LID SPN TRAP

1460586_at

Megf8 multiple EGF-like-domains 8

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
0.020 0.889 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.653
Model: OLS Adj. R-squared: 0.598
Method: Least Squares F-statistic: 11.91
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000129
Time: 04:32:45 Log-Likelihood: -100.94
No. Observations: 23 AIC: 209.9
Df Residuals: 19 BIC: 214.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 137.4254 203.385 0.676 0.507 -288.264 563.115
C(dose)[T.1] -66.8788 275.893 -0.242 0.811 -644.330 510.572
expression -9.2101 22.499 -0.409 0.687 -56.302 37.882
expression:C(dose)[T.1] 13.4640 31.065 0.433 0.670 -51.556 78.484
Omnibus: 0.289 Durbin-Watson: 1.714
Prob(Omnibus): 0.865 Jarque-Bera (JB): 0.464
Skew: -0.030 Prob(JB): 0.793
Kurtosis: 2.307 Cond. No. 728.

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: 18.52
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.81e-05
Time: 04:32:45 Log-Likelihood: -101.05
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 73.6120 137.433 0.536 0.598 -213.067 360.291
C(dose)[T.1] 52.6117 10.158 5.179 0.000 31.423 73.800
expression -2.1475 15.196 -0.141 0.889 -33.845 29.550
Omnibus: 0.358 Durbin-Watson: 1.846
Prob(Omnibus): 0.836 Jarque-Bera (JB): 0.505
Skew: 0.047 Prob(JB): 0.777
Kurtosis: 2.280 Cond. No. 283.

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: 04:32:45 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: 0.179
Model: OLS Adj. R-squared: 0.140
Method: Least Squares F-statistic: 4.583
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0442
Time: 04:32:45 Log-Likelihood: -110.83
No. Observations: 23 AIC: 225.7
Df Residuals: 21 BIC: 227.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 451.6985 173.881 2.598 0.017 90.093 813.304
expression -41.9188 19.581 -2.141 0.044 -82.640 -1.198
Omnibus: 1.489 Durbin-Watson: 2.152
Prob(Omnibus): 0.475 Jarque-Bera (JB): 0.830
Skew: 0.465 Prob(JB): 0.660
Kurtosis: 3.003 Cond. No. 239.

CP101

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

F-statistic p-value df difference
0.039 0.847 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.490
Model: OLS Adj. R-squared: 0.352
Method: Least Squares F-statistic: 3.530
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0521
Time: 04:32:45 Log-Likelihood: -70.243
No. Observations: 15 AIC: 148.5
Df Residuals: 11 BIC: 151.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -82.6402 293.208 -0.282 0.783 -727.987 562.707
C(dose)[T.1] 444.2920 424.440 1.047 0.318 -489.894 1378.478
expression 16.1450 31.520 0.512 0.619 -53.230 85.520
expression:C(dose)[T.1] -41.6866 44.895 -0.929 0.373 -140.501 57.127
Omnibus: 1.337 Durbin-Watson: 0.962
Prob(Omnibus): 0.512 Jarque-Bera (JB): 1.101
Skew: -0.508 Prob(JB): 0.577
Kurtosis: 2.147 Cond. No. 682.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.451
Model: OLS Adj. R-squared: 0.359
Method: Least Squares F-statistic: 4.920
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0275
Time: 04:32:45 Log-Likelihood: -70.809
No. Observations: 15 AIC: 147.6
Df Residuals: 12 BIC: 149.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 108.3546 207.747 0.522 0.611 -344.287 560.996
C(dose)[T.1] 50.5094 17.065 2.960 0.012 13.328 87.691
expression -4.4030 22.316 -0.197 0.847 -53.026 44.220
Omnibus: 2.343 Durbin-Watson: 0.855
Prob(Omnibus): 0.310 Jarque-Bera (JB): 1.708
Skew: -0.786 Prob(JB): 0.426
Kurtosis: 2.488 Cond. No. 254.

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: 04:32:45 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: 0.049
Model: OLS Adj. R-squared: -0.024
Method: Least Squares F-statistic: 0.6761
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.426
Time: 04:32:45 Log-Likelihood: -74.920
No. Observations: 15 AIC: 153.8
Df Residuals: 13 BIC: 155.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -108.2113 245.709 -0.440 0.667 -639.034 422.611
expression 21.3535 25.969 0.822 0.426 -34.748 77.455
Omnibus: 0.669 Durbin-Watson: 1.422
Prob(Omnibus): 0.716 Jarque-Bera (JB): 0.603
Skew: 0.030 Prob(JB): 0.740
Kurtosis: 2.019 Cond. No. 237.