LID SPN TRAP

1459801_at

B3galt5 UDP-Gal:betaGlcNAc beta 1,3-galactosyltransferase, polypeptide 5

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
0.241 0.629 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.653
Model: OLS Adj. R-squared: 0.598
Method: Least Squares F-statistic: 11.93
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000128
Time: 05:05:32 Log-Likelihood: -100.93
No. Observations: 23 AIC: 209.9
Df Residuals: 19 BIC: 214.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 104.2275 123.153 0.846 0.408 -153.535 361.990
C(dose)[T.1] 52.2582 230.581 0.227 0.823 -430.353 534.869
expression -9.4409 23.215 -0.407 0.689 -58.031 39.149
expression:C(dose)[T.1] 0.0405 44.039 0.001 0.999 -92.134 92.215
Omnibus: 0.617 Durbin-Watson: 1.975
Prob(Omnibus): 0.735 Jarque-Bera (JB): 0.630
Skew: 0.034 Prob(JB): 0.730
Kurtosis: 2.192 Cond. No. 333.

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: 18.84
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.51e-05
Time: 05:05:32 Log-Likelihood: -100.93
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 104.1678 102.051 1.021 0.320 -108.707 317.043
C(dose)[T.1] 52.4703 8.895 5.899 0.000 33.916 71.025
expression -9.4296 19.228 -0.490 0.629 -49.539 30.680
Omnibus: 0.617 Durbin-Watson: 1.975
Prob(Omnibus): 0.735 Jarque-Bera (JB): 0.630
Skew: 0.035 Prob(JB): 0.730
Kurtosis: 2.192 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: 05:05:32 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: 0.050
Model: OLS Adj. R-squared: 0.005
Method: Least Squares F-statistic: 1.103
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.306
Time: 05:05:32 Log-Likelihood: -112.52
No. Observations: 23 AIC: 229.0
Df Residuals: 21 BIC: 231.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 247.6841 160.096 1.547 0.137 -85.253 580.621
expression -31.9683 30.441 -1.050 0.306 -95.273 31.337
Omnibus: 1.787 Durbin-Watson: 2.643
Prob(Omnibus): 0.409 Jarque-Bera (JB): 1.166
Skew: 0.259 Prob(JB): 0.558
Kurtosis: 2.026 Cond. No. 124.

CP101

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

F-statistic p-value df difference
0.005 0.942 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.480
Model: OLS Adj. R-squared: 0.338
Method: Least Squares F-statistic: 3.382
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0579
Time: 05:05:32 Log-Likelihood: -70.399
No. Observations: 15 AIC: 148.8
Df Residuals: 11 BIC: 151.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 485.7968 547.715 0.887 0.394 -719.716 1691.310
C(dose)[T.1] -436.2593 602.200 -0.724 0.484 -1761.692 889.174
expression -73.4735 96.167 -0.764 0.461 -285.137 138.190
expression:C(dose)[T.1] 85.2448 105.704 0.806 0.437 -147.409 317.899
Omnibus: 2.700 Durbin-Watson: 0.686
Prob(Omnibus): 0.259 Jarque-Bera (JB): 1.699
Skew: -0.817 Prob(JB): 0.428
Kurtosis: 2.778 Cond. No. 682.

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: 4.890
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0280
Time: 05:05:32 Log-Likelihood: -70.830
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 84.0372 224.262 0.375 0.714 -404.588 572.662
C(dose)[T.1] 49.2112 15.737 3.127 0.009 14.923 83.500
expression -2.9168 39.333 -0.074 0.942 -88.616 82.782
Omnibus: 2.630 Durbin-Watson: 0.805
Prob(Omnibus): 0.268 Jarque-Bera (JB): 1.853
Skew: -0.833 Prob(JB): 0.396
Kurtosis: 2.568 Cond. No. 169.

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: 05:05:32 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: 0.0007141
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.979
Time: 05:05:32 Log-Likelihood: -75.300
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 101.4161 290.178 0.349 0.732 -525.474 728.307
expression -1.3603 50.905 -0.027 0.979 -111.335 108.614
Omnibus: 0.614 Durbin-Watson: 1.622
Prob(Omnibus): 0.736 Jarque-Bera (JB): 0.585
Skew: 0.051 Prob(JB): 0.746
Kurtosis: 2.038 Cond. No. 168.