LID SPN TRAP

1448830_at

Dusp1 dual specificity phosphatase 1

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
0.020 0.888 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: 11.87
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000132
Time: 03:53:03 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 133.3470 229.868 0.580 0.569 -347.772 614.466
C(dose)[T.1] -95.4687 391.230 -0.244 0.810 -914.322 723.385
expression -8.2090 23.835 -0.344 0.734 -58.097 41.679
expression:C(dose)[T.1] 15.0453 39.150 0.384 0.705 -66.898 96.988
Omnibus: 0.382 Durbin-Watson: 1.923
Prob(Omnibus): 0.826 Jarque-Bera (JB): 0.517
Skew: -0.033 Prob(JB): 0.772
Kurtosis: 2.268 Cond. No. 1.08e+03

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: 18.52
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.81e-05
Time: 03:53:03 Log-Likelihood: -101.05
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 79.5856 178.467 0.446 0.660 -292.690 451.862
C(dose)[T.1] 54.7859 13.436 4.078 0.001 26.760 82.812
expression -2.6324 18.502 -0.142 0.888 -41.226 35.961
Omnibus: 0.287 Durbin-Watson: 1.899
Prob(Omnibus): 0.866 Jarque-Bera (JB): 0.464
Skew: 0.061 Prob(JB): 0.793
Kurtosis: 2.315 Cond. No. 409.

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: 03:53:03 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: 0.358
Model: OLS Adj. R-squared: 0.327
Method: Least Squares F-statistic: 11.71
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00256
Time: 03:53:03 Log-Likelihood: -108.01
No. Observations: 23 AIC: 220.0
Df Residuals: 21 BIC: 222.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -460.4702 157.982 -2.915 0.008 -789.011 -131.930
expression 54.5441 15.941 3.422 0.003 21.393 87.696
Omnibus: 11.434 Durbin-Watson: 2.069
Prob(Omnibus): 0.003 Jarque-Bera (JB): 2.400
Skew: 0.210 Prob(JB): 0.301
Kurtosis: 1.474 Cond. No. 274.

CP101

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

F-statistic p-value df difference
7.717 0.017 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.694
Model: OLS Adj. R-squared: 0.610
Method: Least Squares F-statistic: 8.310
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00361
Time: 03:53:03 Log-Likelihood: -66.423
No. Observations: 15 AIC: 140.8
Df Residuals: 11 BIC: 143.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 1064.6308 501.848 2.121 0.057 -39.929 2169.190
C(dose)[T.1] -518.6463 538.413 -0.963 0.356 -1703.686 666.393
expression -105.0295 52.848 -1.987 0.072 -221.348 11.289
expression:C(dose)[T.1] 58.4320 56.922 1.027 0.327 -66.853 183.717
Omnibus: 4.516 Durbin-Watson: 1.850
Prob(Omnibus): 0.105 Jarque-Bera (JB): 2.296
Skew: -0.930 Prob(JB): 0.317
Kurtosis: 3.460 Cond. No. 1.29e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.665
Model: OLS Adj. R-squared: 0.609
Method: Least Squares F-statistic: 11.88
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00143
Time: 03:53:03 Log-Likelihood: -67.109
No. Observations: 15 AIC: 140.2
Df Residuals: 12 BIC: 142.3
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 586.4182 187.042 3.135 0.009 178.888 993.948
C(dose)[T.1] 33.8753 13.461 2.517 0.027 4.547 63.204
expression -54.6622 19.677 -2.778 0.017 -97.536 -11.789
Omnibus: 7.068 Durbin-Watson: 1.195
Prob(Omnibus): 0.029 Jarque-Bera (JB): 3.940
Skew: -1.172 Prob(JB): 0.139
Kurtosis: 3.900 Cond. No. 289.

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: 03:53:03 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: 0.487
Model: OLS Adj. R-squared: 0.448
Method: Least Squares F-statistic: 12.36
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00379
Time: 03:53:03 Log-Likelihood: -70.287
No. Observations: 15 AIC: 144.6
Df Residuals: 13 BIC: 146.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 794.0924 199.332 3.984 0.002 363.462 1224.723
expression -74.9519 21.316 -3.516 0.004 -121.003 -28.901
Omnibus: 0.458 Durbin-Watson: 1.497
Prob(Omnibus): 0.795 Jarque-Bera (JB): 0.551
Skew: -0.281 Prob(JB): 0.759
Kurtosis: 2.248 Cond. No. 259.