LID SPN TRAP

1460246_at

Mecp2 methyl CpG binding protein 2

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
6.551 0.019 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.736
Model: OLS Adj. R-squared: 0.694
Method: Least Squares F-statistic: 17.62
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.02e-05
Time: 04:46:29 Log-Likelihood: -97.804
No. Observations: 23 AIC: 203.6
Df Residuals: 19 BIC: 208.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 328.1119 217.903 1.506 0.149 -127.964 784.187
C(dose)[T.1] 46.3409 250.713 0.185 0.855 -478.407 571.089
expression -35.4968 28.231 -1.257 0.224 -94.584 23.591
expression:C(dose)[T.1] -0.3165 32.760 -0.010 0.992 -68.884 68.251
Omnibus: 0.182 Durbin-Watson: 1.954
Prob(Omnibus): 0.913 Jarque-Bera (JB): 0.319
Skew: -0.177 Prob(JB): 0.853
Kurtosis: 2.545 Cond. No. 716.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.736
Model: OLS Adj. R-squared: 0.709
Method: Least Squares F-statistic: 27.83
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.67e-06
Time: 04:46:29 Log-Likelihood: -97.804
No. Observations: 23 AIC: 201.6
Df Residuals: 20 BIC: 205.0
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 329.9255 107.848 3.059 0.006 104.958 554.893
C(dose)[T.1] 43.9202 8.454 5.195 0.000 26.286 61.555
expression -35.7318 13.960 -2.560 0.019 -64.852 -6.612
Omnibus: 0.186 Durbin-Watson: 1.953
Prob(Omnibus): 0.911 Jarque-Bera (JB): 0.324
Skew: -0.178 Prob(JB): 0.851
Kurtosis: 2.541 Cond. No. 220.

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: 04:46:29 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: 0.379
Model: OLS Adj. R-squared: 0.349
Method: Least Squares F-statistic: 12.81
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00177
Time: 04:46:29 Log-Likelihood: -107.63
No. Observations: 23 AIC: 219.3
Df Residuals: 21 BIC: 221.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 590.5007 142.820 4.135 0.000 293.491 887.510
expression -67.2947 18.801 -3.579 0.002 -106.394 -28.195
Omnibus: 0.322 Durbin-Watson: 2.194
Prob(Omnibus): 0.851 Jarque-Bera (JB): 0.489
Skew: 0.122 Prob(JB): 0.783
Kurtosis: 2.328 Cond. No. 194.

CP101

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

F-statistic p-value df difference
0.302 0.592 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.497
Model: OLS Adj. R-squared: 0.359
Method: Least Squares F-statistic: 3.618
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0489
Time: 04:46:29 Log-Likelihood: -70.151
No. Observations: 15 AIC: 148.3
Df Residuals: 11 BIC: 151.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 495.7432 422.728 1.173 0.266 -434.674 1426.161
C(dose)[T.1] -427.8957 549.934 -0.778 0.453 -1638.292 782.501
expression -52.6270 51.921 -1.014 0.333 -166.905 61.651
expression:C(dose)[T.1] 58.6560 67.708 0.866 0.405 -90.368 207.680
Omnibus: 2.308 Durbin-Watson: 0.941
Prob(Omnibus): 0.315 Jarque-Bera (JB): 1.440
Skew: -0.749 Prob(JB): 0.487
Kurtosis: 2.759 Cond. No. 799.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.462
Model: OLS Adj. R-squared: 0.373
Method: Least Squares F-statistic: 5.159
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0242
Time: 04:46:29 Log-Likelihood: -70.646
No. Observations: 15 AIC: 147.3
Df Residuals: 12 BIC: 149.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 215.0164 268.625 0.800 0.439 -370.267 800.300
C(dose)[T.1] 48.3225 15.626 3.092 0.009 14.276 82.369
expression -18.1341 32.976 -0.550 0.592 -89.984 53.715
Omnibus: 2.113 Durbin-Watson: 0.905
Prob(Omnibus): 0.348 Jarque-Bera (JB): 1.640
Skew: -0.718 Prob(JB): 0.440
Kurtosis: 2.250 Cond. No. 286.

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: 04:46:29 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: -0.040
Method: Least Squares F-statistic: 0.4552
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.512
Time: 04:46:29 Log-Likelihood: -75.042
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 324.9337 342.922 0.948 0.361 -415.905 1065.772
expression -28.5058 42.250 -0.675 0.512 -119.782 62.771
Omnibus: 2.139 Durbin-Watson: 1.655
Prob(Omnibus): 0.343 Jarque-Bera (JB): 1.096
Skew: 0.273 Prob(JB): 0.578
Kurtosis: 1.793 Cond. No. 283.