LID SPN TRAP

1425110_at

Sorcs3 sortilin-related VPS10 domain containing receptor 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
0.001 0.972 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.659
Model: OLS Adj. R-squared: 0.605
Method: Least Squares F-statistic: 12.21
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000111
Time: 03:42:43 Log-Likelihood: -100.75
No. Observations: 23 AIC: 209.5
Df Residuals: 19 BIC: 214.0
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 34.8304 57.847 0.602 0.554 -86.245 155.906
C(dose)[T.1] 133.5632 111.097 1.202 0.244 -98.965 366.091
expression 2.9567 8.777 0.337 0.740 -15.413 21.327
expression:C(dose)[T.1] -12.5773 17.346 -0.725 0.477 -48.883 23.729
Omnibus: 0.091 Durbin-Watson: 1.833
Prob(Omnibus): 0.956 Jarque-Bera (JB): 0.300
Skew: 0.084 Prob(JB): 0.861
Kurtosis: 2.466 Cond. No. 195.

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: 03:42:43 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 55.9330 49.396 1.132 0.271 -47.106 158.972
C(dose)[T.1] 53.2769 8.935 5.963 0.000 34.638 71.916
expression -0.2631 7.480 -0.035 0.972 -15.866 15.340
Omnibus: 0.317 Durbin-Watson: 1.904
Prob(Omnibus): 0.853 Jarque-Bera (JB): 0.482
Skew: 0.066 Prob(JB): 0.786
Kurtosis: 2.303 Cond. No. 74.9

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: 03:42:43 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: 0.025
Model: OLS Adj. R-squared: -0.021
Method: Least Squares F-statistic: 0.5448
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.469
Time: 03:42:43 Log-Likelihood: -112.81
No. Observations: 23 AIC: 229.6
Df Residuals: 21 BIC: 231.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 136.5099 77.275 1.767 0.092 -24.193 297.213
expression -8.8128 11.940 -0.738 0.469 -33.644 16.018
Omnibus: 2.807 Durbin-Watson: 2.697
Prob(Omnibus): 0.246 Jarque-Bera (JB): 1.389
Skew: 0.234 Prob(JB): 0.499
Kurtosis: 1.891 Cond. No. 71.9

CP101

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

F-statistic p-value df difference
0.197 0.665 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.523
Model: OLS Adj. R-squared: 0.393
Method: Least Squares F-statistic: 4.018
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0372
Time: 03:42:43 Log-Likelihood: -69.750
No. Observations: 15 AIC: 147.5
Df Residuals: 11 BIC: 150.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -18.1871 148.021 -0.123 0.904 -343.979 307.605
C(dose)[T.1] 300.8708 205.216 1.466 0.171 -150.806 752.547
expression 11.5395 19.894 0.580 0.574 -32.246 55.325
expression:C(dose)[T.1] -33.5524 27.365 -1.226 0.246 -93.783 26.678
Omnibus: 1.591 Durbin-Watson: 0.859
Prob(Omnibus): 0.451 Jarque-Bera (JB): 1.268
Skew: -0.628 Prob(JB): 0.530
Kurtosis: 2.326 Cond. No. 277.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.458
Model: OLS Adj. R-squared: 0.367
Method: Least Squares F-statistic: 5.064
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0254
Time: 03:42:43 Log-Likelihood: -70.711
No. Observations: 15 AIC: 147.4
Df Residuals: 12 BIC: 149.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 113.3721 104.082 1.089 0.297 -113.402 340.147
C(dose)[T.1] 49.9665 15.708 3.181 0.008 15.742 84.191
expression -6.1924 13.944 -0.444 0.665 -36.574 24.189
Omnibus: 2.405 Durbin-Watson: 0.869
Prob(Omnibus): 0.300 Jarque-Bera (JB): 1.680
Skew: -0.792 Prob(JB): 0.432
Kurtosis: 2.580 Cond. No. 102.

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: 03:42:43 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: -0.076
Method: Least Squares F-statistic: 0.005137
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.944
Time: 03:42:43 Log-Likelihood: -75.297
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 103.3651 135.701 0.762 0.460 -189.799 396.530
expression -1.2956 18.077 -0.072 0.944 -40.349 37.758
Omnibus: 0.505 Durbin-Watson: 1.621
Prob(Omnibus): 0.777 Jarque-Bera (JB): 0.542
Skew: 0.032 Prob(JB): 0.762
Kurtosis: 2.071 Cond. No. 102.