LID SPN TRAP

1452065_at

Vstm2a V-set and transmembrane domain containing 2A

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
0.363 0.553 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.679
Model: OLS Adj. R-squared: 0.628
Method: Least Squares F-statistic: 13.41
Date: Thu, 21 Nov 2024 Prob (F-statistic): 6.21e-05
Time: 03:55:56 Log-Likelihood: -100.03
No. Observations: 23 AIC: 208.1
Df Residuals: 19 BIC: 212.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 128.2575 153.901 0.833 0.415 -193.860 450.375
C(dose)[T.1] -180.6885 200.589 -0.901 0.379 -600.526 239.149
expression -9.7779 20.307 -0.482 0.636 -52.281 32.725
expression:C(dose)[T.1] 32.3094 27.201 1.188 0.250 -24.624 89.242
Omnibus: 0.533 Durbin-Watson: 1.727
Prob(Omnibus): 0.766 Jarque-Bera (JB): 0.614
Skew: 0.293 Prob(JB): 0.736
Kurtosis: 2.456 Cond. No. 465.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.655
Model: OLS Adj. R-squared: 0.621
Method: Least Squares F-statistic: 19.01
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.37e-05
Time: 03:55:56 Log-Likelihood: -100.86
No. Observations: 23 AIC: 207.7
Df Residuals: 20 BIC: 211.1
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -8.1094 103.540 -0.078 0.938 -224.090 207.871
C(dose)[T.1] 57.2289 10.826 5.286 0.000 34.646 79.812
expression 8.2289 13.649 0.603 0.553 -20.243 36.700
Omnibus: 0.217 Durbin-Watson: 2.018
Prob(Omnibus): 0.897 Jarque-Bera (JB): 0.417
Skew: 0.083 Prob(JB): 0.812
Kurtosis: 2.362 Cond. No. 179.

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: 03:55:56 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: 0.174
Model: OLS Adj. R-squared: 0.134
Method: Least Squares F-statistic: 4.416
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0479
Time: 03:55:56 Log-Likelihood: -110.91
No. Observations: 23 AIC: 225.8
Df Residuals: 21 BIC: 228.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 335.3250 121.814 2.753 0.012 82.000 588.650
expression -34.7913 16.556 -2.101 0.048 -69.222 -0.361
Omnibus: 3.972 Durbin-Watson: 1.971
Prob(Omnibus): 0.137 Jarque-Bera (JB): 2.312
Skew: 0.550 Prob(JB): 0.315
Kurtosis: 1.904 Cond. No. 139.

CP101

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

F-statistic p-value df difference
0.003 0.954 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.457
Model: OLS Adj. R-squared: 0.309
Method: Least Squares F-statistic: 3.089
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0719
Time: 03:55:56 Log-Likelihood: -70.717
No. Observations: 15 AIC: 149.4
Df Residuals: 11 BIC: 152.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 104.7886 142.835 0.734 0.479 -209.588 419.166
C(dose)[T.1] -67.1479 284.963 -0.236 0.818 -694.348 560.052
expression -6.5553 24.975 -0.262 0.798 -61.525 48.414
expression:C(dose)[T.1] 19.6741 47.934 0.410 0.689 -85.828 125.176
Omnibus: 3.397 Durbin-Watson: 0.879
Prob(Omnibus): 0.183 Jarque-Bera (JB): 2.094
Skew: -0.914 Prob(JB): 0.351
Kurtosis: 2.886 Cond. No. 258.

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: 03:55:57 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 74.3498 117.768 0.631 0.540 -182.244 330.944
C(dose)[T.1] 49.5869 17.070 2.905 0.013 12.395 86.779
expression -1.2144 20.565 -0.059 0.954 -46.022 43.593
Omnibus: 2.587 Durbin-Watson: 0.824
Prob(Omnibus): 0.274 Jarque-Bera (JB): 1.804
Skew: -0.824 Prob(JB): 0.406
Kurtosis: 2.589 Cond. No. 91.4

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: 03:55:57 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: 0.061
Model: OLS Adj. R-squared: -0.011
Method: Least Squares F-statistic: 0.8505
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.373
Time: 03:55:57 Log-Likelihood: -74.825
No. Observations: 15 AIC: 153.6
Df Residuals: 13 BIC: 155.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -35.0467 139.913 -0.250 0.806 -337.311 267.217
expression 21.9248 23.773 0.922 0.373 -29.435 73.284
Omnibus: 0.348 Durbin-Watson: 1.442
Prob(Omnibus): 0.840 Jarque-Bera (JB): 0.456
Skew: -0.275 Prob(JB): 0.796
Kurtosis: 2.347 Cond. No. 86.0