LID SPN TRAP

1460455_at

Ubr3 ubiquitin protein ligase E3 component n-recognin 3

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
0.484 0.495 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.697
Model: OLS Adj. R-squared: 0.650
Method: Least Squares F-statistic: 14.59
Date: Thu, 21 Nov 2024 Prob (F-statistic): 3.61e-05
Time: 04:57:12 Log-Likelihood: -99.360
No. Observations: 23 AIC: 206.7
Df Residuals: 19 BIC: 211.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 51.3307 157.922 0.325 0.749 -279.204 381.865
C(dose)[T.1] 592.7739 346.297 1.712 0.103 -132.034 1317.581
expression 0.3023 16.579 0.018 0.986 -34.397 35.002
expression:C(dose)[T.1] -61.9772 39.106 -1.585 0.130 -143.828 19.874
Omnibus: 0.603 Durbin-Watson: 1.691
Prob(Omnibus): 0.740 Jarque-Bera (JB): 0.679
Skew: 0.230 Prob(JB): 0.712
Kurtosis: 2.295 Cond. No. 875.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.657
Model: OLS Adj. R-squared: 0.623
Method: Least Squares F-statistic: 19.18
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.23e-05
Time: 04:57:12 Log-Likelihood: -100.79
No. Observations: 23 AIC: 207.6
Df Residuals: 20 BIC: 211.0
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 157.3631 148.357 1.061 0.301 -152.104 466.830
C(dose)[T.1] 44.4580 15.424 2.882 0.009 12.284 76.632
expression -10.8365 15.572 -0.696 0.495 -43.320 21.647
Omnibus: 0.681 Durbin-Watson: 1.909
Prob(Omnibus): 0.711 Jarque-Bera (JB): 0.658
Skew: 0.035 Prob(JB): 0.720
Kurtosis: 2.175 Cond. No. 318.

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: 04:57:12 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: 0.515
Model: OLS Adj. R-squared: 0.492
Method: Least Squares F-statistic: 22.30
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000116
Time: 04:57:12 Log-Likelihood: -104.78
No. Observations: 23 AIC: 213.6
Df Residuals: 21 BIC: 215.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 517.5419 92.851 5.574 0.000 324.448 710.635
expression -47.9687 10.158 -4.722 0.000 -69.093 -26.844
Omnibus: 0.576 Durbin-Watson: 2.420
Prob(Omnibus): 0.750 Jarque-Bera (JB): 0.619
Skew: 0.318 Prob(JB): 0.734
Kurtosis: 2.508 Cond. No. 171.

CP101

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

F-statistic p-value df difference
2.693 0.127 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.557
Model: OLS Adj. R-squared: 0.436
Method: Least Squares F-statistic: 4.602
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0254
Time: 04:57:12 Log-Likelihood: -69.201
No. Observations: 15 AIC: 146.4
Df Residuals: 11 BIC: 149.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 406.8086 325.812 1.249 0.238 -310.299 1123.917
C(dose)[T.1] 224.2404 522.406 0.429 0.676 -925.568 1374.049
expression -33.5164 32.159 -1.042 0.320 -104.298 37.265
expression:C(dose)[T.1] -22.4517 54.835 -0.409 0.690 -143.143 98.239
Omnibus: 6.825 Durbin-Watson: 1.402
Prob(Omnibus): 0.033 Jarque-Bera (JB): 3.733
Skew: -1.135 Prob(JB): 0.155
Kurtosis: 3.906 Cond. No. 858.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.550
Model: OLS Adj. R-squared: 0.475
Method: Least Squares F-statistic: 7.328
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00832
Time: 04:57:12 Log-Likelihood: -69.314
No. Observations: 15 AIC: 144.6
Df Residuals: 12 BIC: 146.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 485.0016 254.655 1.905 0.081 -69.844 1039.847
C(dose)[T.1] 10.6625 27.453 0.388 0.705 -49.151 70.477
expression -41.2386 25.128 -1.641 0.127 -95.988 13.511
Omnibus: 5.401 Durbin-Watson: 1.502
Prob(Omnibus): 0.067 Jarque-Bera (JB): 2.863
Skew: -1.030 Prob(JB): 0.239
Kurtosis: 3.580 Cond. No. 352.

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: 04:57:12 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: 0.544
Model: OLS Adj. R-squared: 0.509
Method: Least Squares F-statistic: 15.52
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00170
Time: 04:57:12 Log-Likelihood: -69.408
No. Observations: 15 AIC: 142.8
Df Residuals: 13 BIC: 144.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 571.0534 121.378 4.705 0.000 308.831 833.276
expression -49.5861 12.587 -3.939 0.002 -76.780 -22.393
Omnibus: 4.249 Durbin-Watson: 1.727
Prob(Omnibus): 0.119 Jarque-Bera (JB): 2.149
Skew: -0.905 Prob(JB): 0.342
Kurtosis: 3.407 Cond. No. 172.