LID SPN TRAP

1441894_s_at

Grasp GRP1 (general receptor for phosphoinositides 1)-associated scaffold 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
2.128 0.160 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.684
Model: OLS Adj. R-squared: 0.635
Method: Least Squares F-statistic: 13.74
Date: Thu, 21 Nov 2024 Prob (F-statistic): 5.31e-05
Time: 03:52:58 Log-Likelihood: -99.839
No. Observations: 23 AIC: 207.7
Df Residuals: 19 BIC: 212.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -434.2036 359.522 -1.208 0.242 -1186.692 318.285
C(dose)[T.1] 254.0912 645.381 0.394 0.698 -1096.707 1604.890
expression 41.7799 30.750 1.359 0.190 -22.581 106.141
expression:C(dose)[T.1] -17.4820 54.726 -0.319 0.753 -132.024 97.060
Omnibus: 0.149 Durbin-Watson: 1.670
Prob(Omnibus): 0.928 Jarque-Bera (JB): 0.367
Skew: -0.008 Prob(JB): 0.832
Kurtosis: 2.381 Cond. No. 2.16e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.683
Model: OLS Adj. R-squared: 0.651
Method: Least Squares F-statistic: 21.53
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.03e-05
Time: 03:52:58 Log-Likelihood: -99.900
No. Observations: 23 AIC: 205.8
Df Residuals: 20 BIC: 209.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -369.6789 290.664 -1.272 0.218 -975.993 236.635
C(dose)[T.1] 47.9472 9.120 5.258 0.000 28.924 66.971
expression 36.2603 24.859 1.459 0.160 -15.595 88.116
Omnibus: 0.074 Durbin-Watson: 1.739
Prob(Omnibus): 0.964 Jarque-Bera (JB): 0.299
Skew: 0.016 Prob(JB): 0.861
Kurtosis: 2.443 Cond. No. 828.

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: 03:52:58 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: 0.244
Model: OLS Adj. R-squared: 0.208
Method: Least Squares F-statistic: 6.793
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0165
Time: 03:52:58 Log-Likelihood: -109.88
No. Observations: 23 AIC: 223.8
Df Residuals: 21 BIC: 226.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -969.5817 402.655 -2.408 0.025 -1806.948 -132.215
expression 89.2170 34.232 2.606 0.016 18.028 160.406
Omnibus: 1.174 Durbin-Watson: 2.227
Prob(Omnibus): 0.556 Jarque-Bera (JB): 1.041
Skew: 0.343 Prob(JB): 0.594
Kurtosis: 2.215 Cond. No. 760.

CP101

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

F-statistic p-value df difference
0.138 0.717 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.468
Model: OLS Adj. R-squared: 0.323
Method: Least Squares F-statistic: 3.222
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0651
Time: 03:52:58 Log-Likelihood: -70.571
No. Observations: 15 AIC: 149.1
Df Residuals: 11 BIC: 152.0
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 253.5896 716.865 0.354 0.730 -1324.219 1831.398
C(dose)[T.1] -377.4911 832.110 -0.454 0.659 -2208.954 1453.972
expression -16.5047 63.547 -0.260 0.800 -156.372 123.362
expression:C(dose)[T.1] 37.7594 73.697 0.512 0.619 -124.447 199.966
Omnibus: 1.988 Durbin-Watson: 0.848
Prob(Omnibus): 0.370 Jarque-Bera (JB): 1.512
Skew: -0.635 Prob(JB): 0.470
Kurtosis: 2.101 Cond. No. 1.74e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.455
Model: OLS Adj. R-squared: 0.364
Method: Least Squares F-statistic: 5.010
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0262
Time: 03:52:58 Log-Likelihood: -70.747
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 -63.0742 351.854 -0.179 0.861 -829.697 703.549
C(dose)[T.1] 48.7670 15.693 3.108 0.009 14.575 82.959
expression 11.5702 31.178 0.371 0.717 -56.361 79.502
Omnibus: 2.301 Durbin-Watson: 0.764
Prob(Omnibus): 0.317 Jarque-Bera (JB): 1.749
Skew: -0.772 Prob(JB): 0.417
Kurtosis: 2.357 Cond. No. 514.

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: 03:52:58 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: 0.016
Model: OLS Adj. R-squared: -0.059
Method: Least Squares F-statistic: 0.2175
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.649
Time: 03:52:58 Log-Likelihood: -75.176
No. Observations: 15 AIC: 154.4
Df Residuals: 13 BIC: 155.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -117.8020 453.572 -0.260 0.799 -1097.685 862.081
expression 18.7156 40.133 0.466 0.649 -67.985 105.417
Omnibus: 0.230 Durbin-Watson: 1.577
Prob(Omnibus): 0.891 Jarque-Bera (JB): 0.414
Skew: 0.038 Prob(JB): 0.813
Kurtosis: 2.190 Cond. No. 513.