LID SPN TRAP

1436449_at

Pcdh11x protocadherin 11 X-linked

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
0.003 0.960 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.657
Model: OLS Adj. R-squared: 0.602
Method: Least Squares F-statistic: 12.11
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000117
Time: 05:18:36 Log-Likelihood: -100.82
No. Observations: 23 AIC: 209.6
Df Residuals: 19 BIC: 214.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 18.5849 109.941 0.169 0.868 -211.525 248.695
C(dose)[T.1] 209.0827 242.931 0.861 0.400 -299.377 717.542
expression 4.7803 14.730 0.325 0.749 -26.049 35.610
expression:C(dose)[T.1] -21.6258 33.750 -0.641 0.529 -92.265 49.014
Omnibus: 0.133 Durbin-Watson: 1.874
Prob(Omnibus): 0.935 Jarque-Bera (JB): 0.351
Skew: 0.063 Prob(JB): 0.839
Kurtosis: 2.408 Cond. No. 468.

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: 18.50
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.83e-05
Time: 05:18:36 Log-Likelihood: -101.06
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 49.2824 97.485 0.506 0.619 -154.069 252.633
C(dose)[T.1] 53.5496 9.721 5.508 0.000 33.271 73.828
expression 0.6610 13.056 0.051 0.960 -26.574 27.896
Omnibus: 0.288 Durbin-Watson: 1.895
Prob(Omnibus): 0.866 Jarque-Bera (JB): 0.465
Skew: 0.062 Prob(JB): 0.793
Kurtosis: 2.315 Cond. No. 166.

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: 05:18:36 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: 0.117
Model: OLS Adj. R-squared: 0.075
Method: Least Squares F-statistic: 2.776
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.111
Time: 05:18:36 Log-Likelihood: -111.68
No. Observations: 23 AIC: 227.4
Df Residuals: 21 BIC: 229.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 301.4467 133.261 2.262 0.034 24.316 578.577
expression -30.3802 18.235 -1.666 0.111 -68.302 7.542
Omnibus: 2.329 Durbin-Watson: 2.407
Prob(Omnibus): 0.312 Jarque-Bera (JB): 1.151
Skew: 0.063 Prob(JB): 0.563
Kurtosis: 1.912 Cond. No. 146.

CP101

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

F-statistic p-value df difference
4.061 0.067 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.591
Model: OLS Adj. R-squared: 0.480
Method: Least Squares F-statistic: 5.308
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0166
Time: 05:18:36 Log-Likelihood: -68.587
No. Observations: 15 AIC: 145.2
Df Residuals: 11 BIC: 148.0
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -501.6239 433.058 -1.158 0.271 -1454.778 451.530
C(dose)[T.1] 171.0071 531.307 0.322 0.754 -998.393 1340.407
expression 73.3918 55.836 1.314 0.215 -49.503 196.287
expression:C(dose)[T.1] -19.8731 66.881 -0.297 0.772 -167.078 127.332
Omnibus: 0.749 Durbin-Watson: 0.717
Prob(Omnibus): 0.688 Jarque-Bera (JB): 0.601
Skew: -0.429 Prob(JB): 0.740
Kurtosis: 2.527 Cond. No. 899.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.588
Model: OLS Adj. R-squared: 0.520
Method: Least Squares F-statistic: 8.569
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00488
Time: 05:18:36 Log-Likelihood: -68.647
No. Observations: 15 AIC: 143.3
Df Residuals: 12 BIC: 145.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -394.2262 229.299 -1.719 0.111 -893.826 105.373
C(dose)[T.1] 13.2869 22.419 0.593 0.564 -35.560 62.134
expression 59.5405 29.545 2.015 0.067 -4.833 123.914
Omnibus: 0.744 Durbin-Watson: 0.723
Prob(Omnibus): 0.690 Jarque-Bera (JB): 0.597
Skew: -0.428 Prob(JB): 0.742
Kurtosis: 2.528 Cond. No. 279.

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: 05:18:36 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: 0.576
Model: OLS Adj. R-squared: 0.543
Method: Least Squares F-statistic: 17.67
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00103
Time: 05:18:36 Log-Likelihood: -68.863
No. Observations: 15 AIC: 141.7
Df Residuals: 13 BIC: 143.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -499.5284 141.282 -3.536 0.004 -804.750 -194.307
expression 73.4581 17.476 4.203 0.001 35.703 111.214
Omnibus: 0.132 Durbin-Watson: 0.695
Prob(Omnibus): 0.936 Jarque-Bera (JB): 0.328
Skew: -0.144 Prob(JB): 0.849
Kurtosis: 2.335 Cond. No. 175.