LID SPN TRAP

1460206_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
1.234 0.280 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.702
Model: OLS Adj. R-squared: 0.655
Method: Least Squares F-statistic: 14.91
Date: Fri, 31 Oct 2025 Prob (F-statistic): 3.14e-05
Time: 10:58:21 Log-Likelihood: -99.189
No. Observations: 23 AIC: 206.4
Df Residuals: 19 BIC: 210.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -605.8181 379.324 -1.597 0.127 -1399.753 188.117
C(dose)[T.1] 1055.0428 702.094 1.503 0.149 -414.458 2524.543
expression 57.3273 32.943 1.740 0.098 -11.623 126.278
expression:C(dose)[T.1] -86.4669 60.197 -1.436 0.167 -212.461 39.527
Omnibus: 0.040 Durbin-Watson: 1.612
Prob(Omnibus): 0.980 Jarque-Bera (JB): 0.116
Skew: -0.066 Prob(JB): 0.944
Kurtosis: 2.678 Cond. No. 2.37e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.669
Model: OLS Adj. R-squared: 0.636
Method: Least Squares F-statistic: 20.25
Date: Fri, 31 Oct 2025 Prob (F-statistic): 1.56e-05
Time: 10:58:21 Log-Likelihood: -100.37
No. Observations: 23 AIC: 206.7
Df Residuals: 20 BIC: 210.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -307.6781 325.829 -0.944 0.356 -987.345 371.988
C(dose)[T.1] 46.6638 10.418 4.479 0.000 24.933 68.395
expression 31.4321 28.296 1.111 0.280 -27.592 90.456
Omnibus: 0.191 Durbin-Watson: 1.833
Prob(Omnibus): 0.909 Jarque-Bera (JB): 0.395
Skew: 0.098 Prob(JB): 0.821
Kurtosis: 2.388 Cond. No. 898.

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: Fri, 31 Oct 2025 Prob (F-statistic): 3.51e-06
Time: 10:58:21 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: 0.338
Model: OLS Adj. R-squared: 0.306
Method: Least Squares F-statistic: 10.71
Date: Fri, 31 Oct 2025 Prob (F-statistic): 0.00363
Time: 10:58:21 Log-Likelihood: -108.36
No. Observations: 23 AIC: 220.7
Df Residuals: 21 BIC: 223.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -1134.2499 370.910 -3.058 0.006 -1905.599 -362.901
expression 104.5187 31.930 3.273 0.004 38.116 170.921
Omnibus: 2.556 Durbin-Watson: 2.128
Prob(Omnibus): 0.279 Jarque-Bera (JB): 2.166
Skew: 0.668 Prob(JB): 0.339
Kurtosis: 2.312 Cond. No. 739.

CP101

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

F-statistic p-value df difference
0.018 0.895 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.471
Model: OLS Adj. R-squared: 0.326
Method: Least Squares F-statistic: 3.261
Date: Fri, 31 Oct 2025 Prob (F-statistic): 0.0632
Time: 10:58:21 Log-Likelihood: -70.528
No. Observations: 15 AIC: 149.1
Df Residuals: 11 BIC: 151.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -115.5390 445.913 -0.259 0.800 -1096.988 865.910
C(dose)[T.1] 441.0547 592.178 0.745 0.472 -862.321 1744.430
expression 17.2904 42.124 0.410 0.689 -75.424 110.005
expression:C(dose)[T.1] -37.1537 56.091 -0.662 0.521 -160.610 86.303
Omnibus: 4.498 Durbin-Watson: 0.924
Prob(Omnibus): 0.106 Jarque-Bera (JB): 2.587
Skew: -1.013 Prob(JB): 0.274
Kurtosis: 3.184 Cond. No. 1.07e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.450
Model: OLS Adj. R-squared: 0.358
Method: Least Squares F-statistic: 4.901
Date: Fri, 31 Oct 2025 Prob (F-statistic): 0.0278
Time: 10:58:21 Log-Likelihood: -70.822
No. Observations: 15 AIC: 147.6
Df Residuals: 12 BIC: 149.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 106.1986 287.603 0.369 0.718 -520.435 732.833
C(dose)[T.1] 48.9562 15.828 3.093 0.009 14.470 83.443
expression -3.6638 27.157 -0.135 0.895 -62.833 55.506
Omnibus: 2.942 Durbin-Watson: 0.815
Prob(Omnibus): 0.230 Jarque-Bera (JB): 1.937
Skew: -0.869 Prob(JB): 0.380
Kurtosis: 2.721 Cond. No. 391.

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: Fri, 31 Oct 2025 Prob (F-statistic): 0.00629
Time: 10:58:21 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: 0.011
Model: OLS Adj. R-squared: -0.065
Method: Least Squares F-statistic: 0.1423
Date: Fri, 31 Oct 2025 Prob (F-statistic): 0.712
Time: 10:58:21 Log-Likelihood: -75.218
No. Observations: 15 AIC: 154.4
Df Residuals: 13 BIC: 155.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 231.9700 366.714 0.633 0.538 -560.267 1024.207
expression -13.1130 34.756 -0.377 0.712 -88.199 61.973
Omnibus: 0.460 Durbin-Watson: 1.665
Prob(Omnibus): 0.794 Jarque-Bera (JB): 0.525
Skew: -0.052 Prob(JB): 0.769
Kurtosis: 2.089 Cond. No. 386.