LID SPN TRAP

1460194_at

Phyh phytanoyl-CoA hydroxylase

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
0.002 0.965 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.653
Model: OLS Adj. R-squared: 0.598
Method: Least Squares F-statistic: 11.90
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000130
Time: 04:27:41 Log-Likelihood: -100.95
No. Observations: 23 AIC: 209.9
Df Residuals: 19 BIC: 214.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 121.5188 239.169 0.508 0.617 -379.067 622.105
C(dose)[T.1] -97.4781 343.348 -0.284 0.780 -816.115 621.158
expression -7.6694 27.242 -0.282 0.781 -64.688 49.349
expression:C(dose)[T.1] 16.7948 38.293 0.439 0.666 -63.353 96.943
Omnibus: 0.581 Durbin-Watson: 1.890
Prob(Omnibus): 0.748 Jarque-Bera (JB): 0.616
Skew: 0.048 Prob(JB): 0.735
Kurtosis: 2.204 Cond. No. 905.

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: 18.50
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.83e-05
Time: 04:27:41 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 46.9191 164.709 0.285 0.779 -296.658 390.497
C(dose)[T.1] 53.0261 11.234 4.720 0.000 29.591 76.461
expression 0.8305 18.754 0.044 0.965 -38.291 39.952
Omnibus: 0.360 Durbin-Watson: 1.901
Prob(Omnibus): 0.835 Jarque-Bera (JB): 0.507
Skew: 0.067 Prob(JB): 0.776
Kurtosis: 2.285 Cond. No. 342.

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: 04:27:41 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: 0.258
Model: OLS Adj. R-squared: 0.223
Method: Least Squares F-statistic: 7.310
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0133
Time: 04:27:41 Log-Likelihood: -109.67
No. Observations: 23 AIC: 223.3
Df Residuals: 21 BIC: 225.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -423.2282 186.125 -2.274 0.034 -810.296 -36.160
expression 56.1603 20.772 2.704 0.013 12.963 99.357
Omnibus: 2.153 Durbin-Watson: 2.521
Prob(Omnibus): 0.341 Jarque-Bera (JB): 1.157
Skew: 0.151 Prob(JB): 0.561
Kurtosis: 1.943 Cond. No. 272.

CP101

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

F-statistic p-value df difference
0.048 0.830 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.534
Model: OLS Adj. R-squared: 0.406
Method: Least Squares F-statistic: 4.195
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0331
Time: 04:27:41 Log-Likelihood: -69.580
No. Observations: 15 AIC: 147.2
Df Residuals: 11 BIC: 150.0
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 370.1470 292.278 1.266 0.232 -273.152 1013.446
C(dose)[T.1] -431.6002 346.112 -1.247 0.238 -1193.388 330.187
expression -37.4076 36.092 -1.036 0.322 -116.845 42.030
expression:C(dose)[T.1] 60.4549 43.318 1.396 0.190 -34.888 155.798
Omnibus: 0.915 Durbin-Watson: 1.414
Prob(Omnibus): 0.633 Jarque-Bera (JB): 0.757
Skew: -0.253 Prob(JB): 0.685
Kurtosis: 2.023 Cond. No. 533.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.451
Model: OLS Adj. R-squared: 0.359
Method: Least Squares F-statistic: 4.929
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0274
Time: 04:27:41 Log-Likelihood: -70.803
No. Observations: 15 AIC: 147.6
Df Residuals: 12 BIC: 149.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 30.5364 168.166 0.182 0.859 -335.867 396.939
C(dose)[T.1] 50.8641 17.443 2.916 0.013 12.859 88.869
expression 4.5588 20.732 0.220 0.830 -40.613 49.731
Omnibus: 2.127 Durbin-Watson: 0.771
Prob(Omnibus): 0.345 Jarque-Bera (JB): 1.592
Skew: -0.745 Prob(JB): 0.451
Kurtosis: 2.429 Cond. No. 173.

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: 04:27:41 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: 0.062
Model: OLS Adj. R-squared: -0.010
Method: Least Squares F-statistic: 0.8586
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.371
Time: 04:27:41 Log-Likelihood: -74.820
No. Observations: 15 AIC: 153.6
Df Residuals: 13 BIC: 155.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 265.2463 185.430 1.430 0.176 -135.351 665.843
expression -21.7262 23.447 -0.927 0.371 -72.380 28.928
Omnibus: 0.813 Durbin-Watson: 1.673
Prob(Omnibus): 0.666 Jarque-Bera (JB): 0.650
Skew: -0.001 Prob(JB): 0.722
Kurtosis: 1.980 Cond. No. 151.