LID SPN TRAP

1448554_s_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
4.473 0.047 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.753
Model: OLS Adj. R-squared: 0.715
Method: Least Squares F-statistic: 19.35
Date: Thu, 21 Nov 2024 Prob (F-statistic): 5.32e-06
Time: 05:00:26 Log-Likelihood: -97.003
No. Observations: 23 AIC: 202.0
Df Residuals: 19 BIC: 206.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 59.9585 232.289 0.258 0.799 -426.228 546.145
C(dose)[T.1] 575.3029 297.669 1.933 0.068 -47.726 1198.331
expression -0.6285 25.383 -0.025 0.981 -53.756 52.499
expression:C(dose)[T.1] -57.4284 32.608 -1.761 0.094 -125.679 10.822
Omnibus: 0.189 Durbin-Watson: 1.928
Prob(Omnibus): 0.910 Jarque-Bera (JB): 0.250
Skew: 0.182 Prob(JB): 0.882
Kurtosis: 2.641 Cond. No. 992.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.713
Model: OLS Adj. R-squared: 0.685
Method: Least Squares F-statistic: 24.87
Date: Thu, 21 Nov 2024 Prob (F-statistic): 3.77e-06
Time: 05:00:26 Log-Likelihood: -98.742
No. Observations: 23 AIC: 203.5
Df Residuals: 20 BIC: 206.9
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 378.3277 153.347 2.467 0.023 58.451 698.205
C(dose)[T.1] 51.2325 7.990 6.412 0.000 34.565 67.900
expression -35.4266 16.750 -2.115 0.047 -70.367 -0.486
Omnibus: 0.584 Durbin-Watson: 2.224
Prob(Omnibus): 0.747 Jarque-Bera (JB): 0.184
Skew: 0.219 Prob(JB): 0.912
Kurtosis: 2.993 Cond. No. 358.

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: 05:00:26 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: 0.124
Model: OLS Adj. R-squared: 0.082
Method: Least Squares F-statistic: 2.963
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0999
Time: 05:00:26 Log-Likelihood: -111.59
No. Observations: 23 AIC: 227.2
Df Residuals: 21 BIC: 229.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 524.8267 258.677 2.029 0.055 -13.122 1062.775
expression -48.8025 28.352 -1.721 0.100 -107.764 10.159
Omnibus: 4.953 Durbin-Watson: 2.722
Prob(Omnibus): 0.084 Jarque-Bera (JB): 1.606
Skew: -0.048 Prob(JB): 0.448
Kurtosis: 1.709 Cond. No. 354.

CP101

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

F-statistic p-value df difference
0.000 0.990 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.467
Model: OLS Adj. R-squared: 0.321
Method: Least Squares F-statistic: 3.208
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0658
Time: 05:00:26 Log-Likelihood: -70.586
No. Observations: 15 AIC: 149.2
Df Residuals: 11 BIC: 152.0
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -32.5232 244.741 -0.133 0.897 -571.194 506.148
C(dose)[T.1] 266.1363 357.967 0.743 0.473 -521.744 1054.017
expression 11.8650 29.019 0.409 0.690 -52.004 75.734
expression:C(dose)[T.1] -25.5714 42.156 -0.607 0.556 -118.357 67.214
Omnibus: 2.268 Durbin-Watson: 0.729
Prob(Omnibus): 0.322 Jarque-Bera (JB): 1.739
Skew: -0.761 Prob(JB): 0.419
Kurtosis: 2.317 Cond. No. 502.

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: 4.885
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0281
Time: 05:00:26 Log-Likelihood: -70.833
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 69.5491 172.970 0.402 0.695 -307.321 446.419
C(dose)[T.1] 49.2244 15.903 3.095 0.009 14.574 83.875
expression -0.2517 20.487 -0.012 0.990 -44.890 44.386
Omnibus: 2.722 Durbin-Watson: 0.809
Prob(Omnibus): 0.256 Jarque-Bera (JB): 1.879
Skew: -0.845 Prob(JB): 0.391
Kurtosis: 2.614 Cond. No. 190.

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: 05:00:26 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: 0.009
Model: OLS Adj. R-squared: -0.068
Method: Least Squares F-statistic: 0.1142
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.741
Time: 05:00:26 Log-Likelihood: -75.234
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 18.7680 221.854 0.085 0.934 -460.517 498.053
expression 8.8289 26.124 0.338 0.741 -47.609 65.267
Omnibus: 0.659 Durbin-Watson: 1.619
Prob(Omnibus): 0.719 Jarque-Bera (JB): 0.614
Skew: 0.122 Prob(JB): 0.736
Kurtosis: 2.039 Cond. No. 189.