LID SPN TRAP

1433531_at

Acsl4 acyl-CoA synthetase long-chain family member 4

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
0.649 0.430 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.665
Model: OLS Adj. R-squared: 0.612
Method: Least Squares F-statistic: 12.58
Date: Thu, 21 Nov 2024 Prob (F-statistic): 9.24e-05
Time: 04:10:33 Log-Likelihood: -100.52
No. Observations: 23 AIC: 209.0
Df Residuals: 19 BIC: 213.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 85.6463 146.842 0.583 0.567 -221.698 392.991
C(dose)[T.1] 171.6585 217.909 0.788 0.441 -284.429 627.747
expression -4.1485 19.360 -0.214 0.833 -44.670 36.373
expression:C(dose)[T.1] -15.1918 28.398 -0.535 0.599 -74.630 44.247
Omnibus: 1.707 Durbin-Watson: 1.793
Prob(Omnibus): 0.426 Jarque-Bera (JB): 1.061
Skew: 0.176 Prob(JB): 0.588
Kurtosis: 2.008 Cond. No. 494.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.660
Model: OLS Adj. R-squared: 0.626
Method: Least Squares F-statistic: 19.42
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.06e-05
Time: 04:10:33 Log-Likelihood: -100.70
No. Observations: 23 AIC: 207.4
Df Residuals: 20 BIC: 210.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 139.1540 105.573 1.318 0.202 -81.066 359.374
C(dose)[T.1] 55.1889 8.931 6.179 0.000 36.558 73.820
expression -11.2092 13.909 -0.806 0.430 -40.223 17.804
Omnibus: 1.820 Durbin-Watson: 1.725
Prob(Omnibus): 0.403 Jarque-Bera (JB): 1.039
Skew: 0.092 Prob(JB): 0.595
Kurtosis: 1.975 Cond. No. 191.

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: 04:10:33 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: 0.011
Model: OLS Adj. R-squared: -0.036
Method: Least Squares F-statistic: 0.2374
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.631
Time: 04:10:33 Log-Likelihood: -112.98
No. Observations: 23 AIC: 230.0
Df Residuals: 21 BIC: 232.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -3.7534 171.458 -0.022 0.983 -360.319 352.812
expression 10.9010 22.372 0.487 0.631 -35.624 57.426
Omnibus: 3.549 Durbin-Watson: 2.468
Prob(Omnibus): 0.170 Jarque-Bera (JB): 1.658
Skew: 0.313 Prob(JB): 0.437
Kurtosis: 1.843 Cond. No. 186.

CP101

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

F-statistic p-value df difference
2.935 0.112 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.607
Model: OLS Adj. R-squared: 0.499
Method: Least Squares F-statistic: 5.653
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0136
Time: 04:10:33 Log-Likelihood: -68.304
No. Observations: 15 AIC: 144.6
Df Residuals: 11 BIC: 147.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -2.4647 217.593 -0.011 0.991 -481.384 476.455
C(dose)[T.1] -321.7075 304.208 -1.058 0.313 -991.265 347.850
expression 10.5909 32.936 0.322 0.754 -61.901 83.082
expression:C(dose)[T.1] 52.8289 44.927 1.176 0.264 -46.056 151.713
Omnibus: 1.686 Durbin-Watson: 0.818
Prob(Omnibus): 0.430 Jarque-Bera (JB): 1.022
Skew: -0.305 Prob(JB): 0.600
Kurtosis: 1.876 Cond. No. 412.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.557
Model: OLS Adj. R-squared: 0.483
Method: Least Squares F-statistic: 7.547
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00755
Time: 04:10:33 Log-Likelihood: -69.192
No. Observations: 15 AIC: 144.4
Df Residuals: 12 BIC: 146.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -189.8313 150.522 -1.261 0.231 -517.791 138.129
C(dose)[T.1] 35.5103 16.213 2.190 0.049 0.184 70.836
expression 38.9824 22.755 1.713 0.112 -10.597 88.561
Omnibus: 2.321 Durbin-Watson: 0.612
Prob(Omnibus): 0.313 Jarque-Bera (JB): 1.194
Skew: -0.332 Prob(JB): 0.551
Kurtosis: 1.788 Cond. No. 149.

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: 04:10:33 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: 0.380
Model: OLS Adj. R-squared: 0.332
Method: Least Squares F-statistic: 7.969
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0144
Time: 04:10:33 Log-Likelihood: -71.714
No. Observations: 15 AIC: 147.4
Df Residuals: 13 BIC: 148.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -337.5490 152.960 -2.207 0.046 -667.999 -7.099
expression 63.5390 22.508 2.823 0.014 14.914 112.164
Omnibus: 1.356 Durbin-Watson: 1.109
Prob(Omnibus): 0.508 Jarque-Bera (JB): 0.803
Skew: -0.035 Prob(JB): 0.669
Kurtosis: 1.868 Cond. No. 133.