LID SPN TRAP

1460288_a_at

Ppp4c protein phosphatase 4, catalytic subunit

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
0.321 0.578 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.688
Model: OLS Adj. R-squared: 0.639
Method: Least Squares F-statistic: 13.99
Date: Thu, 21 Nov 2024 Prob (F-statistic): 4.73e-05
Time: 03:39:38 Log-Likelihood: -99.695
No. Observations: 23 AIC: 207.4
Df Residuals: 19 BIC: 211.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -212.2212 261.952 -0.810 0.428 -760.493 336.051
C(dose)[T.1] 478.8749 295.313 1.622 0.121 -139.223 1096.973
expression 31.3526 30.818 1.017 0.322 -33.150 95.855
expression:C(dose)[T.1] -49.6297 34.563 -1.436 0.167 -121.970 22.711
Omnibus: 0.481 Durbin-Watson: 2.015
Prob(Omnibus): 0.786 Jarque-Bera (JB): 0.599
Skew: 0.244 Prob(JB): 0.741
Kurtosis: 2.378 Cond. No. 900.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.655
Model: OLS Adj. R-squared: 0.620
Method: Least Squares F-statistic: 18.95
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.42e-05
Time: 03:39:38 Log-Likelihood: -100.88
No. Observations: 23 AIC: 207.8
Df Residuals: 20 BIC: 211.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 123.0881 121.814 1.010 0.324 -131.012 377.189
C(dose)[T.1] 55.0192 9.194 5.984 0.000 35.841 74.197
expression -8.1056 14.317 -0.566 0.578 -37.971 21.760
Omnibus: 0.315 Durbin-Watson: 1.942
Prob(Omnibus): 0.854 Jarque-Bera (JB): 0.484
Skew: 0.115 Prob(JB): 0.785
Kurtosis: 2.327 Cond. No. 245.

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: 03:39:38 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: 0.036
Model: OLS Adj. R-squared: -0.010
Method: Least Squares F-statistic: 0.7860
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.385
Time: 03:39:38 Log-Likelihood: -112.68
No. Observations: 23 AIC: 229.4
Df Residuals: 21 BIC: 231.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -88.6413 190.030 -0.466 0.646 -483.830 306.547
expression 19.5832 22.089 0.887 0.385 -26.353 65.519
Omnibus: 3.307 Durbin-Watson: 2.394
Prob(Omnibus): 0.191 Jarque-Bera (JB): 1.929
Skew: 0.465 Prob(JB): 0.381
Kurtosis: 1.929 Cond. No. 234.

CP101

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

F-statistic p-value df difference
1.073 0.321 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.496
Model: OLS Adj. R-squared: 0.359
Method: Least Squares F-statistic: 3.612
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0491
Time: 03:39:38 Log-Likelihood: -70.158
No. Observations: 15 AIC: 148.3
Df Residuals: 11 BIC: 151.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -188.1006 339.614 -0.554 0.591 -935.586 559.385
C(dose)[T.1] 137.9673 417.899 0.330 0.747 -781.823 1057.757
expression 31.0942 41.302 0.753 0.467 -59.812 122.000
expression:C(dose)[T.1] -11.1528 50.520 -0.221 0.829 -122.346 100.041
Omnibus: 5.979 Durbin-Watson: 0.695
Prob(Omnibus): 0.050 Jarque-Bera (JB): 3.430
Skew: -1.146 Prob(JB): 0.180
Kurtosis: 3.486 Cond. No. 644.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.494
Model: OLS Adj. R-squared: 0.410
Method: Least Squares F-statistic: 5.858
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0168
Time: 03:39:38 Log-Likelihood: -70.191
No. Observations: 15 AIC: 146.4
Df Residuals: 12 BIC: 148.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -126.8413 187.874 -0.675 0.512 -536.184 282.502
C(dose)[T.1] 45.7801 15.436 2.966 0.012 12.147 79.413
expression 23.6398 22.822 1.036 0.321 -26.086 73.365
Omnibus: 7.123 Durbin-Watson: 0.700
Prob(Omnibus): 0.028 Jarque-Bera (JB): 4.184
Skew: -1.247 Prob(JB): 0.123
Kurtosis: 3.687 Cond. No. 211.

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: 03:39:38 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: 0.123
Model: OLS Adj. R-squared: 0.056
Method: Least Squares F-statistic: 1.826
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.200
Time: 03:39:38 Log-Likelihood: -74.314
No. Observations: 15 AIC: 152.6
Df Residuals: 13 BIC: 154.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -222.3852 234.099 -0.950 0.359 -728.126 283.355
expression 38.1015 28.198 1.351 0.200 -22.818 99.021
Omnibus: 2.717 Durbin-Watson: 1.619
Prob(Omnibus): 0.257 Jarque-Bera (JB): 1.113
Skew: -0.144 Prob(JB): 0.573
Kurtosis: 1.697 Cond. No. 207.