LID SPN TRAP

1435621_at

Far2 fatty acyl CoA reductase 2

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
0.219 0.645 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.675
Model: OLS Adj. R-squared: 0.624
Method: Least Squares F-statistic: 13.16
Date: Thu, 21 Nov 2024 Prob (F-statistic): 6.99e-05
Time: 05:22:06 Log-Likelihood: -100.18
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 133.7582 65.785 2.033 0.056 -3.932 271.449
C(dose)[T.1] -34.3175 76.503 -0.449 0.659 -194.441 125.806
expression -14.2676 11.750 -1.214 0.240 -38.860 10.325
expression:C(dose)[T.1] 15.8288 13.900 1.139 0.269 -13.263 44.921
Omnibus: 0.683 Durbin-Watson: 2.099
Prob(Omnibus): 0.711 Jarque-Bera (JB): 0.657
Skew: -0.001 Prob(JB): 0.720
Kurtosis: 2.172 Cond. No. 141.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.653
Model: OLS Adj. R-squared: 0.618
Method: Least Squares F-statistic: 18.81
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.54e-05
Time: 05:22:06 Log-Likelihood: -100.94
No. Observations: 23 AIC: 207.9
Df Residuals: 20 BIC: 211.3
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 70.6910 35.769 1.976 0.062 -3.922 145.304
C(dose)[T.1] 52.2011 9.054 5.765 0.000 33.314 71.088
expression -2.9562 6.323 -0.468 0.645 -16.147 10.234
Omnibus: 0.210 Durbin-Watson: 2.011
Prob(Omnibus): 0.900 Jarque-Bera (JB): 0.413
Skew: -0.005 Prob(JB): 0.814
Kurtosis: 2.344 Cond. No. 46.4

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: 05:22:06 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: 0.076
Model: OLS Adj. R-squared: 0.032
Method: Least Squares F-statistic: 1.725
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.203
Time: 05:22:06 Log-Likelihood: -112.20
No. Observations: 23 AIC: 228.4
Df Residuals: 21 BIC: 230.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 148.4095 52.753 2.813 0.010 38.704 258.115
expression -12.7402 9.699 -1.314 0.203 -32.910 7.430
Omnibus: 3.606 Durbin-Watson: 2.632
Prob(Omnibus): 0.165 Jarque-Bera (JB): 1.901
Skew: 0.426 Prob(JB): 0.386
Kurtosis: 1.879 Cond. No. 42.7

CP101

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

F-statistic p-value df difference
0.007 0.936 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.457
Model: OLS Adj. R-squared: 0.309
Method: Least Squares F-statistic: 3.090
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0718
Time: 05:22:06 Log-Likelihood: -70.716
No. Observations: 15 AIC: 149.4
Df Residuals: 11 BIC: 152.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 95.3229 157.793 0.604 0.558 -251.977 442.623
C(dose)[T.1] -63.5317 275.363 -0.231 0.822 -669.602 542.539
expression -4.1447 23.379 -0.177 0.863 -55.601 47.312
expression:C(dose)[T.1] 16.1832 39.618 0.408 0.691 -71.016 103.383
Omnibus: 3.310 Durbin-Watson: 0.780
Prob(Omnibus): 0.191 Jarque-Bera (JB): 2.058
Skew: -0.905 Prob(JB): 0.357
Kurtosis: 2.861 Cond. No. 298.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.449
Model: OLS Adj. R-squared: 0.357
Method: Least Squares F-statistic: 4.891
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0280
Time: 05:22:06 Log-Likelihood: -70.829
No. Observations: 15 AIC: 147.7
Df Residuals: 12 BIC: 149.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 57.3964 123.076 0.466 0.649 -210.763 325.556
C(dose)[T.1] 48.7244 16.758 2.907 0.013 12.211 85.238
expression 1.4906 18.207 0.082 0.936 -38.180 41.161
Omnibus: 2.922 Durbin-Watson: 0.805
Prob(Omnibus): 0.232 Jarque-Bera (JB): 1.972
Skew: -0.873 Prob(JB): 0.373
Kurtosis: 2.671 Cond. No. 111.

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: 05:22:06 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: 0.061
Model: OLS Adj. R-squared: -0.011
Method: Least Squares F-statistic: 0.8444
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.375
Time: 05:22:06 Log-Likelihood: -74.828
No. Observations: 15 AIC: 153.7
Df Residuals: 13 BIC: 155.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -42.2780 148.267 -0.285 0.780 -362.590 278.034
expression 19.7048 21.444 0.919 0.375 -26.621 66.031
Omnibus: 0.298 Durbin-Watson: 1.493
Prob(Omnibus): 0.861 Jarque-Bera (JB): 0.336
Skew: -0.269 Prob(JB): 0.845
Kurtosis: 2.501 Cond. No. 106.