LID SPN TRAP

1460631_at

Ogt O-linked N-acetylglucosamine (GlcNAc) transferase (UDP-N-acetylglucosamine:polypeptide-N-acetylglucosaminyl transferase)

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
0.172 0.683 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.675
Model: OLS Adj. R-squared: 0.623
Method: Least Squares F-statistic: 13.13
Date: Thu, 21 Nov 2024 Prob (F-statistic): 7.09e-05
Time: 04:56:36 Log-Likelihood: -100.20
No. Observations: 23 AIC: 208.4
Df Residuals: 19 BIC: 212.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 277.3013 211.951 1.308 0.206 -166.316 720.919
C(dose)[T.1] -321.9850 325.633 -0.989 0.335 -1003.544 359.574
expression -24.0693 22.858 -1.053 0.306 -71.912 23.773
expression:C(dose)[T.1] 41.0573 35.820 1.146 0.266 -33.915 116.030
Omnibus: 0.123 Durbin-Watson: 1.826
Prob(Omnibus): 0.940 Jarque-Bera (JB): 0.317
Skew: 0.120 Prob(JB): 0.854
Kurtosis: 2.478 Cond. No. 865.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.652
Model: OLS Adj. R-squared: 0.617
Method: Least Squares F-statistic: 18.74
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.60e-05
Time: 04:56:36 Log-Likelihood: -100.96
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 122.3355 164.506 0.744 0.466 -220.819 465.490
C(dose)[T.1] 51.0748 10.298 4.959 0.000 29.593 72.557
expression -7.3502 17.737 -0.414 0.683 -44.348 29.648
Omnibus: 0.281 Durbin-Watson: 1.841
Prob(Omnibus): 0.869 Jarque-Bera (JB): 0.459
Skew: 0.036 Prob(JB): 0.795
Kurtosis: 2.312 Cond. No. 349.

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: 04:56:36 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: 0.224
Model: OLS Adj. R-squared: 0.187
Method: Least Squares F-statistic: 6.066
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0225
Time: 04:56:37 Log-Likelihood: -110.19
No. Observations: 23 AIC: 224.4
Df Residuals: 21 BIC: 226.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 572.0905 200.016 2.860 0.009 156.135 988.046
expression -53.9790 21.917 -2.463 0.023 -99.557 -8.401
Omnibus: 2.229 Durbin-Watson: 1.903
Prob(Omnibus): 0.328 Jarque-Bera (JB): 1.673
Skew: 0.487 Prob(JB): 0.433
Kurtosis: 2.107 Cond. No. 291.

CP101

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

F-statistic p-value df difference
5.491 0.037 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.655
Model: OLS Adj. R-squared: 0.560
Method: Least Squares F-statistic: 6.948
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00686
Time: 04:56:37 Log-Likelihood: -67.328
No. Observations: 15 AIC: 142.7
Df Residuals: 11 BIC: 145.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 748.0322 405.209 1.846 0.092 -143.826 1639.890
C(dose)[T.1] 992.6383 933.452 1.063 0.310 -1061.877 3047.153
expression -72.6321 43.231 -1.680 0.121 -167.783 22.518
expression:C(dose)[T.1] -102.6052 100.504 -1.021 0.329 -323.813 118.603
Omnibus: 0.126 Durbin-Watson: 1.150
Prob(Omnibus): 0.939 Jarque-Bera (JB): 0.347
Skew: -0.020 Prob(JB): 0.841
Kurtosis: 2.256 Cond. No. 1.60e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.622
Model: OLS Adj. R-squared: 0.559
Method: Least Squares F-statistic: 9.866
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00292
Time: 04:56:37 Log-Likelihood: -68.007
No. Observations: 15 AIC: 142.0
Df Residuals: 12 BIC: 144.1
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 925.9237 366.473 2.527 0.027 127.447 1724.400
C(dose)[T.1] 39.7721 13.643 2.915 0.013 10.046 69.498
expression -91.6162 39.096 -2.343 0.037 -176.799 -6.434
Omnibus: 1.444 Durbin-Watson: 1.262
Prob(Omnibus): 0.486 Jarque-Bera (JB): 1.040
Skew: -0.394 Prob(JB): 0.594
Kurtosis: 1.979 Cond. No. 532.

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: 04:56:37 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: 0.354
Model: OLS Adj. R-squared: 0.304
Method: Least Squares F-statistic: 7.124
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0193
Time: 04:56:37 Log-Likelihood: -72.023
No. Observations: 15 AIC: 148.0
Df Residuals: 13 BIC: 149.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 1260.1093 437.088 2.883 0.013 315.839 2204.380
expression -125.2126 46.911 -2.669 0.019 -226.558 -23.867
Omnibus: 2.702 Durbin-Watson: 2.290
Prob(Omnibus): 0.259 Jarque-Bera (JB): 1.295
Skew: 0.354 Prob(JB): 0.523
Kurtosis: 1.746 Cond. No. 504.