LID SPN TRAP

1431167_at

Dgkg diacylglycerol kinase, gamma

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
0.767 0.392 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.666
Model: OLS Adj. R-squared: 0.613
Method: Least Squares F-statistic: 12.61
Date: Thu, 21 Nov 2024 Prob (F-statistic): 9.09e-05
Time: 05:22:05 Log-Likelihood: -100.50
No. Observations: 23 AIC: 209.0
Df Residuals: 19 BIC: 213.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 93.4562 111.808 0.836 0.414 -140.560 327.472
C(dose)[T.1] 140.9826 179.107 0.787 0.441 -233.893 515.859
expression -6.3529 18.071 -0.352 0.729 -44.176 31.470
expression:C(dose)[T.1] -12.7539 27.741 -0.460 0.651 -70.816 45.308
Omnibus: 0.667 Durbin-Watson: 1.529
Prob(Omnibus): 0.716 Jarque-Bera (JB): 0.444
Skew: -0.325 Prob(JB): 0.801
Kurtosis: 2.796 Cond. No. 337.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.662
Model: OLS Adj. R-squared: 0.628
Method: Least Squares F-statistic: 19.59
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.95e-05
Time: 05:22:05 Log-Likelihood: -100.63
No. Observations: 23 AIC: 207.3
Df Residuals: 20 BIC: 210.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 126.8929 83.231 1.525 0.143 -46.723 300.509
C(dose)[T.1] 58.7878 10.622 5.534 0.000 36.631 80.945
expression -11.7651 13.438 -0.876 0.392 -39.796 16.265
Omnibus: 0.437 Durbin-Watson: 1.489
Prob(Omnibus): 0.804 Jarque-Bera (JB): 0.477
Skew: -0.281 Prob(JB): 0.788
Kurtosis: 2.574 Cond. No. 128.

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: 05:22:05 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: 0.144
Model: OLS Adj. R-squared: 0.104
Method: Least Squares F-statistic: 3.543
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0737
Time: 05:22:05 Log-Likelihood: -111.31
No. Observations: 23 AIC: 226.6
Df Residuals: 21 BIC: 228.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -123.9353 108.395 -1.143 0.266 -349.356 101.485
expression 31.8229 16.906 1.882 0.074 -3.335 66.980
Omnibus: 1.515 Durbin-Watson: 2.879
Prob(Omnibus): 0.469 Jarque-Bera (JB): 1.297
Skew: 0.432 Prob(JB): 0.523
Kurtosis: 2.221 Cond. No. 107.

CP101

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

F-statistic p-value df difference
4.769 0.050 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.620
Model: OLS Adj. R-squared: 0.516
Method: Least Squares F-statistic: 5.983
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0113
Time: 05:22:05 Log-Likelihood: -68.043
No. Observations: 15 AIC: 144.1
Df Residuals: 11 BIC: 146.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -90.8856 346.055 -0.263 0.798 -852.547 670.776
C(dose)[T.1] -186.7180 390.609 -0.478 0.642 -1046.443 673.007
expression 21.1416 46.194 0.458 0.656 -80.530 122.813
expression:C(dose)[T.1] 34.1063 52.695 0.647 0.531 -81.874 150.087
Omnibus: 1.532 Durbin-Watson: 1.041
Prob(Omnibus): 0.465 Jarque-Bera (JB): 1.072
Skew: -0.620 Prob(JB): 0.585
Kurtosis: 2.581 Cond. No. 637.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.606
Model: OLS Adj. R-squared: 0.540
Method: Least Squares F-statistic: 9.211
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00377
Time: 05:22:06 Log-Likelihood: -68.323
No. Observations: 15 AIC: 142.6
Df Residuals: 12 BIC: 144.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -287.1510 162.655 -1.765 0.103 -641.545 67.243
C(dose)[T.1] 65.8946 15.354 4.292 0.001 32.441 99.348
expression 47.3512 21.682 2.184 0.050 0.109 94.593
Omnibus: 0.741 Durbin-Watson: 1.041
Prob(Omnibus): 0.691 Jarque-Bera (JB): 0.468
Skew: -0.402 Prob(JB): 0.791
Kurtosis: 2.678 Cond. No. 183.

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: 0.000
Model: OLS Adj. R-squared: -0.077
Method: Least Squares F-statistic: 0.001234
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.973
Time: 05:22:06 Log-Likelihood: -75.299
No. Observations: 15 AIC: 154.6
Df Residuals: 13 BIC: 156.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 86.2895 210.212 0.410 0.688 -367.847 540.426
expression 1.0105 28.762 0.035 0.973 -61.125 63.146
Omnibus: 0.591 Durbin-Watson: 1.627
Prob(Omnibus): 0.744 Jarque-Bera (JB): 0.576
Skew: 0.044 Prob(JB): 0.750
Kurtosis: 2.044 Cond. No. 154.