LID SPN TRAP

1458615_at

Depdc5 DEP domain containing 5

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
2.323 0.143 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.686
Model: OLS Adj. R-squared: 0.636
Method: Least Squares F-statistic: 13.81
Date: Thu, 21 Nov 2024 Prob (F-statistic): 5.14e-05
Time: 04:34:33 Log-Likelihood: -99.796
No. Observations: 23 AIC: 207.6
Df Residuals: 19 BIC: 212.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -7.5522 54.269 -0.139 0.891 -121.138 106.033
C(dose)[T.1] 53.5928 84.735 0.632 0.535 -123.760 230.946
expression 11.0534 9.655 1.145 0.267 -9.155 31.262
expression:C(dose)[T.1] -0.9758 14.349 -0.068 0.946 -31.009 29.057
Omnibus: 0.185 Durbin-Watson: 1.923
Prob(Omnibus): 0.912 Jarque-Bera (JB): 0.259
Skew: 0.181 Prob(JB): 0.878
Kurtosis: 2.627 Cond. No. 153.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.686
Model: OLS Adj. R-squared: 0.654
Method: Least Squares F-statistic: 21.80
Date: Thu, 21 Nov 2024 Prob (F-statistic): 9.44e-06
Time: 04:34:33 Log-Likelihood: -99.799
No. Observations: 23 AIC: 205.6
Df Residuals: 20 BIC: 209.0
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -5.0836 39.324 -0.129 0.898 -87.113 76.945
C(dose)[T.1] 47.8648 9.044 5.292 0.000 28.999 66.731
expression 10.6115 6.963 1.524 0.143 -3.912 25.135
Omnibus: 0.242 Durbin-Watson: 1.926
Prob(Omnibus): 0.886 Jarque-Bera (JB): 0.264
Skew: 0.203 Prob(JB): 0.876
Kurtosis: 2.668 Cond. No. 57.7

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: 04:34:33 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: 0.245
Model: OLS Adj. R-squared: 0.209
Method: Least Squares F-statistic: 6.824
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0163
Time: 04:34:33 Log-Likelihood: -109.87
No. Observations: 23 AIC: 223.7
Df Residuals: 21 BIC: 226.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -67.5344 56.718 -1.191 0.247 -185.485 50.416
expression 25.2397 9.662 2.612 0.016 5.146 45.333
Omnibus: 2.365 Durbin-Watson: 2.509
Prob(Omnibus): 0.307 Jarque-Bera (JB): 1.178
Skew: 0.531 Prob(JB): 0.555
Kurtosis: 3.318 Cond. No. 54.6

CP101

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

F-statistic p-value df difference
1.718 0.214 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.612
Model: OLS Adj. R-squared: 0.506
Method: Least Squares F-statistic: 5.778
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0127
Time: 04:34:33 Log-Likelihood: -68.204
No. Observations: 15 AIC: 144.4
Df Residuals: 11 BIC: 147.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 29.5809 127.396 0.232 0.821 -250.815 309.977
C(dose)[T.1] -356.0681 244.281 -1.458 0.173 -893.727 181.591
expression 6.8615 23.024 0.298 0.771 -43.813 57.536
expression:C(dose)[T.1] 69.6585 42.696 1.631 0.131 -24.315 163.632
Omnibus: 1.829 Durbin-Watson: 1.111
Prob(Omnibus): 0.401 Jarque-Bera (JB): 0.924
Skew: -0.607 Prob(JB): 0.630
Kurtosis: 2.936 Cond. No. 254.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.518
Model: OLS Adj. R-squared: 0.437
Method: Least Squares F-statistic: 6.443
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0126
Time: 04:34:33 Log-Likelihood: -69.829
No. Observations: 15 AIC: 145.7
Df Residuals: 12 BIC: 147.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -82.1465 114.621 -0.717 0.487 -331.884 167.591
C(dose)[T.1] 41.7432 15.781 2.645 0.021 7.359 76.127
expression 27.1168 20.688 1.311 0.214 -17.959 72.193
Omnibus: 3.174 Durbin-Watson: 0.825
Prob(Omnibus): 0.205 Jarque-Bera (JB): 2.099
Skew: -0.906 Prob(JB): 0.350
Kurtosis: 2.730 Cond. No. 91.9

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: 04:34:33 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: 0.237
Model: OLS Adj. R-squared: 0.178
Method: Least Squares F-statistic: 4.030
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0659
Time: 04:34:33 Log-Likelihood: -73.275
No. Observations: 15 AIC: 150.5
Df Residuals: 13 BIC: 152.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -171.5374 132.399 -1.296 0.218 -457.568 114.493
expression 46.8349 23.329 2.008 0.066 -3.564 97.234
Omnibus: 0.522 Durbin-Watson: 1.836
Prob(Omnibus): 0.770 Jarque-Bera (JB): 0.463
Skew: -0.357 Prob(JB): 0.793
Kurtosis: 2.520 Cond. No. 87.3