LID SPN TRAP

1458654_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
0.460 0.506 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.674
Model: OLS Adj. R-squared: 0.622
Method: Least Squares F-statistic: 13.07
Date: Thu, 21 Nov 2024 Prob (F-statistic): 7.29e-05
Time: 03:55:48 Log-Likelihood: -100.23
No. Observations: 23 AIC: 208.5
Df Residuals: 19 BIC: 213.0
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -16.0072 60.177 -0.266 0.793 -141.959 109.945
C(dose)[T.1] 136.4530 82.863 1.647 0.116 -36.982 309.888
expression 10.2936 8.778 1.173 0.255 -8.079 28.666
expression:C(dose)[T.1] -12.3314 12.532 -0.984 0.337 -38.561 13.898
Omnibus: 0.156 Durbin-Watson: 1.998
Prob(Omnibus): 0.925 Jarque-Bera (JB): 0.170
Skew: 0.151 Prob(JB): 0.918
Kurtosis: 2.706 Cond. No. 167.

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: 19.15
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.26e-05
Time: 03:55:48 Log-Likelihood: -100.80
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 25.2630 43.119 0.586 0.565 -64.682 115.208
C(dose)[T.1] 55.4198 9.199 6.025 0.000 36.231 74.609
expression 4.2434 6.260 0.678 0.506 -8.814 17.301
Omnibus: 0.005 Durbin-Watson: 1.829
Prob(Omnibus): 0.997 Jarque-Bera (JB): 0.189
Skew: -0.008 Prob(JB): 0.910
Kurtosis: 2.556 Cond. No. 67.8

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: 03:55:48 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: 0.034
Model: OLS Adj. R-squared: -0.012
Method: Least Squares F-statistic: 0.7474
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.397
Time: 03:55:48 Log-Likelihood: -112.70
No. Observations: 23 AIC: 229.4
Df Residuals: 21 BIC: 231.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 134.7285 64.024 2.104 0.048 1.582 267.875
expression -8.3520 9.661 -0.865 0.397 -28.443 11.738
Omnibus: 1.690 Durbin-Watson: 2.495
Prob(Omnibus): 0.430 Jarque-Bera (JB): 1.096
Skew: 0.221 Prob(JB): 0.578
Kurtosis: 2.026 Cond. No. 61.2

CP101

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

F-statistic p-value df difference
0.149 0.706 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.528
Model: OLS Adj. R-squared: 0.399
Method: Least Squares F-statistic: 4.096
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0353
Time: 03:55:48 Log-Likelihood: -69.675
No. Observations: 15 AIC: 147.4
Df Residuals: 11 BIC: 150.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -3.0000 97.216 -0.031 0.976 -216.971 210.971
C(dose)[T.1] 203.2069 122.124 1.664 0.124 -65.587 472.001
expression 11.4816 15.745 0.729 0.481 -23.172 46.136
expression:C(dose)[T.1] -26.7845 20.674 -1.296 0.222 -72.288 18.719
Omnibus: 7.296 Durbin-Watson: 1.096
Prob(Omnibus): 0.026 Jarque-Bera (JB): 4.160
Skew: -1.214 Prob(JB): 0.125
Kurtosis: 3.869 Cond. No. 133.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.456
Model: OLS Adj. R-squared: 0.365
Method: Least Squares F-statistic: 5.020
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0260
Time: 03:55:48 Log-Likelihood: -70.740
No. Observations: 15 AIC: 147.5
Df Residuals: 12 BIC: 149.6
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 92.2911 65.342 1.412 0.183 -50.077 234.659
C(dose)[T.1] 46.4720 17.158 2.708 0.019 9.088 83.856
expression -4.0532 10.488 -0.386 0.706 -26.905 18.799
Omnibus: 2.863 Durbin-Watson: 0.713
Prob(Omnibus): 0.239 Jarque-Bera (JB): 1.881
Skew: -0.856 Prob(JB): 0.390
Kurtosis: 2.719 Cond. No. 50.8

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: Thu, 21 Nov 2024 Prob (F-statistic): 0.00629
Time: 03:55:48 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: 0.123
Model: OLS Adj. R-squared: 0.055
Method: Least Squares F-statistic: 1.818
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.201
Time: 03:55:48 Log-Likelihood: -74.318
No. Observations: 15 AIC: 152.6
Df Residuals: 13 BIC: 154.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 184.4902 68.022 2.712 0.018 37.538 331.442
expression -15.7256 11.662 -1.348 0.201 -40.919 9.468
Omnibus: 1.023 Durbin-Watson: 1.207
Prob(Omnibus): 0.600 Jarque-Bera (JB): 0.811
Skew: -0.281 Prob(JB): 0.667
Kurtosis: 2.009 Cond. No. 42.9