LID SPN TRAP

1438624_x_at

Hs3st2 heparan sulfate (glucosamine) 3-O-sulfotransferase 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
0.049 0.828 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.683
Model: OLS Adj. R-squared: 0.633
Method: Least Squares F-statistic: 13.63
Date: Sun, 26 Oct 2025 Prob (F-statistic): 5.60e-05
Time: 03:06:57 Log-Likelihood: -99.904
No. Observations: 23 AIC: 207.8
Df Residuals: 19 BIC: 212.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 9.2251 49.649 0.186 0.855 -94.692 113.142
C(dose)[T.1] 148.4547 68.038 2.182 0.042 6.050 290.859
expression 10.1670 11.142 0.913 0.373 -13.153 33.487
expression:C(dose)[T.1] -20.6401 14.728 -1.401 0.177 -51.466 10.186
Omnibus: 0.279 Durbin-Watson: 1.845
Prob(Omnibus): 0.870 Jarque-Bera (JB): 0.254
Skew: 0.212 Prob(JB): 0.881
Kurtosis: 2.708 Cond. No. 103.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.650
Model: OLS Adj. R-squared: 0.615
Method: Least Squares F-statistic: 18.56
Date: Sun, 26 Oct 2025 Prob (F-statistic): 2.77e-05
Time: 03:06:57 Log-Likelihood: -101.03
No. Observations: 23 AIC: 208.1
Df Residuals: 20 BIC: 211.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 61.4871 33.558 1.832 0.082 -8.514 131.488
C(dose)[T.1] 53.9335 9.167 5.883 0.000 34.811 73.056
expression -1.6451 7.460 -0.221 0.828 -17.207 13.916
Omnibus: 0.449 Durbin-Watson: 1.866
Prob(Omnibus): 0.799 Jarque-Bera (JB): 0.554
Skew: 0.053 Prob(JB): 0.758
Kurtosis: 2.247 Cond. No. 37.5

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: Sun, 26 Oct 2025 Prob (F-statistic): 3.51e-06
Time: 03:06:57 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: 0.044
Model: OLS Adj. R-squared: -0.002
Method: Least Squares F-statistic: 0.9667
Date: Sun, 26 Oct 2025 Prob (F-statistic): 0.337
Time: 03:06:57 Log-Likelihood: -112.59
No. Observations: 23 AIC: 229.2
Df Residuals: 21 BIC: 231.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 27.7525 53.322 0.520 0.608 -83.136 138.641
expression 11.3021 11.495 0.983 0.337 -12.604 35.208
Omnibus: 2.992 Durbin-Watson: 2.257
Prob(Omnibus): 0.224 Jarque-Bera (JB): 1.909
Skew: 0.489 Prob(JB): 0.385
Kurtosis: 1.982 Cond. No. 36.7

CP101

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

F-statistic p-value df difference
2.135 0.170 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.821
Model: OLS Adj. R-squared: 0.772
Method: Least Squares F-statistic: 16.80
Date: Sun, 26 Oct 2025 Prob (F-statistic): 0.000203
Time: 03:06:57 Log-Likelihood: -62.404
No. Observations: 15 AIC: 132.8
Df Residuals: 11 BIC: 135.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -42.2070 112.049 -0.377 0.714 -288.826 204.412
C(dose)[T.1] 744.4813 168.135 4.428 0.001 374.419 1114.543
expression 22.1130 22.558 0.980 0.348 -27.536 71.762
expression:C(dose)[T.1] -147.5795 35.046 -4.211 0.001 -224.714 -70.445
Omnibus: 0.838 Durbin-Watson: 2.033
Prob(Omnibus): 0.658 Jarque-Bera (JB): 0.203
Skew: -0.285 Prob(JB): 0.904
Kurtosis: 3.017 Cond. No. 231.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.532
Model: OLS Adj. R-squared: 0.454
Method: Least Squares F-statistic: 6.821
Date: Sun, 26 Oct 2025 Prob (F-statistic): 0.0105
Time: 03:06:57 Log-Likelihood: -69.605
No. Observations: 15 AIC: 145.2
Df Residuals: 12 BIC: 147.3
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 260.9370 132.867 1.964 0.073 -28.556 550.430
C(dose)[T.1] 37.8697 16.445 2.303 0.040 2.040 73.699
expression -39.0297 26.713 -1.461 0.170 -97.233 19.174
Omnibus: 8.631 Durbin-Watson: 1.253
Prob(Omnibus): 0.013 Jarque-Bera (JB): 5.348
Skew: -1.396 Prob(JB): 0.0690
Kurtosis: 3.875 Cond. No. 92.8

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: Sun, 26 Oct 2025 Prob (F-statistic): 0.00629
Time: 03:06:57 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: 0.325
Model: OLS Adj. R-squared: 0.273
Method: Least Squares F-statistic: 6.265
Date: Sun, 26 Oct 2025 Prob (F-statistic): 0.0264
Time: 03:06:57 Log-Likelihood: -72.350
No. Observations: 15 AIC: 148.7
Df Residuals: 13 BIC: 150.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 420.4332 130.816 3.214 0.007 137.823 703.043
expression -68.0310 27.180 -2.503 0.026 -126.749 -9.313
Omnibus: 18.967 Durbin-Watson: 2.143
Prob(Omnibus): 0.000 Jarque-Bera (JB): 18.273
Skew: -1.881 Prob(JB): 0.000108
Kurtosis: 6.885 Cond. No. 78.7