LID SPN TRAP

1458669_at

Agpat5 1-acylglycerol-3-phosphate O-acyltransferase 5 (lysophosphatidic acid acyltransferase, epsilon)

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
0.165 0.689 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.655
Model: OLS Adj. R-squared: 0.601
Method: Least Squares F-statistic: 12.03
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000122
Time: 04:39:39 Log-Likelihood: -100.87
No. Observations: 23 AIC: 209.7
Df Residuals: 19 BIC: 214.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 53.4036 30.027 1.779 0.091 -9.444 116.251
C(dose)[T.1] 68.1666 39.277 1.736 0.099 -14.040 150.373
expression 0.2399 8.762 0.027 0.978 -18.099 18.579
expression:C(dose)[T.1] -4.9797 12.052 -0.413 0.684 -30.204 20.245
Omnibus: 0.099 Durbin-Watson: 1.769
Prob(Omnibus): 0.952 Jarque-Bera (JB): 0.324
Skew: 0.018 Prob(JB): 0.850
Kurtosis: 2.419 Cond. No. 41.1

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.652
Model: OLS Adj. R-squared: 0.617
Method: Least Squares F-statistic: 18.73
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.61e-05
Time: 04:39:39 Log-Likelihood: -100.97
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 62.2314 20.657 3.013 0.007 19.142 105.321
C(dose)[T.1] 52.3924 9.038 5.797 0.000 33.539 71.246
expression -2.3922 5.890 -0.406 0.689 -14.679 9.894
Omnibus: 0.142 Durbin-Watson: 1.786
Prob(Omnibus): 0.932 Jarque-Bera (JB): 0.334
Skew: 0.129 Prob(JB): 0.846
Kurtosis: 2.469 Cond. No. 17.1

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: 04:39:39 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: 0.067
Model: OLS Adj. R-squared: 0.023
Method: Least Squares F-statistic: 1.511
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.233
Time: 04:39:39 Log-Likelihood: -112.31
No. Observations: 23 AIC: 228.6
Df Residuals: 21 BIC: 230.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 115.0986 29.612 3.887 0.001 53.518 176.679
expression -11.1790 9.093 -1.229 0.233 -30.089 7.731
Omnibus: 5.856 Durbin-Watson: 2.357
Prob(Omnibus): 0.054 Jarque-Bera (JB): 1.985
Skew: 0.292 Prob(JB): 0.371
Kurtosis: 1.684 Cond. No. 15.1

CP101

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

F-statistic p-value df difference
0.478 0.503 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.477
Model: OLS Adj. R-squared: 0.334
Method: Least Squares F-statistic: 3.341
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0597
Time: 04:39:39 Log-Likelihood: -70.442
No. Observations: 15 AIC: 148.9
Df Residuals: 11 BIC: 151.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 68.2075 48.698 1.401 0.189 -38.977 175.392
C(dose)[T.1] 74.8579 60.668 1.234 0.243 -58.672 208.387
expression -0.2305 13.991 -0.016 0.987 -31.024 30.563
expression:C(dose)[T.1] -6.2027 16.314 -0.380 0.711 -42.110 29.704
Omnibus: 7.657 Durbin-Watson: 0.834
Prob(Omnibus): 0.022 Jarque-Bera (JB): 4.507
Skew: -1.276 Prob(JB): 0.105
Kurtosis: 3.832 Cond. No. 47.3

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.470
Model: OLS Adj. R-squared: 0.382
Method: Least Squares F-statistic: 5.318
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0222
Time: 04:39:39 Log-Likelihood: -70.540
No. Observations: 15 AIC: 147.1
Df Residuals: 12 BIC: 149.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 83.6211 26.002 3.216 0.007 26.967 140.275
C(dose)[T.1] 52.7000 16.247 3.244 0.007 17.301 88.099
expression -4.7923 6.935 -0.691 0.503 -19.902 10.317
Omnibus: 6.030 Durbin-Watson: 0.866
Prob(Omnibus): 0.049 Jarque-Bera (JB): 3.512
Skew: -1.164 Prob(JB): 0.173
Kurtosis: 3.452 Cond. No. 14.2

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: 04:39:39 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: 0.005
Model: OLS Adj. R-squared: -0.071
Method: Least Squares F-statistic: 0.06597
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.801
Time: 04:39:39 Log-Likelihood: -75.262
No. Observations: 15 AIC: 154.5
Df Residuals: 13 BIC: 155.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 85.2721 34.218 2.492 0.027 11.348 159.196
expression 2.2274 8.672 0.257 0.801 -16.507 20.962
Omnibus: 0.552 Durbin-Watson: 1.519
Prob(Omnibus): 0.759 Jarque-Bera (JB): 0.572
Skew: 0.119 Prob(JB): 0.751
Kurtosis: 2.073 Cond. No. 14.1