LID SPN TRAP

1460436_at

Ndst1 N-deacetylase/N-sulfotransferase (heparan glucosaminyl) 1

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
0.026 0.873 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.656
Model: OLS Adj. R-squared: 0.602
Method: Least Squares F-statistic: 12.09
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000118
Time: 05:15:18 Log-Likelihood: -100.83
No. Observations: 23 AIC: 209.7
Df Residuals: 19 BIC: 214.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 132.2839 138.637 0.954 0.352 -157.887 422.455
C(dose)[T.1] -57.1606 181.194 -0.315 0.756 -436.404 322.083
expression -9.3616 16.607 -0.564 0.580 -44.120 25.397
expression:C(dose)[T.1] 13.3812 22.008 0.608 0.550 -32.682 59.444
Omnibus: 0.388 Durbin-Watson: 1.746
Prob(Omnibus): 0.824 Jarque-Bera (JB): 0.522
Skew: 0.051 Prob(JB): 0.770
Kurtosis: 2.269 Cond. No. 454.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.650
Model: OLS Adj. R-squared: 0.614
Method: Least Squares F-statistic: 18.53
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.80e-05
Time: 05:15:18 Log-Likelihood: -101.05
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 68.7408 89.647 0.767 0.452 -118.259 255.740
C(dose)[T.1] 52.8598 9.243 5.719 0.000 33.579 72.141
expression -1.7425 10.724 -0.162 0.873 -24.113 20.628
Omnibus: 0.306 Durbin-Watson: 1.861
Prob(Omnibus): 0.858 Jarque-Bera (JB): 0.476
Skew: 0.074 Prob(JB): 0.788
Kurtosis: 2.311 Cond. No. 171.

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: 05:15:18 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: 0.076
Model: OLS Adj. R-squared: 0.032
Method: Least Squares F-statistic: 1.738
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.202
Time: 05:15:18 Log-Likelihood: -112.19
No. Observations: 23 AIC: 228.4
Df Residuals: 21 BIC: 230.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 254.0274 132.418 1.918 0.069 -21.352 529.407
expression -21.2341 16.109 -1.318 0.202 -54.734 12.266
Omnibus: 1.649 Durbin-Watson: 2.435
Prob(Omnibus): 0.438 Jarque-Bera (JB): 1.428
Skew: 0.487 Prob(JB): 0.490
Kurtosis: 2.264 Cond. No. 159.

CP101

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

F-statistic p-value df difference
5.249 0.041 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.694
Model: OLS Adj. R-squared: 0.610
Method: Least Squares F-statistic: 8.299
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00363
Time: 05:15:18 Log-Likelihood: -66.429
No. Observations: 15 AIC: 140.9
Df Residuals: 11 BIC: 143.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 437.3859 271.137 1.613 0.135 -159.383 1034.155
C(dose)[T.1] 990.6817 567.449 1.746 0.109 -258.265 2239.629
expression -41.3546 30.292 -1.365 0.199 -108.026 25.317
expression:C(dose)[T.1] -105.7549 63.587 -1.663 0.124 -245.710 34.200
Omnibus: 1.209 Durbin-Watson: 0.868
Prob(Omnibus): 0.546 Jarque-Bera (JB): 0.936
Skew: -0.553 Prob(JB): 0.626
Kurtosis: 2.476 Cond. No. 1.00e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.617
Model: OLS Adj. R-squared: 0.553
Method: Least Squares F-statistic: 9.646
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00318
Time: 05:15:18 Log-Likelihood: -68.112
No. Observations: 15 AIC: 142.2
Df Residuals: 12 BIC: 144.3
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 652.0873 255.376 2.553 0.025 95.671 1208.504
C(dose)[T.1] 47.1547 13.158 3.584 0.004 18.485 75.825
expression -65.3544 28.526 -2.291 0.041 -127.508 -3.201
Omnibus: 1.276 Durbin-Watson: 1.210
Prob(Omnibus): 0.528 Jarque-Bera (JB): 0.832
Skew: -0.189 Prob(JB): 0.660
Kurtosis: 1.910 Cond. No. 353.

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: 05:15:18 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: 0.206
Model: OLS Adj. R-squared: 0.145
Method: Least Squares F-statistic: 3.375
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0892
Time: 05:15:19 Log-Likelihood: -73.569
No. Observations: 15 AIC: 151.1
Df Residuals: 13 BIC: 152.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 739.0582 351.426 2.103 0.056 -20.151 1498.267
expression -72.2778 39.343 -1.837 0.089 -157.274 12.718
Omnibus: 6.225 Durbin-Watson: 2.343
Prob(Omnibus): 0.044 Jarque-Bera (JB): 1.617
Skew: 0.217 Prob(JB): 0.445
Kurtosis: 1.451 Cond. No. 351.