LID SPN TRAP

1442905_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
0.173 0.682 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.652
Model: OLS Adj. R-squared: 0.597
Method: Least Squares F-statistic: 11.88
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000131
Time: 04:25:33 Log-Likelihood: -100.96
No. Observations: 23 AIC: 209.9
Df Residuals: 19 BIC: 214.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 13.7841 174.071 0.079 0.938 -350.552 378.120
C(dose)[T.1] 27.4568 259.648 0.106 0.917 -515.993 570.906
expression 4.3721 18.815 0.232 0.819 -35.008 43.752
expression:C(dose)[T.1] 2.5123 27.454 0.092 0.928 -54.949 59.973
Omnibus: 0.403 Durbin-Watson: 1.879
Prob(Omnibus): 0.818 Jarque-Bera (JB): 0.540
Skew: 0.130 Prob(JB): 0.763
Kurtosis: 2.296 Cond. No. 708.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.652
Model: OLS Adj. R-squared: 0.617
Method: Least Squares F-statistic: 18.74
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.60e-05
Time: 04:25:33 Log-Likelihood: -100.96
No. Observations: 23 AIC: 207.9
Df Residuals: 20 BIC: 211.3
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 2.8739 123.650 0.023 0.982 -255.055 260.803
C(dose)[T.1] 51.1985 10.135 5.052 0.000 30.057 72.340
expression 5.5522 13.358 0.416 0.682 -22.311 33.416
Omnibus: 0.370 Durbin-Watson: 1.852
Prob(Omnibus): 0.831 Jarque-Bera (JB): 0.520
Skew: 0.127 Prob(JB): 0.771
Kurtosis: 2.308 Cond. No. 271.

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: 04:25:33 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: 0.208
Model: OLS Adj. R-squared: 0.170
Method: Least Squares F-statistic: 5.520
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0287
Time: 04:25:33 Log-Likelihood: -110.42
No. Observations: 23 AIC: 224.8
Df Residuals: 21 BIC: 227.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -295.6572 159.905 -1.849 0.079 -628.197 36.883
expression 39.8061 16.943 2.349 0.029 4.571 75.041
Omnibus: 1.603 Durbin-Watson: 2.366
Prob(Omnibus): 0.449 Jarque-Bera (JB): 1.243
Skew: 0.365 Prob(JB): 0.537
Kurtosis: 2.125 Cond. No. 238.

CP101

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

F-statistic p-value df difference
4.345 0.059 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.596
Model: OLS Adj. R-squared: 0.486
Method: Least Squares F-statistic: 5.410
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0156
Time: 04:25:33 Log-Likelihood: -68.502
No. Observations: 15 AIC: 145.0
Df Residuals: 11 BIC: 147.8
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 356.3069 343.862 1.036 0.322 -400.529 1113.142
C(dose)[T.1] 108.5865 393.762 0.276 0.788 -758.077 975.250
expression -34.6424 41.218 -0.840 0.419 -125.362 56.077
expression:C(dose)[T.1] -6.6344 47.060 -0.141 0.890 -110.212 96.943
Omnibus: 0.251 Durbin-Watson: 0.927
Prob(Omnibus): 0.882 Jarque-Bera (JB): 0.022
Skew: -0.042 Prob(JB): 0.989
Kurtosis: 2.830 Cond. No. 715.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.595
Model: OLS Adj. R-squared: 0.528
Method: Least Squares F-statistic: 8.826
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00439
Time: 04:25:33 Log-Likelihood: -68.515
No. Observations: 15 AIC: 143.0
Df Residuals: 12 BIC: 145.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 398.7470 159.251 2.504 0.028 51.769 745.725
C(dose)[T.1] 53.1107 13.616 3.900 0.002 23.443 82.778
expression -39.7318 19.061 -2.084 0.059 -81.262 1.798
Omnibus: 0.222 Durbin-Watson: 0.918
Prob(Omnibus): 0.895 Jarque-Bera (JB): 0.025
Skew: -0.031 Prob(JB): 0.987
Kurtosis: 2.808 Cond. No. 202.

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: 04:25:33 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: 0.082
Model: OLS Adj. R-squared: 0.012
Method: Least Squares F-statistic: 1.165
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.300
Time: 04:25:33 Log-Likelihood: -74.657
No. Observations: 15 AIC: 153.3
Df Residuals: 13 BIC: 154.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 341.0342 229.415 1.487 0.161 -154.587 836.656
expression -29.4786 27.315 -1.079 0.300 -88.488 29.531
Omnibus: 0.539 Durbin-Watson: 1.774
Prob(Omnibus): 0.764 Jarque-Bera (JB): 0.579
Skew: 0.173 Prob(JB): 0.748
Kurtosis: 2.102 Cond. No. 201.