LID SPN TRAP

1439478_at

Acot2 acyl-CoA thioesterase 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
0.058 0.813 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.681
Model: OLS Adj. R-squared: 0.631
Method: Least Squares F-statistic: 13.54
Date: Thu, 21 Nov 2024 Prob (F-statistic): 5.84e-05
Time: 05:02:42 Log-Likelihood: -99.955
No. Observations: 23 AIC: 207.9
Df Residuals: 19 BIC: 212.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -233.2740 249.417 -0.935 0.361 -755.311 288.763
C(dose)[T.1] 421.9677 270.858 1.558 0.136 -144.945 988.881
expression 34.2861 29.738 1.153 0.263 -27.956 96.528
expression:C(dose)[T.1] -44.2514 32.434 -1.364 0.188 -112.138 23.635
Omnibus: 1.602 Durbin-Watson: 2.277
Prob(Omnibus): 0.449 Jarque-Bera (JB): 1.073
Skew: -0.223 Prob(JB): 0.585
Kurtosis: 2.041 Cond. No. 797.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.650
Model: OLS Adj. R-squared: 0.615
Method: Least Squares F-statistic: 18.58
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.75e-05
Time: 05:02:42 Log-Likelihood: -101.03
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 78.6350 101.842 0.772 0.449 -133.804 291.074
C(dose)[T.1] 52.6328 9.235 5.699 0.000 33.369 71.896
expression -2.9132 12.125 -0.240 0.813 -28.204 22.378
Omnibus: 0.283 Durbin-Watson: 1.830
Prob(Omnibus): 0.868 Jarque-Bera (JB): 0.461
Skew: 0.041 Prob(JB): 0.794
Kurtosis: 2.312 Cond. No. 196.

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: 05:02:42 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: 0.082
Model: OLS Adj. R-squared: 0.038
Method: Least Squares F-statistic: 1.869
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.186
Time: 05:02:42 Log-Likelihood: -112.12
No. Observations: 23 AIC: 228.2
Df Residuals: 21 BIC: 230.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 285.1938 150.460 1.895 0.072 -27.706 598.093
expression -24.8484 18.176 -1.367 0.186 -62.648 12.951
Omnibus: 4.252 Durbin-Watson: 2.181
Prob(Omnibus): 0.119 Jarque-Bera (JB): 1.591
Skew: 0.180 Prob(JB): 0.451
Kurtosis: 1.762 Cond. No. 183.

CP101

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

F-statistic p-value df difference
1.544 0.238 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.513
Model: OLS Adj. R-squared: 0.380
Method: Least Squares F-statistic: 3.863
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0413
Time: 05:02:42 Log-Likelihood: -69.903
No. Observations: 15 AIC: 147.8
Df Residuals: 11 BIC: 150.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 294.9039 246.069 1.198 0.256 -246.691 836.499
C(dose)[T.1] 116.4626 454.458 0.256 0.802 -883.793 1116.718
expression -24.7656 26.762 -0.925 0.375 -83.668 34.137
expression:C(dose)[T.1] -9.2347 51.549 -0.179 0.861 -122.693 104.224
Omnibus: 2.533 Durbin-Watson: 0.729
Prob(Omnibus): 0.282 Jarque-Bera (JB): 1.790
Skew: -0.817 Prob(JB): 0.409
Kurtosis: 2.559 Cond. No. 642.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.512
Model: OLS Adj. R-squared: 0.430
Method: Least Squares F-statistic: 6.286
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0136
Time: 05:02:42 Log-Likelihood: -69.925
No. Observations: 15 AIC: 145.8
Df Residuals: 12 BIC: 148.0
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 317.7650 201.729 1.575 0.141 -121.765 757.295
C(dose)[T.1] 35.1239 18.647 1.884 0.084 -5.505 75.753
expression -27.2545 21.931 -1.243 0.238 -75.038 20.529
Omnibus: 2.553 Durbin-Watson: 0.755
Prob(Omnibus): 0.279 Jarque-Bera (JB): 1.817
Skew: -0.821 Prob(JB): 0.403
Kurtosis: 2.544 Cond. No. 247.

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: 05:02:42 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: 0.367
Model: OLS Adj. R-squared: 0.319
Method: Least Squares F-statistic: 7.545
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0166
Time: 05:02:42 Log-Likelihood: -71.868
No. Observations: 15 AIC: 147.7
Df Residuals: 13 BIC: 149.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 560.0057 169.971 3.295 0.006 192.807 927.205
expression -52.3402 19.055 -2.747 0.017 -93.507 -11.174
Omnibus: 2.862 Durbin-Watson: 1.459
Prob(Omnibus): 0.239 Jarque-Bera (JB): 1.109
Skew: -0.072 Prob(JB): 0.574
Kurtosis: 1.676 Cond. No. 190.