LID SPN TRAP

1453146_at

Gapvd1 GTPase activating protein and VPS9 domains 1

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
0.279 0.603 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.655
Model: OLS Adj. R-squared: 0.600
Method: Least Squares F-statistic: 12.02
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000122
Time: 04:37:09 Log-Likelihood: -100.87
No. Observations: 23 AIC: 209.7
Df Residuals: 19 BIC: 214.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 101.2183 86.601 1.169 0.257 -80.040 282.477
C(dose)[T.1] 22.1355 129.306 0.171 0.866 -248.504 292.775
expression -6.9876 12.840 -0.544 0.593 -33.861 19.886
expression:C(dose)[T.1] 4.6187 19.260 0.240 0.813 -35.694 44.931
Omnibus: 0.375 Durbin-Watson: 1.899
Prob(Omnibus): 0.829 Jarque-Bera (JB): 0.519
Skew: 0.093 Prob(JB): 0.771
Kurtosis: 2.288 Cond. No. 252.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.654
Model: OLS Adj. R-squared: 0.619
Method: Least Squares F-statistic: 18.89
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.47e-05
Time: 04:37:09 Log-Likelihood: -100.90
No. Observations: 23 AIC: 207.8
Df Residuals: 20 BIC: 211.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 87.4095 63.140 1.384 0.181 -44.297 219.116
C(dose)[T.1] 53.0690 8.724 6.083 0.000 34.871 71.267
expression -4.9350 9.342 -0.528 0.603 -24.423 14.553
Omnibus: 0.305 Durbin-Watson: 1.944
Prob(Omnibus): 0.858 Jarque-Bera (JB): 0.478
Skew: 0.137 Prob(JB): 0.787
Kurtosis: 2.349 Cond. No. 99.9

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: 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: 0.014
Model: OLS Adj. R-squared: -0.033
Method: Least Squares F-statistic: 0.2877
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.597
Time: 04:37:09 Log-Likelihood: -112.95
No. Observations: 23 AIC: 229.9
Df Residuals: 21 BIC: 232.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 134.9500 103.226 1.307 0.205 -79.721 349.621
expression -8.2416 15.366 -0.536 0.597 -40.197 23.714
Omnibus: 2.407 Durbin-Watson: 2.544
Prob(Omnibus): 0.300 Jarque-Bera (JB): 1.448
Skew: 0.338 Prob(JB): 0.485
Kurtosis: 1.974 Cond. No. 98.9

CP101

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

F-statistic p-value df difference
3.926 0.071 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.602
Model: OLS Adj. R-squared: 0.494
Method: Least Squares F-statistic: 5.549
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0144
Time: 04:37:09 Log-Likelihood: -68.388
No. Observations: 15 AIC: 144.8
Df Residuals: 11 BIC: 147.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 269.0100 191.642 1.404 0.188 -152.791 690.811
C(dose)[T.1] 226.5122 287.548 0.788 0.447 -406.376 859.401
expression -28.5518 27.106 -1.053 0.315 -88.211 31.107
expression:C(dose)[T.1] -29.5966 42.602 -0.695 0.502 -123.364 64.171
Omnibus: 1.261 Durbin-Watson: 1.494
Prob(Omnibus): 0.532 Jarque-Bera (JB): 0.782
Skew: -0.057 Prob(JB): 0.676
Kurtosis: 1.887 Cond. No. 364.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.585
Model: OLS Adj. R-squared: 0.515
Method: Least Squares F-statistic: 8.446
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00513
Time: 04:37:09 Log-Likelihood: -68.710
No. Observations: 15 AIC: 143.4
Df Residuals: 12 BIC: 145.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 353.5976 144.766 2.443 0.031 38.179 669.016
C(dose)[T.1] 27.1405 17.623 1.540 0.149 -11.256 65.537
expression -40.5328 20.456 -1.981 0.071 -85.102 4.037
Omnibus: 2.943 Durbin-Watson: 1.573
Prob(Omnibus): 0.230 Jarque-Bera (JB): 1.142
Skew: -0.125 Prob(JB): 0.565
Kurtosis: 1.672 Cond. No. 148.

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: 0.503
Model: OLS Adj. R-squared: 0.464
Method: Least Squares F-statistic: 13.13
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00309
Time: 04:37:09 Log-Likelihood: -70.063
No. Observations: 15 AIC: 144.1
Df Residuals: 13 BIC: 145.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 502.7853 113.114 4.445 0.001 258.417 747.153
expression -60.4313 16.675 -3.624 0.003 -96.455 -24.408
Omnibus: 1.065 Durbin-Watson: 1.801
Prob(Omnibus): 0.587 Jarque-Bera (JB): 0.768
Skew: 0.181 Prob(JB): 0.681
Kurtosis: 1.952 Cond. No. 109.