LID SPN TRAP

1450445_at

Phex phosphate regulating gene with homologies to endopeptidases on the X chromosome (hypophosphatemia, vitamin D resistant rickets)

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
0.142 0.711 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.707
Model: OLS Adj. R-squared: 0.660
Method: Least Squares F-statistic: 15.26
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.69e-05
Time: 05:20:18 Log-Likelihood: -98.999
No. Observations: 23 AIC: 206.0
Df Residuals: 19 BIC: 210.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 6.1719 36.111 0.171 0.866 -69.410 81.753
C(dose)[T.1] 176.4364 65.377 2.699 0.014 39.600 313.273
expression 11.0124 8.175 1.347 0.194 -6.098 28.123
expression:C(dose)[T.1] -28.9602 15.317 -1.891 0.074 -61.020 3.099
Omnibus: 1.857 Durbin-Watson: 2.010
Prob(Omnibus): 0.395 Jarque-Bera (JB): 1.019
Skew: -0.514 Prob(JB): 0.601
Kurtosis: 3.087 Cond. No. 85.7

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.652
Model: OLS Adj. R-squared: 0.617
Method: Least Squares F-statistic: 18.70
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.64e-05
Time: 05:20:18 Log-Likelihood: -100.98
No. Observations: 23 AIC: 208.0
Df Residuals: 20 BIC: 211.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 42.1562 32.604 1.293 0.211 -25.854 110.167
C(dose)[T.1] 53.8337 8.838 6.091 0.000 35.398 72.270
expression 2.7630 7.345 0.376 0.711 -12.558 18.084
Omnibus: 0.777 Durbin-Watson: 2.018
Prob(Omnibus): 0.678 Jarque-Bera (JB): 0.698
Skew: 0.045 Prob(JB): 0.706
Kurtosis: 2.151 Cond. No. 34.2

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: 05:20:18 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: 0.005
Model: OLS Adj. R-squared: -0.042
Method: Least Squares F-statistic: 0.1071
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.747
Time: 05:20:18 Log-Likelihood: -113.05
No. Observations: 23 AIC: 230.1
Df Residuals: 21 BIC: 232.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 96.4790 51.712 1.866 0.076 -11.063 204.021
expression -3.9199 11.976 -0.327 0.747 -28.825 20.985
Omnibus: 4.021 Durbin-Watson: 2.402
Prob(Omnibus): 0.134 Jarque-Bera (JB): 1.598
Skew: 0.218 Prob(JB): 0.450
Kurtosis: 1.784 Cond. No. 32.7

CP101

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

F-statistic p-value df difference
0.115 0.741 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.523
Model: OLS Adj. R-squared: 0.393
Method: Least Squares F-statistic: 4.021
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0371
Time: 05:20:18 Log-Likelihood: -69.747
No. Observations: 15 AIC: 147.5
Df Residuals: 11 BIC: 150.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -151.9996 187.871 -0.809 0.436 -565.501 261.502
C(dose)[T.1] 293.9145 193.032 1.523 0.156 -130.945 718.774
expression 63.0559 53.892 1.170 0.267 -55.560 181.671
expression:C(dose)[T.1] -69.3627 54.953 -1.262 0.233 -190.313 51.588
Omnibus: 1.925 Durbin-Watson: 1.358
Prob(Omnibus): 0.382 Jarque-Bera (JB): 1.379
Skew: -0.705 Prob(JB): 0.502
Kurtosis: 2.535 Cond. No. 174.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.454
Model: OLS Adj. R-squared: 0.363
Method: Least Squares F-statistic: 4.989
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0265
Time: 05:20:18 Log-Likelihood: -70.762
No. Observations: 15 AIC: 147.5
Df Residuals: 12 BIC: 149.6
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 80.1468 39.269 2.041 0.064 -5.413 165.707
C(dose)[T.1] 51.1337 16.677 3.066 0.010 14.797 87.470
expression -3.6548 10.795 -0.339 0.741 -27.175 19.866
Omnibus: 2.737 Durbin-Watson: 0.802
Prob(Omnibus): 0.254 Jarque-Bera (JB): 1.976
Skew: -0.855 Prob(JB): 0.372
Kurtosis: 2.512 Cond. No. 20.8

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: 05:20:18 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: 0.026
Model: OLS Adj. R-squared: -0.049
Method: Least Squares F-statistic: 0.3504
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.564
Time: 05:20:18 Log-Likelihood: -75.101
No. Observations: 15 AIC: 154.2
Df Residuals: 13 BIC: 155.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 64.6903 49.967 1.295 0.218 -43.257 172.638
expression 7.7012 13.010 0.592 0.564 -20.405 35.807
Omnibus: 0.426 Durbin-Watson: 1.505
Prob(Omnibus): 0.808 Jarque-Bera (JB): 0.529
Skew: 0.187 Prob(JB): 0.768
Kurtosis: 2.159 Cond. No. 20.4