LID SPN TRAP

1443888_at

AU023762 expressed sequence AU023762

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
7.033 0.015 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.743
Model: OLS Adj. R-squared: 0.703
Method: Least Squares F-statistic: 18.32
Date: Thu, 03 Apr 2025 Prob (F-statistic): 7.81e-06
Time: 22:49:22 Log-Likelihood: -97.474
No. Observations: 23 AIC: 202.9
Df Residuals: 19 BIC: 207.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 168.1404 57.806 2.909 0.009 47.150 289.130
C(dose)[T.1] 26.0680 76.432 0.341 0.737 -133.906 186.042
expression -19.9615 10.085 -1.979 0.062 -41.070 1.147
expression:C(dose)[T.1] 5.8593 12.926 0.453 0.655 -21.196 32.915
Omnibus: 2.641 Durbin-Watson: 1.917
Prob(Omnibus): 0.267 Jarque-Bera (JB): 1.276
Skew: -0.160 Prob(JB): 0.528
Kurtosis: 1.892 Cond. No. 166.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.740
Model: OLS Adj. R-squared: 0.714
Method: Least Squares F-statistic: 28.51
Date: Thu, 03 Apr 2025 Prob (F-statistic): 1.39e-06
Time: 22:49:22 Log-Likelihood: -97.597
No. Observations: 23 AIC: 201.2
Df Residuals: 20 BIC: 204.6
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 147.7847 35.669 4.143 0.001 73.381 222.188
C(dose)[T.1] 60.5142 8.014 7.551 0.000 43.797 77.231
expression -16.3951 6.182 -2.652 0.015 -29.291 -3.499
Omnibus: 3.817 Durbin-Watson: 1.823
Prob(Omnibus): 0.148 Jarque-Bera (JB): 1.548
Skew: -0.205 Prob(JB): 0.461
Kurtosis: 1.797 Cond. No. 58.2

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, 03 Apr 2025 Prob (F-statistic): 3.51e-06
Time: 22:49:22 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: 0.000
Model: OLS Adj. R-squared: -0.047
Method: Least Squares F-statistic: 0.003206
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.955
Time: 22:49:22 Log-Likelihood: -113.10
No. Observations: 23 AIC: 230.2
Df Residuals: 21 BIC: 232.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 83.4511 66.331 1.258 0.222 -54.492 221.394
expression -0.6310 11.144 -0.057 0.955 -23.806 22.544
Omnibus: 3.356 Durbin-Watson: 2.497
Prob(Omnibus): 0.187 Jarque-Bera (JB): 1.577
Skew: 0.288 Prob(JB): 0.454
Kurtosis: 1.853 Cond. No. 56.2

CP101

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

F-statistic p-value df difference
2.108 0.172 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.543
Model: OLS Adj. R-squared: 0.419
Method: Least Squares F-statistic: 4.364
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0296
Time: 22:49:22 Log-Likelihood: -69.420
No. Observations: 15 AIC: 146.8
Df Residuals: 11 BIC: 149.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 9.0347 83.052 0.109 0.915 -173.762 191.831
C(dose)[T.1] -12.0892 122.722 -0.099 0.923 -282.199 258.021
expression 10.1165 14.263 0.709 0.493 -21.277 41.510
expression:C(dose)[T.1] 11.8619 21.800 0.544 0.597 -36.119 59.843
Omnibus: 0.931 Durbin-Watson: 0.780
Prob(Omnibus): 0.628 Jarque-Bera (JB): 0.847
Skew: -0.451 Prob(JB): 0.655
Kurtosis: 2.263 Cond. No. 123.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.531
Model: OLS Adj. R-squared: 0.453
Method: Least Squares F-statistic: 6.797
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0106
Time: 22:49:22 Log-Likelihood: -69.619
No. Observations: 15 AIC: 145.2
Df Residuals: 12 BIC: 147.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -20.2768 61.331 -0.331 0.747 -153.905 113.351
C(dose)[T.1] 54.1623 14.914 3.632 0.003 21.668 86.656
expression 15.1946 10.465 1.452 0.172 -7.607 37.997
Omnibus: 1.545 Durbin-Watson: 0.652
Prob(Omnibus): 0.462 Jarque-Bera (JB): 1.252
Skew: -0.589 Prob(JB): 0.535
Kurtosis: 2.216 Cond. No. 49.6

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, 03 Apr 2025 Prob (F-statistic): 0.00629
Time: 22:49:22 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: 0.016
Model: OLS Adj. R-squared: -0.060
Method: Least Squares F-statistic: 0.2087
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.655
Time: 22:49:23 Log-Likelihood: -75.181
No. Observations: 15 AIC: 154.4
Df Residuals: 13 BIC: 155.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 57.4035 80.012 0.717 0.486 -115.451 230.258
expression 6.4781 14.179 0.457 0.655 -24.155 37.111
Omnibus: 1.243 Durbin-Watson: 1.698
Prob(Omnibus): 0.537 Jarque-Bera (JB): 0.785
Skew: 0.090 Prob(JB): 0.675
Kurtosis: 1.894 Cond. No. 46.2