LID SPN TRAP

1440589_at

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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
0.751 0.397 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.663
Model: OLS Adj. R-squared: 0.609
Method: Least Squares F-statistic: 12.44
Date: Thu, 21 Nov 2024 Prob (F-statistic): 9.89e-05
Time: 05:00:22 Log-Likelihood: -100.61
No. Observations: 23 AIC: 209.2
Df Residuals: 19 BIC: 213.8
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 259.4884 267.761 0.969 0.345 -300.941 819.918
C(dose)[T.1] -39.2497 378.082 -0.104 0.918 -830.585 752.085
expression -22.3152 29.100 -0.767 0.453 -83.222 38.591
expression:C(dose)[T.1] 9.5128 42.022 0.226 0.823 -78.439 97.465
Omnibus: 0.130 Durbin-Watson: 2.087
Prob(Omnibus): 0.937 Jarque-Bera (JB): 0.347
Skew: 0.071 Prob(JB): 0.841
Kurtosis: 2.415 Cond. No. 1.01e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.662
Model: OLS Adj. R-squared: 0.628
Method: Least Squares F-statistic: 19.56
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.96e-05
Time: 05:00:22 Log-Likelihood: -100.64
No. Observations: 23 AIC: 207.3
Df Residuals: 20 BIC: 210.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 217.5233 188.577 1.154 0.262 -175.840 610.887
C(dose)[T.1] 46.2964 11.839 3.911 0.001 21.601 70.992
expression -17.7533 20.489 -0.866 0.397 -60.493 24.986
Omnibus: 0.221 Durbin-Watson: 2.061
Prob(Omnibus): 0.895 Jarque-Bera (JB): 0.420
Skew: 0.032 Prob(JB): 0.810
Kurtosis: 2.341 Cond. No. 401.

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: 05:00:22 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: 0.403
Model: OLS Adj. R-squared: 0.375
Method: Least Squares F-statistic: 14.18
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00114
Time: 05:00:22 Log-Likelihood: -107.17
No. Observations: 23 AIC: 218.3
Df Residuals: 21 BIC: 220.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 735.1426 174.127 4.222 0.000 373.027 1097.259
expression -72.7487 19.317 -3.766 0.001 -112.921 -32.576
Omnibus: 8.911 Durbin-Watson: 2.304
Prob(Omnibus): 0.012 Jarque-Bera (JB): 2.588
Skew: 0.405 Prob(JB): 0.274
Kurtosis: 1.570 Cond. No. 285.

CP101

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

F-statistic p-value df difference
1.153 0.304 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.511
Model: OLS Adj. R-squared: 0.377
Method: Least Squares F-statistic: 3.829
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0423
Time: 05:00:22 Log-Likelihood: -69.937
No. Observations: 15 AIC: 147.9
Df Residuals: 11 BIC: 150.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 666.0834 515.101 1.293 0.222 -467.646 1799.812
C(dose)[T.1] -411.4929 835.219 -0.493 0.632 -2249.797 1426.811
expression -63.2805 54.435 -1.162 0.270 -183.092 56.531
expression:C(dose)[T.1] 48.8113 87.843 0.556 0.590 -144.530 242.153
Omnibus: 2.720 Durbin-Watson: 1.212
Prob(Omnibus): 0.257 Jarque-Bera (JB): 1.832
Skew: -0.839 Prob(JB): 0.400
Kurtosis: 2.663 Cond. No. 1.31e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.497
Model: OLS Adj. R-squared: 0.413
Method: Least Squares F-statistic: 5.931
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0162
Time: 05:00:22 Log-Likelihood: -70.145
No. Observations: 15 AIC: 146.3
Df Residuals: 12 BIC: 148.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 488.7570 392.518 1.245 0.237 -366.467 1343.981
C(dose)[T.1] 52.5251 15.350 3.422 0.005 19.080 85.970
expression -44.5363 41.475 -1.074 0.304 -134.902 45.829
Omnibus: 2.915 Durbin-Watson: 1.003
Prob(Omnibus): 0.233 Jarque-Bera (JB): 2.070
Skew: -0.883 Prob(JB): 0.355
Kurtosis: 2.559 Cond. No. 504.

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: 05:00:22 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: 0.006
Model: OLS Adj. R-squared: -0.070
Method: Least Squares F-statistic: 0.08377
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.777
Time: 05:00:22 Log-Likelihood: -75.252
No. Observations: 15 AIC: 154.5
Df Residuals: 13 BIC: 155.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 244.5028 521.242 0.469 0.647 -881.571 1370.577
expression -15.8772 54.856 -0.289 0.777 -134.386 102.632
Omnibus: 1.169 Durbin-Watson: 1.730
Prob(Omnibus): 0.557 Jarque-Bera (JB): 0.774
Skew: 0.120 Prob(JB): 0.679
Kurtosis: 1.914 Cond. No. 494.