LID SPN TRAP

1460219_at

Mag myelin-associated glycoprotein

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
0.123 0.730 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.661
Model: OLS Adj. R-squared: 0.607
Method: Least Squares F-statistic: 12.34
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000104
Time: 05:12:01 Log-Likelihood: -100.67
No. Observations: 23 AIC: 209.3
Df Residuals: 19 BIC: 213.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 65.9028 38.257 1.723 0.101 -14.170 145.976
C(dose)[T.1] 14.1122 53.303 0.265 0.794 -97.452 125.676
expression -2.6237 8.473 -0.310 0.760 -20.358 15.110
expression:C(dose)[T.1] 8.4692 11.491 0.737 0.470 -15.583 32.521
Omnibus: 0.073 Durbin-Watson: 1.834
Prob(Omnibus): 0.964 Jarque-Bera (JB): 0.057
Skew: -0.034 Prob(JB): 0.972
Kurtosis: 2.767 Cond. No. 77.4

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.651
Model: OLS Adj. R-squared: 0.616
Method: Least Squares F-statistic: 18.67
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.67e-05
Time: 05:12:01 Log-Likelihood: -100.99
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 45.3812 25.934 1.750 0.095 -8.716 99.478
C(dose)[T.1] 52.8371 8.859 5.964 0.000 34.357 71.317
expression 1.9804 5.658 0.350 0.730 -9.822 13.783
Omnibus: 0.031 Durbin-Watson: 1.983
Prob(Omnibus): 0.985 Jarque-Bera (JB): 0.231
Skew: 0.048 Prob(JB): 0.891
Kurtosis: 2.518 Cond. No. 29.0

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: 05:12:01 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: 0.031
Model: OLS Adj. R-squared: -0.015
Method: Least Squares F-statistic: 0.6677
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.423
Time: 05:12:01 Log-Likelihood: -112.74
No. Observations: 23 AIC: 229.5
Df Residuals: 21 BIC: 231.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 45.7372 42.187 1.084 0.291 -41.996 133.470
expression 7.4226 9.084 0.817 0.423 -11.468 26.313
Omnibus: 2.844 Durbin-Watson: 2.624
Prob(Omnibus): 0.241 Jarque-Bera (JB): 1.364
Skew: 0.207 Prob(JB): 0.506
Kurtosis: 1.881 Cond. No. 28.8

CP101

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

F-statistic p-value df difference
5.874 0.032 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.630
Model: OLS Adj. R-squared: 0.529
Method: Least Squares F-statistic: 6.242
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00987
Time: 05:12:01 Log-Likelihood: -67.844
No. Observations: 15 AIC: 143.7
Df Residuals: 11 BIC: 146.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 212.6049 81.726 2.601 0.025 32.728 392.482
C(dose)[T.1] 41.9545 124.402 0.337 0.742 -231.852 315.761
expression -24.5356 13.712 -1.789 0.101 -54.715 5.644
expression:C(dose)[T.1] 0.5952 21.224 0.028 0.978 -46.118 47.309
Omnibus: 11.363 Durbin-Watson: 1.099
Prob(Omnibus): 0.003 Jarque-Bera (JB): 7.470
Skew: -1.465 Prob(JB): 0.0239
Kurtosis: 4.834 Cond. No. 143.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.630
Model: OLS Adj. R-squared: 0.568
Method: Least Squares F-statistic: 10.21
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00257
Time: 05:12:01 Log-Likelihood: -67.845
No. Observations: 15 AIC: 141.7
Df Residuals: 12 BIC: 143.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 211.1350 60.036 3.517 0.004 80.327 341.943
C(dose)[T.1] 45.4223 12.990 3.497 0.004 17.119 73.725
expression -24.2872 10.021 -2.424 0.032 -46.121 -2.454
Omnibus: 11.411 Durbin-Watson: 1.095
Prob(Omnibus): 0.003 Jarque-Bera (JB): 7.517
Skew: -1.468 Prob(JB): 0.0233
Kurtosis: 4.845 Cond. No. 56.6

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: 05:12:01 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: 0.253
Model: OLS Adj. R-squared: 0.195
Method: Least Squares F-statistic: 4.400
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0561
Time: 05:12:01 Log-Likelihood: -73.114
No. Observations: 15 AIC: 150.2
Df Residuals: 13 BIC: 151.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 259.8657 79.719 3.260 0.006 87.643 432.088
expression -28.4876 13.581 -2.098 0.056 -57.828 0.853
Omnibus: 4.834 Durbin-Watson: 2.308
Prob(Omnibus): 0.089 Jarque-Bera (JB): 1.424
Skew: 0.163 Prob(JB): 0.491
Kurtosis: 1.526 Cond. No. 54.8