LID SPN TRAP

1453029_s_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
0.463 0.504 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.666
Model: OLS Adj. R-squared: 0.614
Method: Least Squares F-statistic: 12.64
Date: Thu, 21 Nov 2024 Prob (F-statistic): 8.97e-05
Time: 05:12:16 Log-Likelihood: -100.49
No. Observations: 23 AIC: 209.0
Df Residuals: 19 BIC: 213.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 88.3428 245.516 0.360 0.723 -425.528 602.214
C(dose)[T.1] 342.5238 411.785 0.832 0.416 -519.351 1204.399
expression -3.2013 23.018 -0.139 0.891 -51.379 44.977
expression:C(dose)[T.1] -29.2122 40.346 -0.724 0.478 -113.658 55.234
Omnibus: 0.360 Durbin-Watson: 1.876
Prob(Omnibus): 0.835 Jarque-Bera (JB): 0.505
Skew: 0.036 Prob(JB): 0.777
Kurtosis: 2.278 Cond. No. 1.18e+03

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): 2.25e-05
Time: 05:12:16 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 189.7283 199.255 0.952 0.352 -225.910 605.367
C(dose)[T.1] 44.5941 15.501 2.877 0.009 12.260 76.928
expression -12.7096 18.678 -0.680 0.504 -51.672 26.253
Omnibus: 0.438 Durbin-Watson: 1.960
Prob(Omnibus): 0.803 Jarque-Bera (JB): 0.548
Skew: 0.053 Prob(JB): 0.760
Kurtosis: 2.251 Cond. No. 482.

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: 05:12:16 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: 05:12:16 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 671.3690 125.380 5.355 0.000 410.626 932.112
expression -57.2539 12.123 -4.723 0.000 -82.466 -32.042
Omnibus: 0.344 Durbin-Watson: 2.484
Prob(Omnibus): 0.842 Jarque-Bera (JB): 0.496
Skew: -0.031 Prob(JB): 0.780
Kurtosis: 2.283 Cond. No. 260.

CP101

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

F-statistic p-value df difference
1.549 0.237 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.524
Model: OLS Adj. R-squared: 0.394
Method: Least Squares F-statistic: 4.038
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0367
Time: 05:12:16 Log-Likelihood: -69.731
No. Observations: 15 AIC: 147.5
Df Residuals: 11 BIC: 150.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 710.6804 496.769 1.431 0.180 -382.700 1804.061
C(dose)[T.1] -428.4646 825.784 -0.519 0.614 -2246.003 1389.074
expression -58.1936 44.930 -1.295 0.222 -157.085 40.697
expression:C(dose)[T.1] 41.9641 78.725 0.533 0.605 -131.308 215.236
Omnibus: 3.730 Durbin-Watson: 1.441
Prob(Omnibus): 0.155 Jarque-Bera (JB): 1.908
Skew: -0.863 Prob(JB): 0.385
Kurtosis: 3.268 Cond. No. 1.43e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.512
Model: OLS Adj. R-squared: 0.430
Method: Least Squares F-statistic: 6.290
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0135
Time: 05:12:16 Log-Likelihood: -69.923
No. Observations: 15 AIC: 145.8
Df Residuals: 12 BIC: 148.0
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 559.5895 395.611 1.414 0.183 -302.373 1421.552
C(dose)[T.1] 11.3244 33.845 0.335 0.744 -62.417 85.066
expression -44.5248 35.777 -1.245 0.237 -122.476 33.426
Omnibus: 6.623 Durbin-Watson: 1.328
Prob(Omnibus): 0.036 Jarque-Bera (JB): 3.497
Skew: -1.068 Prob(JB): 0.174
Kurtosis: 4.016 Cond. No. 575.

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: 05:12:16 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: 0.507
Model: OLS Adj. R-squared: 0.469
Method: Least Squares F-statistic: 13.38
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00289
Time: 05:12:16 Log-Likelihood: -69.992
No. Observations: 15 AIC: 144.0
Df Residuals: 13 BIC: 145.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 679.7212 160.367 4.239 0.001 333.269 1026.173
expression -55.2882 15.114 -3.658 0.003 -87.940 -22.636
Omnibus: 6.564 Durbin-Watson: 1.488
Prob(Omnibus): 0.038 Jarque-Bera (JB): 3.391
Skew: -1.016 Prob(JB): 0.184
Kurtosis: 4.139 Cond. No. 241.