LID SPN TRAP

1434819_at

St6gal2 beta galactoside alpha 2,6 sialyltransferase 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
0.256 0.618 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.660
Model: OLS Adj. R-squared: 0.607
Method: Least Squares F-statistic: 12.31
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000106
Time: 06:23:35 Log-Likelihood: -100.69
No. Observations: 23 AIC: 209.4
Df Residuals: 19 BIC: 213.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 57.6799 39.196 1.472 0.158 -24.358 139.717
C(dose)[T.1] 91.5255 66.220 1.382 0.183 -47.075 230.126
expression -0.8327 9.287 -0.090 0.929 -20.270 18.604
expression:C(dose)[T.1] -10.6141 17.269 -0.615 0.546 -46.760 25.531
Omnibus: 0.110 Durbin-Watson: 1.822
Prob(Omnibus): 0.947 Jarque-Bera (JB): 0.324
Skew: -0.078 Prob(JB): 0.850
Kurtosis: 2.440 Cond. No. 74.6

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.653
Model: OLS Adj. R-squared: 0.619
Method: Least Squares F-statistic: 18.86
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.49e-05
Time: 06:23:35 Log-Likelihood: -100.92
No. Observations: 23 AIC: 207.8
Df Residuals: 20 BIC: 211.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 70.4754 32.689 2.156 0.043 2.288 138.663
C(dose)[T.1] 51.2713 9.622 5.329 0.000 31.200 71.343
expression -3.9021 7.707 -0.506 0.618 -19.978 12.174
Omnibus: 0.258 Durbin-Watson: 1.754
Prob(Omnibus): 0.879 Jarque-Bera (JB): 0.445
Skew: 0.055 Prob(JB): 0.800
Kurtosis: 2.327 Cond. No. 32.1

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: 06:23:35 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: 0.162
Model: OLS Adj. R-squared: 0.122
Method: Least Squares F-statistic: 4.047
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0572
Time: 06:23:35 Log-Likelihood: -111.08
No. Observations: 23 AIC: 226.2
Df Residuals: 21 BIC: 228.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 163.1815 42.011 3.884 0.001 75.816 250.547
expression -21.3156 10.595 -2.012 0.057 -43.350 0.719
Omnibus: 5.025 Durbin-Watson: 2.096
Prob(Omnibus): 0.081 Jarque-Bera (JB): 1.633
Skew: 0.089 Prob(JB): 0.442
Kurtosis: 1.707 Cond. No. 26.8

CP101

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

F-statistic p-value df difference
0.000 0.988 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.518
Model: OLS Adj. R-squared: 0.386
Method: Least Squares F-statistic: 3.936
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0393
Time: 06:23:35 Log-Likelihood: -69.831
No. Observations: 15 AIC: 147.7
Df Residuals: 11 BIC: 150.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 236.3958 178.226 1.326 0.212 -155.877 628.669
C(dose)[T.1] -257.5763 245.394 -1.050 0.316 -797.685 282.532
expression -34.4521 36.268 -0.950 0.363 -114.277 45.373
expression:C(dose)[T.1] 60.6741 48.390 1.254 0.236 -45.832 167.180
Omnibus: 2.975 Durbin-Watson: 1.020
Prob(Omnibus): 0.226 Jarque-Bera (JB): 1.916
Skew: -0.867 Prob(JB): 0.384
Kurtosis: 2.764 Cond. No. 232.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.449
Model: OLS Adj. R-squared: 0.357
Method: Least Squares F-statistic: 4.885
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0281
Time: 06:23:35 Log-Likelihood: -70.833
No. Observations: 15 AIC: 147.7
Df Residuals: 12 BIC: 149.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 69.2401 121.076 0.572 0.578 -194.561 333.041
C(dose)[T.1] 49.3261 17.947 2.748 0.018 10.222 88.430
expression -0.3694 24.576 -0.015 0.988 -53.915 53.176
Omnibus: 2.704 Durbin-Watson: 0.809
Prob(Omnibus): 0.259 Jarque-Bera (JB): 1.863
Skew: -0.842 Prob(JB): 0.394
Kurtosis: 2.618 Cond. No. 82.4

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: 06:23:35 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: 0.102
Model: OLS Adj. R-squared: 0.033
Method: Least Squares F-statistic: 1.474
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.246
Time: 06:23:35 Log-Likelihood: -74.495
No. Observations: 15 AIC: 153.0
Df Residuals: 13 BIC: 154.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -69.7050 134.926 -0.517 0.614 -361.195 221.785
expression 32.0867 26.432 1.214 0.246 -25.017 89.190
Omnibus: 0.479 Durbin-Watson: 1.540
Prob(Omnibus): 0.787 Jarque-Bera (JB): 0.534
Skew: -0.331 Prob(JB): 0.766
Kurtosis: 2.355 Cond. No. 74.3