LID SPN TRAP

1419200_at

Fxyd7 FXYD domain-containing ion transport regulator 7

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
1.554 0.227 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.689
Model: OLS Adj. R-squared: 0.640
Method: Least Squares F-statistic: 14.02
Date: Tue, 03 Dec 2024 Prob (F-statistic): 4.67e-05
Time: 11:39:10 Log-Likelihood: -99.679
No. Observations: 23 AIC: 207.4
Df Residuals: 19 BIC: 211.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 206.9594 100.520 2.059 0.053 -3.432 417.350
C(dose)[T.1] -75.6071 123.249 -0.613 0.547 -333.571 182.357
expression -22.7209 14.926 -1.522 0.144 -53.962 8.520
expression:C(dose)[T.1] 18.4546 19.620 0.941 0.359 -22.610 59.519
Omnibus: 0.139 Durbin-Watson: 1.845
Prob(Omnibus): 0.933 Jarque-Bera (JB): 0.352
Skew: -0.085 Prob(JB): 0.839
Kurtosis: 2.418 Cond. No. 249.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.674
Model: OLS Adj. R-squared: 0.642
Method: Least Squares F-statistic: 20.71
Date: Tue, 03 Dec 2024 Prob (F-statistic): 1.34e-05
Time: 11:39:10 Log-Likelihood: -100.20
No. Observations: 23 AIC: 206.4
Df Residuals: 20 BIC: 209.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 135.1501 65.202 2.073 0.051 -0.859 271.159
C(dose)[T.1] 39.5778 13.901 2.847 0.010 10.582 68.574
expression -12.0397 9.659 -1.246 0.227 -32.189 8.110
Omnibus: 0.008 Durbin-Watson: 1.881
Prob(Omnibus): 0.996 Jarque-Bera (JB): 0.170
Skew: -0.037 Prob(JB): 0.919
Kurtosis: 2.586 Cond. No. 100.

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, 03 Dec 2024 Prob (F-statistic): 3.51e-06
Time: 11:39:10 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: 0.542
Model: OLS Adj. R-squared: 0.521
Method: Least Squares F-statistic: 24.89
Date: Tue, 03 Dec 2024 Prob (F-statistic): 6.16e-05
Time: 11:39:10 Log-Likelihood: -104.12
No. Observations: 23 AIC: 212.2
Df Residuals: 21 BIC: 214.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 288.9744 42.229 6.843 0.000 201.154 376.795
expression -33.8804 6.791 -4.989 0.000 -48.004 -19.757
Omnibus: 0.244 Durbin-Watson: 1.994
Prob(Omnibus): 0.885 Jarque-Bera (JB): 0.385
Skew: 0.200 Prob(JB): 0.825
Kurtosis: 2.508 Cond. No. 55.2

CP101

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

F-statistic p-value df difference
4.837 0.048 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.609
Model: OLS Adj. R-squared: 0.503
Method: Least Squares F-statistic: 5.723
Date: Tue, 03 Dec 2024 Prob (F-statistic): 0.0131
Time: 11:39:10 Log-Likelihood: -68.248
No. Observations: 15 AIC: 144.5
Df Residuals: 11 BIC: 147.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 144.3283 81.904 1.762 0.106 -35.941 324.598
C(dose)[T.1] 80.6332 100.147 0.805 0.438 -139.789 301.056
expression -17.7517 18.762 -0.946 0.364 -59.047 23.544
expression:C(dose)[T.1] -5.7624 22.456 -0.257 0.802 -55.188 43.663
Omnibus: 2.274 Durbin-Watson: 0.746
Prob(Omnibus): 0.321 Jarque-Bera (JB): 1.074
Skew: -0.212 Prob(JB): 0.584
Kurtosis: 1.759 Cond. No. 100.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.607
Model: OLS Adj. R-squared: 0.542
Method: Least Squares F-statistic: 9.273
Date: Tue, 03 Dec 2024 Prob (F-statistic): 0.00368
Time: 11:39:10 Log-Likelihood: -68.293
No. Observations: 15 AIC: 142.6
Df Residuals: 12 BIC: 144.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 161.7541 43.971 3.679 0.003 65.950 257.558
C(dose)[T.1] 55.1918 13.564 4.069 0.002 25.638 84.746
expression -21.7743 9.900 -2.199 0.048 -43.344 -0.204
Omnibus: 3.336 Durbin-Watson: 0.749
Prob(Omnibus): 0.189 Jarque-Bera (JB): 1.246
Skew: -0.196 Prob(JB): 0.536
Kurtosis: 1.644 Cond. No. 31.7

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, 03 Dec 2024 Prob (F-statistic): 0.00629
Time: 11:39:10 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: 0.065
Model: OLS Adj. R-squared: -0.007
Method: Least Squares F-statistic: 0.9057
Date: Tue, 03 Dec 2024 Prob (F-statistic): 0.359
Time: 11:39:10 Log-Likelihood: -74.795
No. Observations: 15 AIC: 153.6
Df Residuals: 13 BIC: 155.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 154.9335 65.121 2.379 0.033 14.248 295.619
expression -13.6793 14.373 -0.952 0.359 -44.731 17.373
Omnibus: 0.727 Durbin-Watson: 1.688
Prob(Omnibus): 0.695 Jarque-Bera (JB): 0.651
Skew: 0.165 Prob(JB): 0.722
Kurtosis: 2.034 Cond. No. 31.5