LID SPN TRAP

1457983_s_at

Rwdd4a RWD domain containing 4A

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
0.348 0.562 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.661
Model: OLS Adj. R-squared: 0.607
Method: Least Squares F-statistic: 12.32
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.000105
Time: 23:03:50 Log-Likelihood: -100.68
No. Observations: 23 AIC: 209.4
Df Residuals: 19 BIC: 213.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 51.5162 365.460 0.141 0.889 -713.401 816.434
C(dose)[T.1] 335.7855 505.349 0.664 0.514 -721.923 1393.494
expression 0.2622 35.593 0.007 0.994 -74.235 74.760
expression:C(dose)[T.1] -27.0035 48.781 -0.554 0.586 -129.102 75.095
Omnibus: 0.916 Durbin-Watson: 1.849
Prob(Omnibus): 0.633 Jarque-Bera (JB): 0.830
Skew: 0.223 Prob(JB): 0.661
Kurtosis: 2.184 Cond. No. 1.57e+03

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: 18.99
Date: Thu, 03 Apr 2025 Prob (F-statistic): 2.38e-05
Time: 23:03:51 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 199.1117 245.574 0.811 0.427 -313.146 711.369
C(dose)[T.1] 56.0940 9.870 5.683 0.000 35.506 76.682
expression -14.1145 23.913 -0.590 0.562 -63.997 35.768
Omnibus: 0.926 Durbin-Watson: 1.975
Prob(Omnibus): 0.629 Jarque-Bera (JB): 0.844
Skew: 0.238 Prob(JB): 0.656
Kurtosis: 2.191 Cond. No. 592.

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, 03 Apr 2025 Prob (F-statistic): 3.51e-06
Time: 23:03:51 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: 0.098
Model: OLS Adj. R-squared: 0.055
Method: Least Squares F-statistic: 2.281
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.146
Time: 23:03:51 Log-Likelihood: -111.92
No. Observations: 23 AIC: 227.8
Df Residuals: 21 BIC: 230.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -440.3933 344.478 -1.278 0.215 -1156.774 275.988
expression 50.2053 33.245 1.510 0.146 -18.932 119.342
Omnibus: 0.868 Durbin-Watson: 2.095
Prob(Omnibus): 0.648 Jarque-Bera (JB): 0.754
Skew: 0.120 Prob(JB): 0.686
Kurtosis: 2.146 Cond. No. 526.

CP101

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

F-statistic p-value df difference
0.274 0.610 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.502
Model: OLS Adj. R-squared: 0.367
Method: Least Squares F-statistic: 3.700
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0462
Time: 23:03:51 Log-Likelihood: -70.067
No. Observations: 15 AIC: 148.1
Df Residuals: 11 BIC: 151.0
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 840.7965 712.186 1.181 0.263 -726.713 2408.306
C(dose)[T.1] -748.6165 835.317 -0.896 0.389 -2587.137 1089.904
expression -76.4452 70.389 -1.086 0.301 -231.369 78.479
expression:C(dose)[T.1] 78.8727 82.659 0.954 0.360 -103.058 260.803
Omnibus: 1.886 Durbin-Watson: 0.700
Prob(Omnibus): 0.389 Jarque-Bera (JB): 1.252
Skew: -0.686 Prob(JB): 0.535
Kurtosis: 2.651 Cond. No. 1.60e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.461
Model: OLS Adj. R-squared: 0.371
Method: Least Squares F-statistic: 5.134
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0245
Time: 23:03:51 Log-Likelihood: -70.664
No. Observations: 15 AIC: 147.3
Df Residuals: 12 BIC: 149.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 262.1823 372.104 0.705 0.495 -548.562 1072.927
C(dose)[T.1] 48.3001 15.657 3.085 0.009 14.187 82.413
expression -19.2509 36.764 -0.524 0.610 -99.353 60.852
Omnibus: 2.129 Durbin-Watson: 0.791
Prob(Omnibus): 0.345 Jarque-Bera (JB): 1.314
Skew: -0.715 Prob(JB): 0.518
Kurtosis: 2.759 Cond. No. 489.

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, 03 Apr 2025 Prob (F-statistic): 0.00629
Time: 23:03:51 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: 0.4532
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.513
Time: 23:03:51 Log-Likelihood: -75.043
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 413.0780 474.567 0.870 0.400 -612.161 1438.317
expression -31.6506 47.015 -0.673 0.513 -133.219 69.918
Omnibus: 1.085 Durbin-Watson: 1.531
Prob(Omnibus): 0.581 Jarque-Bera (JB): 0.830
Skew: 0.278 Prob(JB): 0.660
Kurtosis: 1.991 Cond. No. 484.