LID SPN TRAP

1459661_at

symbol_not_found name_not_found

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
0.000 0.988 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.675
Model: OLS Adj. R-squared: 0.624
Method: Least Squares F-statistic: 13.15
Date: Thu, 21 Nov 2024 Prob (F-statistic): 7.01e-05
Time: 05:14:05 Log-Likelihood: -100.18
No. Observations: 23 AIC: 208.4
Df Residuals: 19 BIC: 212.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 37.1472 35.239 1.054 0.305 -36.609 110.904
C(dose)[T.1] 150.6908 79.681 1.891 0.074 -16.084 317.465
expression 5.5420 11.280 0.491 0.629 -18.068 29.152
expression:C(dose)[T.1] -33.7646 27.448 -1.230 0.234 -91.215 23.686
Omnibus: 0.330 Durbin-Watson: 1.720
Prob(Omnibus): 0.848 Jarque-Bera (JB): 0.494
Skew: 0.114 Prob(JB): 0.781
Kurtosis: 2.319 Cond. No. 70.0

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.649
Model: OLS Adj. R-squared: 0.614
Method: Least Squares F-statistic: 18.49
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.83e-05
Time: 05:14:05 Log-Likelihood: -101.06
No. Observations: 23 AIC: 208.1
Df Residuals: 20 BIC: 211.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 54.7025 32.631 1.676 0.109 -13.364 122.769
C(dose)[T.1] 53.2996 9.101 5.857 0.000 34.316 72.284
expression -0.1605 10.415 -0.015 0.988 -21.885 21.564
Omnibus: 0.322 Durbin-Watson: 1.885
Prob(Omnibus): 0.851 Jarque-Bera (JB): 0.485
Skew: 0.062 Prob(JB): 0.784
Kurtosis: 2.299 Cond. No. 25.1

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: 05:14:05 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: 0.047
Model: OLS Adj. R-squared: 0.002
Method: Least Squares F-statistic: 1.041
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.319
Time: 05:14:05 Log-Likelihood: -112.55
No. Observations: 23 AIC: 229.1
Df Residuals: 21 BIC: 231.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 128.5576 48.394 2.656 0.015 27.917 229.198
expression -16.4621 16.138 -1.020 0.319 -50.023 17.098
Omnibus: 3.344 Durbin-Watson: 2.408
Prob(Omnibus): 0.188 Jarque-Bera (JB): 1.423
Skew: 0.167 Prob(JB): 0.491
Kurtosis: 1.828 Cond. No. 22.8

CP101

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

F-statistic p-value df difference
0.000 0.997 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.449
Model: OLS Adj. R-squared: 0.298
Method: Least Squares F-statistic: 2.986
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0777
Time: 05:14:05 Log-Likelihood: -70.833
No. Observations: 15 AIC: 149.7
Df Residuals: 11 BIC: 152.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 66.4245 77.981 0.852 0.412 -105.210 238.059
C(dose)[T.1] 52.6303 131.435 0.400 0.697 -236.655 341.916
expression 0.3159 24.239 0.013 0.990 -53.035 53.667
expression:C(dose)[T.1] -1.0727 40.759 -0.026 0.979 -90.782 88.637
Omnibus: 2.786 Durbin-Watson: 0.805
Prob(Omnibus): 0.248 Jarque-Bera (JB): 1.897
Skew: -0.853 Prob(JB): 0.387
Kurtosis: 2.645 Cond. No. 70.6

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: 05:14:05 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 67.6304 60.413 1.119 0.285 -63.998 199.259
C(dose)[T.1] 49.1985 15.751 3.124 0.009 14.880 83.517
expression -0.0635 18.658 -0.003 0.997 -40.716 40.589
Omnibus: 2.720 Durbin-Watson: 0.811
Prob(Omnibus): 0.257 Jarque-Bera (JB): 1.871
Skew: -0.844 Prob(JB): 0.392
Kurtosis: 2.622 Cond. No. 27.5

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: 05:14:05 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: 0.001
Model: OLS Adj. R-squared: -0.076
Method: Least Squares F-statistic: 0.007846
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.931
Time: 05:14:05 Log-Likelihood: -75.296
No. Observations: 15 AIC: 154.6
Df Residuals: 13 BIC: 156.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 86.8390 77.748 1.117 0.284 -81.126 254.804
expression 2.1365 24.120 0.089 0.931 -49.972 54.245
Omnibus: 0.595 Durbin-Watson: 1.606
Prob(Omnibus): 0.743 Jarque-Bera (JB): 0.579
Skew: 0.057 Prob(JB): 0.749
Kurtosis: 2.044 Cond. No. 27.0