LID SPN TRAP

1458117_at

Ralgapa1 Ral GTPase activating protein, alpha subunit 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
0.014 0.908 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.669
Model: OLS Adj. R-squared: 0.616
Method: Least Squares F-statistic: 12.79
Date: Tue, 28 Jan 2025 Prob (F-statistic): 8.36e-05
Time: 17:59:35 Log-Likelihood: -100.40
No. Observations: 23 AIC: 208.8
Df Residuals: 19 BIC: 213.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 99.4582 55.790 1.783 0.091 -17.312 216.228
C(dose)[T.1] -39.2481 89.374 -0.439 0.666 -226.309 147.813
expression -10.5862 12.975 -0.816 0.425 -37.744 16.571
expression:C(dose)[T.1] 19.7844 18.733 1.056 0.304 -19.425 58.994
Omnibus: 0.404 Durbin-Watson: 1.916
Prob(Omnibus): 0.817 Jarque-Bera (JB): 0.534
Skew: 0.087 Prob(JB): 0.766
Kurtosis: 2.274 Cond. No. 130.

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: 18.51
Date: Tue, 28 Jan 2025 Prob (F-statistic): 2.81e-05
Time: 17:59:35 Log-Likelihood: -101.05
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 58.8884 40.575 1.451 0.162 -25.749 143.526
C(dose)[T.1] 54.2916 11.992 4.527 0.000 29.278 79.306
expression -1.0949 9.386 -0.117 0.908 -20.674 18.484
Omnibus: 0.329 Durbin-Watson: 1.873
Prob(Omnibus): 0.848 Jarque-Bera (JB): 0.489
Skew: 0.061 Prob(JB): 0.783
Kurtosis: 2.296 Cond. No. 47.0

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: Tue, 28 Jan 2025 Prob (F-statistic): 3.51e-06
Time: 17:59:35 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: 0.290
Model: OLS Adj. R-squared: 0.256
Method: Least Squares F-statistic: 8.572
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.00804
Time: 17:59:35 Log-Likelihood: -109.17
No. Observations: 23 AIC: 222.3
Df Residuals: 21 BIC: 224.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -51.1636 45.116 -1.134 0.270 -144.987 42.660
expression 27.8985 9.529 2.928 0.008 8.082 47.715
Omnibus: 0.841 Durbin-Watson: 2.160
Prob(Omnibus): 0.657 Jarque-Bera (JB): 0.727
Skew: 0.067 Prob(JB): 0.695
Kurtosis: 2.140 Cond. No. 36.7

CP101

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

F-statistic p-value df difference
1.102 0.314 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.568
Model: OLS Adj. R-squared: 0.451
Method: Least Squares F-statistic: 4.830
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.0221
Time: 17:59:35 Log-Likelihood: -68.997
No. Observations: 15 AIC: 146.0
Df Residuals: 11 BIC: 148.8
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 67.5267 58.351 1.157 0.272 -60.904 195.957
C(dose)[T.1] -108.0986 107.719 -1.004 0.337 -345.187 128.990
expression -0.0246 14.376 -0.002 0.999 -31.667 31.618
expression:C(dose)[T.1] 31.5291 23.066 1.367 0.199 -19.238 82.296
Omnibus: 1.653 Durbin-Watson: 0.942
Prob(Omnibus): 0.438 Jarque-Bera (JB): 1.309
Skew: -0.641 Prob(JB): 0.520
Kurtosis: 2.328 Cond. No. 91.2

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.495
Model: OLS Adj. R-squared: 0.411
Method: Least Squares F-statistic: 5.885
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.0166
Time: 17:59:35 Log-Likelihood: -70.174
No. Observations: 15 AIC: 146.3
Df Residuals: 12 BIC: 148.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 18.6425 47.748 0.390 0.703 -85.391 122.676
C(dose)[T.1] 36.9888 19.028 1.944 0.076 -4.470 78.447
expression 12.2240 11.642 1.050 0.314 -13.142 37.590
Omnibus: 2.636 Durbin-Watson: 1.188
Prob(Omnibus): 0.268 Jarque-Bera (JB): 2.008
Skew: -0.824 Prob(JB): 0.366
Kurtosis: 2.296 Cond. No. 31.5

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: Tue, 28 Jan 2025 Prob (F-statistic): 0.00629
Time: 17:59:35 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: 0.336
Model: OLS Adj. R-squared: 0.285
Method: Least Squares F-statistic: 6.584
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.0235
Time: 17:59:35 Log-Likelihood: -72.227
No. Observations: 15 AIC: 148.5
Df Residuals: 13 BIC: 149.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -24.1824 46.670 -0.518 0.613 -125.007 76.642
expression 26.0520 10.153 2.566 0.023 4.117 47.987
Omnibus: 1.347 Durbin-Watson: 1.784
Prob(Omnibus): 0.510 Jarque-Bera (JB): 1.050
Skew: -0.585 Prob(JB): 0.592
Kurtosis: 2.441 Cond. No. 27.1