LID SPN TRAP

1419963_at

Deptor DEP domain containing MTOR-interacting protein

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
0.345 0.564 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.656
Model: OLS Adj. R-squared: 0.602
Method: Least Squares F-statistic: 12.09
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.000118
Time: 22:50:43 Log-Likelihood: -100.83
No. Observations: 23 AIC: 209.7
Df Residuals: 19 BIC: 214.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 114.4220 99.153 1.154 0.263 -93.107 321.951
C(dose)[T.1] 11.8189 160.883 0.073 0.942 -324.914 348.552
expression -10.3936 17.082 -0.608 0.550 -46.146 25.359
expression:C(dose)[T.1] 7.0851 28.164 0.252 0.804 -51.863 66.033
Omnibus: 0.278 Durbin-Watson: 1.800
Prob(Omnibus): 0.870 Jarque-Bera (JB): 0.458
Skew: 0.064 Prob(JB): 0.795
Kurtosis: 2.320 Cond. No. 262.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.655
Model: OLS Adj. R-squared: 0.621
Method: Least Squares F-statistic: 18.99
Date: Thu, 03 Apr 2025 Prob (F-statistic): 2.39e-05
Time: 22:50:43 Log-Likelihood: -100.87
No. Observations: 23 AIC: 207.7
Df Residuals: 20 BIC: 211.1
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 99.3227 77.052 1.289 0.212 -61.404 260.050
C(dose)[T.1] 52.2264 8.898 5.869 0.000 33.665 70.788
expression -7.7873 13.260 -0.587 0.564 -35.446 19.872
Omnibus: 0.316 Durbin-Watson: 1.842
Prob(Omnibus): 0.854 Jarque-Bera (JB): 0.483
Skew: 0.088 Prob(JB): 0.785
Kurtosis: 2.312 Cond. No. 105.

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, 03 Apr 2025 Prob (F-statistic): 3.51e-06
Time: 22:50:43 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: 0.061
Model: OLS Adj. R-squared: 0.016
Method: Least Squares F-statistic: 1.360
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.257
Time: 22:50:43 Log-Likelihood: -112.38
No. Observations: 23 AIC: 228.8
Df Residuals: 21 BIC: 231.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 218.9907 119.645 1.830 0.081 -29.825 467.807
expression -24.3267 20.863 -1.166 0.257 -67.713 19.059
Omnibus: 2.028 Durbin-Watson: 2.234
Prob(Omnibus): 0.363 Jarque-Bera (JB): 1.351
Skew: 0.343 Prob(JB): 0.509
Kurtosis: 2.031 Cond. No. 101.

CP101

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

F-statistic p-value df difference
4.313 0.060 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.598
Model: OLS Adj. R-squared: 0.488
Method: Least Squares F-statistic: 5.444
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0153
Time: 22:50:43 Log-Likelihood: -68.474
No. Observations: 15 AIC: 144.9
Df Residuals: 11 BIC: 147.8
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 395.2035 210.389 1.878 0.087 -67.859 858.266
C(dose)[T.1] -19.3397 292.447 -0.066 0.948 -663.011 624.331
expression -61.1511 39.204 -1.560 0.147 -147.439 25.137
expression:C(dose)[T.1] 15.2600 53.171 0.287 0.779 -101.768 132.288
Omnibus: 0.089 Durbin-Watson: 1.407
Prob(Omnibus): 0.957 Jarque-Bera (JB): 0.131
Skew: 0.112 Prob(JB): 0.937
Kurtosis: 2.601 Cond. No. 323.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.595
Model: OLS Adj. R-squared: 0.527
Method: Least Squares F-statistic: 8.797
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.00445
Time: 22:50:43 Log-Likelihood: -68.530
No. Observations: 15 AIC: 143.1
Df Residuals: 12 BIC: 145.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 350.7351 136.775 2.564 0.025 52.727 648.743
C(dose)[T.1] 64.4672 15.372 4.194 0.001 30.974 97.961
expression -52.8549 25.451 -2.077 0.060 -108.308 2.598
Omnibus: 0.284 Durbin-Watson: 1.422
Prob(Omnibus): 0.867 Jarque-Bera (JB): 0.148
Skew: 0.198 Prob(JB): 0.929
Kurtosis: 2.718 Cond. No. 117.

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, 03 Apr 2025 Prob (F-statistic): 0.00629
Time: 22:50:44 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: 0.000
Model: OLS Adj. R-squared: -0.077
Method: Least Squares F-statistic: 0.002848
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.958
Time: 22:50:44 Log-Likelihood: -75.298
No. Observations: 15 AIC: 154.6
Df Residuals: 13 BIC: 156.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 103.5890 186.206 0.556 0.587 -298.684 505.862
expression -1.7994 33.718 -0.053 0.958 -74.643 71.044
Omnibus: 0.680 Durbin-Watson: 1.630
Prob(Omnibus): 0.712 Jarque-Bera (JB): 0.612
Skew: 0.072 Prob(JB): 0.736
Kurtosis: 2.021 Cond. No. 105.