.139 81.446
expression -12.2891 17.687 -0.695 0.500 -50.826 26.248
Omnibus: 3.936 Durbin-Watson: 0.825
Prob(Omnibus): 0.140 Jarque-Bera (JB): 2.273
Skew: -0.953 Prob(JB): 0.321
Kurtosis: 3.083 Cond. No. 109.

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: 05:16:43 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: 0.187
Model: OLS Adj. R-squared: 0.124
Method: Least Squares F-statistic: 2.981
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.108
Time: 05:16:43 Log-Likelihood: -73.752
No. Observations: 15 AIC: 151.5
Df Residuals: 13 BIC: 152.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 310.2659 125.787 2.467 0.028 38.520 582.012
expression -32.4613 18.801 -1.727 0.108 -73.079 8.157
Omnibus: 0.816 Durbin-Watson: 1.301
Prob(Omnibus): 0.665 Jarque-Bera (JB): 0.696
Skew: 0.198 Prob(JB): 0.706
Kurtosis: 2.022 Cond. No. 93.9