LID SPN TRAP

1453003_at

Sorl1 sortilin-related receptor, LDLR class A repeats-containing

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
1.644 0.214 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.743
Model: OLS Adj. R-squared: 0.702
Method: Least Squares F-statistic: 18.31
Date: Thu, 21 Nov 2024 Prob (F-statistic): 7.84e-06
Time: 04:37:09 Log-Likelihood: -97.479
No. Observations: 23 AIC: 203.0
Df Residuals: 19 BIC: 207.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 273.5719 91.415 2.993 0.007 82.238 464.906
C(dose)[T.1] -314.9916 165.373 -1.905 0.072 -661.120 31.137
expression -28.3825 11.808 -2.404 0.027 -53.096 -3.669
expression:C(dose)[T.1] 47.6214 21.347 2.231 0.038 2.942 92.300
Omnibus: 1.500 Durbin-Watson: 2.178
Prob(Omnibus): 0.472 Jarque-Bera (JB): 0.613
Skew: -0.384 Prob(JB): 0.736
Kurtosis: 3.227 Cond. No. 405.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.676
Model: OLS Adj. R-squared: 0.643
Method: Least Squares F-statistic: 20.84
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.29e-05
Time: 04:37:09 Log-Likelihood: -100.15
No. Observations: 23 AIC: 206.3
Df Residuals: 20 BIC: 209.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 160.9582 83.447 1.929 0.068 -13.110 335.027
C(dose)[T.1] 53.5318 8.431 6.349 0.000 35.944 71.120
expression -13.8119 10.771 -1.282 0.214 -36.279 8.655
Omnibus: 0.532 Durbin-Watson: 2.021
Prob(Omnibus): 0.766 Jarque-Bera (JB): 0.593
Skew: 0.040 Prob(JB): 0.744
Kurtosis: 2.218 Cond. No. 156.

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: 04:37:09 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: 0.022
Model: OLS Adj. R-squared: -0.024
Method: Least Squares F-statistic: 0.4752
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.498
Time: 04:37:09 Log-Likelihood: -112.85
No. Observations: 23 AIC: 229.7
Df Residuals: 21 BIC: 232.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 177.0378 141.351 1.252 0.224 -116.918 470.993
expression -12.5809 18.250 -0.689 0.498 -50.533 25.371
Omnibus: 3.388 Durbin-Watson: 2.431
Prob(Omnibus): 0.184 Jarque-Bera (JB): 2.028
Skew: 0.497 Prob(JB): 0.363
Kurtosis: 1.937 Cond. No. 156.

CP101

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

F-statistic p-value df difference
0.004 0.951 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.467
Model: OLS Adj. R-squared: 0.322
Method: Least Squares F-statistic: 3.214
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0655
Time: 04:37:09 Log-Likelihood: -70.579
No. Observations: 15 AIC: 149.2
Df Residuals: 11 BIC: 152.0
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -21.9933 233.129 -0.094 0.927 -535.107 491.120
C(dose)[T.1] 250.8275 329.856 0.760 0.463 -475.181 976.836
expression 10.8671 28.295 0.384 0.708 -51.410 73.144
expression:C(dose)[T.1] -24.8599 40.565 -0.613 0.552 -114.143 64.424
Omnibus: 2.849 Durbin-Watson: 0.643
Prob(Omnibus): 0.241 Jarque-Bera (JB): 1.831
Skew: -0.847 Prob(JB): 0.400
Kurtosis: 2.756 Cond. No. 446.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.449
Model: OLS Adj. R-squared: 0.357
Method: Least Squares F-statistic: 4.888
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0280
Time: 04:37:09 Log-Likelihood: -70.831
No. Observations: 15 AIC: 147.7
Df Residuals: 12 BIC: 149.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 77.5334 162.845 0.476 0.643 -277.276 432.343
C(dose)[T.1] 48.9391 16.272 3.008 0.011 13.486 84.392
expression -1.2280 19.741 -0.062 0.951 -44.239 41.783
Omnibus: 2.772 Durbin-Watson: 0.822
Prob(Omnibus): 0.250 Jarque-Bera (JB): 1.891
Skew: -0.851 Prob(JB): 0.389
Kurtosis: 2.641 Cond. No. 172.

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: 04:37:09 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: 0.034
Model: OLS Adj. R-squared: -0.041
Method: Least Squares F-statistic: 0.4515
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.513
Time: 04:37:09 Log-Likelihood: -75.044
No. Observations: 15 AIC: 154.1
Df Residuals: 13 BIC: 155.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 226.1530 197.429 1.145 0.273 -200.366 652.672
expression -16.3223 24.292 -0.672 0.513 -68.802 36.157
Omnibus: 0.177 Durbin-Watson: 1.605
Prob(Omnibus): 0.915 Jarque-Bera (JB): 0.373
Skew: -0.139 Prob(JB): 0.830
Kurtosis: 2.279 Cond. No. 163.