LID SPN TRAP

1457024_x_at

Slc35a2 solute carrier family 35 (UDP-galactose transporter), member A2

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
1.247 0.277 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.733
Model: OLS Adj. R-squared: 0.691
Method: Least Squares F-statistic: 17.42
Date: Thu, 03 Apr 2025 Prob (F-statistic): 1.11e-05
Time: 23:03:04 Log-Likelihood: -97.903
No. Observations: 23 AIC: 203.8
Df Residuals: 19 BIC: 208.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 57.1470 31.903 1.791 0.089 -9.627 123.921
C(dose)[T.1] -54.7540 53.784 -1.018 0.321 -167.325 57.817
expression -0.6847 7.325 -0.093 0.927 -16.016 14.647
expression:C(dose)[T.1] 29.6146 13.898 2.131 0.046 0.527 58.703
Omnibus: 1.102 Durbin-Watson: 2.018
Prob(Omnibus): 0.576 Jarque-Bera (JB): 0.994
Skew: 0.450 Prob(JB): 0.608
Kurtosis: 2.524 Cond. No. 69.2

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.670
Model: OLS Adj. R-squared: 0.637
Method: Least Squares F-statistic: 20.27
Date: Thu, 03 Apr 2025 Prob (F-statistic): 1.55e-05
Time: 23:03:04 Log-Likelihood: -100.37
No. Observations: 23 AIC: 206.7
Df Residuals: 20 BIC: 210.1
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 21.8368 29.577 0.738 0.469 -39.860 83.533
C(dose)[T.1] 58.2940 9.597 6.074 0.000 38.276 78.312
expression 7.5424 6.754 1.117 0.277 -6.545 21.630
Omnibus: 0.231 Durbin-Watson: 2.103
Prob(Omnibus): 0.891 Jarque-Bera (JB): 0.316
Skew: 0.204 Prob(JB): 0.854
Kurtosis: 2.596 Cond. No. 30.3

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, 03 Apr 2025 Prob (F-statistic): 3.51e-06
Time: 23:03:05 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: 0.060
Model: OLS Adj. R-squared: 0.015
Method: Least Squares F-statistic: 1.345
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.259
Time: 23:03:05 Log-Likelihood: -112.39
No. Observations: 23 AIC: 228.8
Df Residuals: 21 BIC: 231.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 125.1866 39.824 3.144 0.005 42.369 208.004
expression -11.4312 9.856 -1.160 0.259 -31.928 9.066
Omnibus: 2.504 Durbin-Watson: 2.237
Prob(Omnibus): 0.286 Jarque-Bera (JB): 1.510
Skew: 0.360 Prob(JB): 0.470
Kurtosis: 1.972 Cond. No. 24.4

CP101

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

F-statistic p-value df difference
0.382 0.548 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.470
Model: OLS Adj. R-squared: 0.325
Method: Least Squares F-statistic: 3.250
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0638
Time: 23:03:05 Log-Likelihood: -70.540
No. Observations: 15 AIC: 149.1
Df Residuals: 11 BIC: 151.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 61.5606 125.931 0.489 0.635 -215.613 338.734
C(dose)[T.1] 94.4551 139.790 0.676 0.513 -213.220 402.131
expression 1.5994 34.174 0.047 0.964 -73.616 76.815
expression:C(dose)[T.1] -10.7772 36.894 -0.292 0.776 -91.980 70.426
Omnibus: 2.383 Durbin-Watson: 0.913
Prob(Omnibus): 0.304 Jarque-Bera (JB): 1.253
Skew: -0.708 Prob(JB): 0.534
Kurtosis: 2.983 Cond. No. 119.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.466
Model: OLS Adj. R-squared: 0.377
Method: Least Squares F-statistic: 5.231
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0232
Time: 23:03:05 Log-Likelihood: -70.598
No. Observations: 15 AIC: 147.2
Df Residuals: 12 BIC: 149.3
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 95.4852 46.804 2.040 0.064 -6.492 197.462
C(dose)[T.1] 53.9608 17.308 3.118 0.009 16.249 91.672
expression -7.6471 12.378 -0.618 0.548 -34.617 19.323
Omnibus: 2.139 Durbin-Watson: 0.890
Prob(Omnibus): 0.343 Jarque-Bera (JB): 1.208
Skew: -0.692 Prob(JB): 0.547
Kurtosis: 2.867 Cond. No. 26.5

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, 03 Apr 2025 Prob (F-statistic): 0.00629
Time: 23:03:05 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: 0.033
Model: OLS Adj. R-squared: -0.041
Method: Least Squares F-statistic: 0.4443
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.517
Time: 23:03:05 Log-Likelihood: -75.048
No. Observations: 15 AIC: 154.1
Df Residuals: 13 BIC: 155.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 55.4632 58.177 0.953 0.358 -70.221 181.148
expression 9.5480 14.324 0.667 0.517 -21.397 40.493
Omnibus: 0.917 Durbin-Watson: 1.610
Prob(Omnibus): 0.632 Jarque-Bera (JB): 0.686
Skew: 0.053 Prob(JB): 0.710
Kurtosis: 1.958 Cond. No. 25.0