LID SPN TRAP

1427070_at

Snx21 sorting nexin family member 21

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
0.061 0.808 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.652
Model: OLS Adj. R-squared: 0.597
Method: Least Squares F-statistic: 11.87
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000132
Time: 04:42:46 Log-Likelihood: -100.96
No. Observations: 23 AIC: 209.9
Df Residuals: 19 BIC: 214.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 33.1824 210.948 0.157 0.877 -408.337 474.702
C(dose)[T.1] 147.9361 281.426 0.526 0.605 -441.095 736.967
expression 2.5994 26.068 0.100 0.922 -51.962 57.161
expression:C(dose)[T.1] -11.1161 33.823 -0.329 0.746 -81.908 59.676
Omnibus: 0.277 Durbin-Watson: 1.813
Prob(Omnibus): 0.871 Jarque-Bera (JB): 0.457
Skew: 0.040 Prob(JB): 0.796
Kurtosis: 2.314 Cond. No. 721.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.650
Model: OLS Adj. R-squared: 0.615
Method: Least Squares F-statistic: 18.58
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.75e-05
Time: 04:42:46 Log-Likelihood: -101.03
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 86.5935 131.460 0.659 0.518 -187.626 360.813
C(dose)[T.1] 55.5397 12.508 4.440 0.000 29.449 81.630
expression -4.0038 16.235 -0.247 0.808 -37.870 29.862
Omnibus: 0.277 Durbin-Watson: 1.889
Prob(Omnibus): 0.870 Jarque-Bera (JB): 0.458
Skew: 0.068 Prob(JB): 0.795
Kurtosis: 2.322 Cond. No. 256.

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: 04:42:46 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: 0.305
Model: OLS Adj. R-squared: 0.272
Method: Least Squares F-statistic: 9.224
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00627
Time: 04:42:46 Log-Likelihood: -108.92
No. Observations: 23 AIC: 221.8
Df Residuals: 21 BIC: 224.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -316.7645 130.686 -2.424 0.024 -588.540 -44.989
expression 47.4730 15.631 3.037 0.006 14.966 79.980
Omnibus: 1.580 Durbin-Watson: 2.329
Prob(Omnibus): 0.454 Jarque-Bera (JB): 1.173
Skew: 0.318 Prob(JB): 0.556
Kurtosis: 2.095 Cond. No. 184.

CP101

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

F-statistic p-value df difference
0.066 0.802 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: 0.321
Method: Least Squares F-statistic: 3.207
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0658
Time: 04:42:46 Log-Likelihood: -70.587
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 172.7631 330.959 0.522 0.612 -555.673 901.199
C(dose)[T.1] -174.3437 402.736 -0.433 0.673 -1060.759 712.071
expression -15.7459 49.442 -0.318 0.756 -124.567 93.075
expression:C(dose)[T.1] 32.9781 59.675 0.553 0.592 -98.365 164.322
Omnibus: 3.075 Durbin-Watson: 0.704
Prob(Omnibus): 0.215 Jarque-Bera (JB): 1.980
Skew: -0.883 Prob(JB): 0.372
Kurtosis: 2.773 Cond. No. 501.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.452
Model: OLS Adj. R-squared: 0.360
Method: Least Squares F-statistic: 4.944
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0271
Time: 04:42:46 Log-Likelihood: -70.792
No. Observations: 15 AIC: 147.6
Df Residuals: 12 BIC: 149.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 21.3250 180.131 0.118 0.908 -371.146 413.796
C(dose)[T.1] 48.0254 16.347 2.938 0.012 12.408 83.643
expression 6.8918 26.872 0.256 0.802 -51.658 65.441
Omnibus: 2.763 Durbin-Watson: 0.787
Prob(Omnibus): 0.251 Jarque-Bera (JB): 1.865
Skew: -0.847 Prob(JB): 0.394
Kurtosis: 2.662 Cond. No. 160.

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: 04:42:46 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: 0.057
Model: OLS Adj. R-squared: -0.015
Method: Least Squares F-statistic: 0.7928
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.389
Time: 04:42:46 Log-Likelihood: -74.856
No. Observations: 15 AIC: 153.7
Df Residuals: 13 BIC: 155.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -102.5682 220.614 -0.465 0.650 -579.177 374.040
expression 28.9421 32.505 0.890 0.389 -41.281 99.165
Omnibus: 0.357 Durbin-Watson: 1.495
Prob(Omnibus): 0.836 Jarque-Bera (JB): 0.478
Skew: -0.017 Prob(JB): 0.787
Kurtosis: 2.126 Cond. No. 155.