LID SPN TRAP

1431422_a_at

Dusp14 dual specificity phosphatase 14

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
0.426 0.521 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.664
Model: OLS Adj. R-squared: 0.611
Method: Least Squares F-statistic: 12.54
Date: Thu, 21 Nov 2024 Prob (F-statistic): 9.44e-05
Time: 04:17:25 Log-Likelihood: -100.55
No. Observations: 23 AIC: 209.1
Df Residuals: 19 BIC: 213.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 138.6042 251.275 0.552 0.588 -387.320 664.528
C(dose)[T.1] 509.8241 672.275 0.758 0.458 -897.263 1916.911
expression -7.5342 22.425 -0.336 0.741 -54.471 39.402
expression:C(dose)[T.1] -39.1661 58.319 -0.672 0.510 -161.229 82.897
Omnibus: 1.723 Durbin-Watson: 1.949
Prob(Omnibus): 0.423 Jarque-Bera (JB): 1.147
Skew: 0.259 Prob(JB): 0.564
Kurtosis: 2.037 Cond. No. 2.03e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.656
Model: OLS Adj. R-squared: 0.622
Method: Least Squares F-statistic: 19.10
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.30e-05
Time: 04:17:25 Log-Likelihood: -100.82
No. Observations: 23 AIC: 207.6
Df Residuals: 20 BIC: 211.0
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 203.4750 228.761 0.889 0.384 -273.712 680.662
C(dose)[T.1] 58.4046 11.644 5.016 0.000 34.116 82.693
expression -13.3253 20.415 -0.653 0.521 -55.910 29.260
Omnibus: 1.217 Durbin-Watson: 1.980
Prob(Omnibus): 0.544 Jarque-Bera (JB): 0.857
Skew: 0.066 Prob(JB): 0.652
Kurtosis: 2.064 Cond. No. 607.

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: 04:17:25 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: 0.224
Model: OLS Adj. R-squared: 0.187
Method: Least Squares F-statistic: 6.066
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0225
Time: 04:17:25 Log-Likelihood: -110.19
No. Observations: 23 AIC: 224.4
Df Residuals: 21 BIC: 226.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -545.8111 254.067 -2.148 0.044 -1074.172 -17.450
expression 54.9501 22.312 2.463 0.023 8.550 101.350
Omnibus: 2.697 Durbin-Watson: 2.139
Prob(Omnibus): 0.260 Jarque-Bera (JB): 1.230
Skew: 0.064 Prob(JB): 0.541
Kurtosis: 1.874 Cond. No. 459.

CP101

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

F-statistic p-value df difference
0.482 0.501 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.540
Model: OLS Adj. R-squared: 0.415
Method: Least Squares F-statistic: 4.312
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0306
Time: 04:17:25 Log-Likelihood: -69.469
No. Observations: 15 AIC: 146.9
Df Residuals: 11 BIC: 149.8
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -263.6308 581.629 -0.453 0.659 -1543.787 1016.525
C(dose)[T.1] 1023.8656 748.284 1.368 0.199 -623.097 2670.829
expression 35.6313 62.588 0.569 0.581 -102.125 173.387
expression:C(dose)[T.1] -103.9527 80.085 -1.298 0.221 -280.219 72.314
Omnibus: 0.740 Durbin-Watson: 1.200
Prob(Omnibus): 0.691 Jarque-Bera (JB): 0.571
Skew: -0.424 Prob(JB): 0.752
Kurtosis: 2.559 Cond. No. 1.32e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.470
Model: OLS Adj. R-squared: 0.382
Method: Least Squares F-statistic: 5.322
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0222
Time: 04:17:26 Log-Likelihood: -70.538
No. Observations: 15 AIC: 147.1
Df Residuals: 12 BIC: 149.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 326.2876 373.181 0.874 0.399 -486.803 1139.379
C(dose)[T.1] 52.7920 16.280 3.243 0.007 17.322 88.262
expression -27.8605 40.146 -0.694 0.501 -115.332 59.611
Omnibus: 2.909 Durbin-Watson: 1.008
Prob(Omnibus): 0.234 Jarque-Bera (JB): 1.949
Skew: -0.869 Prob(JB): 0.377
Kurtosis: 2.685 Cond. No. 460.

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: 04:17:26 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: 0.006
Model: OLS Adj. R-squared: -0.071
Method: Least Squares F-statistic: 0.07344
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.791
Time: 04:17:26 Log-Likelihood: -75.258
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 -33.3876 468.934 -0.071 0.944 -1046.458 979.683
expression 13.5740 50.088 0.271 0.791 -94.634 121.782
Omnibus: 0.945 Durbin-Watson: 1.514
Prob(Omnibus): 0.623 Jarque-Bera (JB): 0.708
Skew: 0.117 Prob(JB): 0.702
Kurtosis: 1.962 Cond. No. 438.