LID SPN TRAP

1460401_at

Edem3 ER degradation enhancer, mannosidase alpha-like 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
0.000 0.985 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.665
Model: OLS Adj. R-squared: 0.612
Method: Least Squares F-statistic: 12.56
Date: Thu, 21 Nov 2024 Prob (F-statistic): 9.31e-05
Time: 04:28:10 Log-Likelihood: -100.53
No. Observations: 23 AIC: 209.1
Df Residuals: 19 BIC: 213.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 104.7421 84.979 1.233 0.233 -73.121 282.605
C(dose)[T.1] -83.7131 145.362 -0.576 0.571 -387.960 220.534
expression -6.7537 11.328 -0.596 0.558 -30.464 16.956
expression:C(dose)[T.1] 17.4434 18.440 0.946 0.356 -21.153 56.039
Omnibus: 0.378 Durbin-Watson: 1.824
Prob(Omnibus): 0.828 Jarque-Bera (JB): 0.527
Skew: 0.197 Prob(JB): 0.768
Kurtosis: 2.372 Cond. No. 323.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.649
Model: OLS Adj. R-squared: 0.614
Method: Least Squares F-statistic: 18.50
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.83e-05
Time: 04:28:10 Log-Likelihood: -101.06
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 55.4876 66.981 0.828 0.417 -84.232 195.207
C(dose)[T.1] 53.4416 10.324 5.176 0.000 31.906 74.977
expression -0.1710 8.915 -0.019 0.985 -18.767 18.425
Omnibus: 0.323 Durbin-Watson: 1.886
Prob(Omnibus): 0.851 Jarque-Bera (JB): 0.486
Skew: 0.058 Prob(JB): 0.784
Kurtosis: 2.298 Cond. No. 122.

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: 04:28:10 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: 0.179
Model: OLS Adj. R-squared: 0.140
Method: Least Squares F-statistic: 4.575
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0443
Time: 04:28:10 Log-Likelihood: -110.84
No. Observations: 23 AIC: 225.7
Df Residuals: 21 BIC: 227.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -108.2743 88.131 -1.229 0.233 -291.552 75.003
expression 24.1801 11.304 2.139 0.044 0.671 47.689
Omnibus: 0.118 Durbin-Watson: 2.401
Prob(Omnibus): 0.943 Jarque-Bera (JB): 0.339
Skew: 0.052 Prob(JB): 0.844
Kurtosis: 2.415 Cond. No. 107.

CP101

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

F-statistic p-value df difference
0.035 0.856 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.518
Model: OLS Adj. R-squared: 0.386
Method: Least Squares F-statistic: 3.937
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0393
Time: 04:28:10 Log-Likelihood: -69.830
No. Observations: 15 AIC: 147.7
Df Residuals: 11 BIC: 150.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 168.4326 112.014 1.504 0.161 -78.108 414.973
C(dose)[T.1] -183.4089 188.913 -0.971 0.352 -599.204 232.387
expression -14.8335 16.368 -0.906 0.384 -50.858 21.191
expression:C(dose)[T.1] 33.3003 26.859 1.240 0.241 -25.815 92.416
Omnibus: 1.299 Durbin-Watson: 1.139
Prob(Omnibus): 0.522 Jarque-Bera (JB): 1.088
Skew: -0.525 Prob(JB): 0.580
Kurtosis: 2.201 Cond. No. 222.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.450
Model: OLS Adj. R-squared: 0.359
Method: Least Squares F-statistic: 4.916
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0276
Time: 04:28:10 Log-Likelihood: -70.811
No. Observations: 15 AIC: 147.6
Df Residuals: 12 BIC: 149.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 84.2271 91.047 0.925 0.373 -114.148 282.602
C(dose)[T.1] 49.9790 16.271 3.072 0.010 14.529 85.430
expression -2.4670 13.265 -0.186 0.856 -31.368 26.434
Omnibus: 2.829 Durbin-Watson: 0.887
Prob(Omnibus): 0.243 Jarque-Bera (JB): 1.864
Skew: -0.851 Prob(JB): 0.394
Kurtosis: 2.710 Cond. No. 83.3

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: 04:28:10 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: 0.018
Model: OLS Adj. R-squared: -0.057
Method: Least Squares F-statistic: 0.2406
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.632
Time: 04:28:10 Log-Likelihood: -75.163
No. Observations: 15 AIC: 154.3
Df Residuals: 13 BIC: 155.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 37.3477 115.259 0.324 0.751 -211.654 286.350
expression 8.0705 16.453 0.491 0.632 -27.475 43.616
Omnibus: 0.207 Durbin-Watson: 1.536
Prob(Omnibus): 0.902 Jarque-Bera (JB): 0.399
Skew: -0.107 Prob(JB): 0.819
Kurtosis: 2.230 Cond. No. 81.8