LID SPN TRAP

1425608_at

Dusp3 dual specificity phosphatase 3 (vaccinia virus phosphatase VH1-related)

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
0.812 0.378 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.674
Model: OLS Adj. R-squared: 0.623
Method: Least Squares F-statistic: 13.11
Date: Thu, 21 Nov 2024 Prob (F-statistic): 7.14e-05
Time: 04:28:37 Log-Likelihood: -100.20
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 31.4925 92.455 0.341 0.737 -162.018 225.003
C(dose)[T.1] -76.4755 158.290 -0.483 0.635 -407.781 254.830
expression 2.9389 11.936 0.246 0.808 -22.044 27.922
expression:C(dose)[T.1] 16.7837 20.441 0.821 0.422 -25.999 59.567
Omnibus: 0.506 Durbin-Watson: 1.857
Prob(Omnibus): 0.777 Jarque-Bera (JB): 0.411
Skew: -0.294 Prob(JB): 0.814
Kurtosis: 2.711 Cond. No. 349.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.663
Model: OLS Adj. R-squared: 0.629
Method: Least Squares F-statistic: 19.65
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.90e-05
Time: 04:28:37 Log-Likelihood: -100.60
No. Observations: 23 AIC: 207.2
Df Residuals: 20 BIC: 210.6
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -12.7436 74.521 -0.171 0.866 -168.192 142.705
C(dose)[T.1] 53.2995 8.597 6.200 0.000 35.366 71.233
expression 8.6620 9.611 0.901 0.378 -11.385 28.709
Omnibus: 0.268 Durbin-Watson: 1.873
Prob(Omnibus): 0.875 Jarque-Bera (JB): 0.429
Skew: -0.192 Prob(JB): 0.807
Kurtosis: 2.452 Cond. No. 137.

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: 04:28:37 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: 0.015
Model: OLS Adj. R-squared: -0.032
Method: Least Squares F-statistic: 0.3117
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.583
Time: 04:28:37 Log-Likelihood: -112.94
No. Observations: 23 AIC: 229.9
Df Residuals: 21 BIC: 232.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 10.5133 124.154 0.085 0.933 -247.679 268.706
expression 8.9510 16.032 0.558 0.583 -24.389 42.291
Omnibus: 3.431 Durbin-Watson: 2.481
Prob(Omnibus): 0.180 Jarque-Bera (JB): 1.462
Skew: 0.191 Prob(JB): 0.481
Kurtosis: 1.825 Cond. No. 136.

CP101

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

F-statistic p-value df difference
7.535 0.018 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.667
Model: OLS Adj. R-squared: 0.576
Method: Least Squares F-statistic: 7.344
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00565
Time: 04:28:37 Log-Likelihood: -67.053
No. Observations: 15 AIC: 142.1
Df Residuals: 11 BIC: 144.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -197.5400 149.889 -1.318 0.214 -527.443 132.363
C(dose)[T.1] -45.0043 232.871 -0.193 0.850 -557.550 467.541
expression 34.6655 19.572 1.771 0.104 -8.412 77.743
expression:C(dose)[T.1] 13.2386 30.769 0.430 0.675 -54.483 80.960
Omnibus: 2.876 Durbin-Watson: 1.048
Prob(Omnibus): 0.237 Jarque-Bera (JB): 1.378
Skew: -0.736 Prob(JB): 0.502
Kurtosis: 3.188 Cond. No. 359.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.661
Model: OLS Adj. R-squared: 0.605
Method: Least Squares F-statistic: 11.72
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00151
Time: 04:28:37 Log-Likelihood: -67.178
No. Observations: 15 AIC: 140.4
Df Residuals: 12 BIC: 142.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -238.4831 111.807 -2.133 0.054 -482.090 5.124
C(dose)[T.1] 55.0358 12.518 4.396 0.001 27.761 82.311
expression 40.0220 14.580 2.745 0.018 8.255 71.789
Omnibus: 1.391 Durbin-Watson: 1.145
Prob(Omnibus): 0.499 Jarque-Bera (JB): 0.913
Skew: -0.579 Prob(JB): 0.633
Kurtosis: 2.654 Cond. No. 140.

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: 04:28:37 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: 0.116
Model: OLS Adj. R-squared: 0.048
Method: Least Squares F-statistic: 1.706
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.214
Time: 04:28:37 Log-Likelihood: -74.375
No. Observations: 15 AIC: 152.8
Df Residuals: 13 BIC: 154.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -126.7177 169.022 -0.750 0.467 -491.868 238.432
expression 29.1291 22.305 1.306 0.214 -19.057 77.315
Omnibus: 2.940 Durbin-Watson: 2.010
Prob(Omnibus): 0.230 Jarque-Bera (JB): 1.313
Skew: 0.333 Prob(JB): 0.519
Kurtosis: 1.712 Cond. No. 136.