LID SPN TRAP

1435767_at

Scn3b sodium channel, voltage-gated, type III, beta

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
2.199 0.154 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.729
Model: OLS Adj. R-squared: 0.686
Method: Least Squares F-statistic: 17.02
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.30e-05
Time: 05:12:58 Log-Likelihood: -98.098
No. Observations: 23 AIC: 204.2
Df Residuals: 19 BIC: 208.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 48.3547 106.775 0.453 0.656 -175.128 271.837
C(dose)[T.1] -228.9025 162.166 -1.412 0.174 -568.320 110.515
expression 0.7665 13.963 0.055 0.957 -28.458 29.991
expression:C(dose)[T.1] 38.5690 21.727 1.775 0.092 -6.906 84.045
Omnibus: 0.210 Durbin-Watson: 1.634
Prob(Omnibus): 0.900 Jarque-Bera (JB): 0.413
Skew: -0.044 Prob(JB): 0.813
Kurtosis: 2.350 Cond. No. 391.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.684
Model: OLS Adj. R-squared: 0.652
Method: Least Squares F-statistic: 21.63
Date: Thu, 21 Nov 2024 Prob (F-statistic): 9.98e-06
Time: 05:12:58 Log-Likelihood: -99.863
No. Observations: 23 AIC: 205.7
Df Residuals: 20 BIC: 209.1
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -73.2942 86.174 -0.851 0.405 -253.049 106.461
C(dose)[T.1] 58.5626 9.039 6.479 0.000 39.707 77.418
expression 16.6954 11.258 1.483 0.154 -6.789 40.180
Omnibus: 0.211 Durbin-Watson: 1.896
Prob(Omnibus): 0.900 Jarque-Bera (JB): 0.406
Skew: 0.127 Prob(JB): 0.816
Kurtosis: 2.401 Cond. No. 159.

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: 05:12:58 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: 0.020
Model: OLS Adj. R-squared: -0.026
Method: Least Squares F-statistic: 0.4344
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.517
Time: 05:12:58 Log-Likelihood: -112.87
No. Observations: 23 AIC: 229.7
Df Residuals: 21 BIC: 232.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 167.6104 133.545 1.255 0.223 -110.111 445.332
expression -11.7389 17.811 -0.659 0.517 -48.778 25.300
Omnibus: 2.396 Durbin-Watson: 2.501
Prob(Omnibus): 0.302 Jarque-Bera (JB): 1.473
Skew: 0.355 Prob(JB): 0.479
Kurtosis: 1.984 Cond. No. 143.

CP101

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

F-statistic p-value df difference
1.854 0.198 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.547
Model: OLS Adj. R-squared: 0.423
Method: Least Squares F-statistic: 4.421
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0285
Time: 05:12:58 Log-Likelihood: -69.367
No. Observations: 15 AIC: 146.7
Df Residuals: 11 BIC: 149.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 137.9009 121.292 1.137 0.280 -129.060 404.862
C(dose)[T.1] 227.8885 212.926 1.070 0.307 -240.758 696.535
expression -10.1541 17.406 -0.583 0.571 -48.464 28.156
expression:C(dose)[T.1] -21.6011 28.262 -0.764 0.461 -83.805 40.602
Omnibus: 1.437 Durbin-Watson: 1.347
Prob(Omnibus): 0.487 Jarque-Bera (JB): 0.250
Skew: 0.251 Prob(JB): 0.882
Kurtosis: 3.386 Cond. No. 276.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.523
Model: OLS Adj. R-squared: 0.443
Method: Least Squares F-statistic: 6.567
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0118
Time: 05:12:58 Log-Likelihood: -69.755
No. Observations: 15 AIC: 145.5
Df Residuals: 12 BIC: 147.6
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 194.7667 94.119 2.069 0.061 -10.301 399.834
C(dose)[T.1] 65.8217 19.069 3.452 0.005 24.274 107.369
expression -18.3476 13.473 -1.362 0.198 -47.703 11.008
Omnibus: 0.150 Durbin-Watson: 1.181
Prob(Omnibus): 0.928 Jarque-Bera (JB): 0.120
Skew: -0.137 Prob(JB): 0.942
Kurtosis: 2.657 Cond. No. 98.8

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: 05:12:58 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: -0.025
Method: Least Squares F-statistic: 0.6627
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.430
Time: 05:12:58 Log-Likelihood: -74.927
No. Observations: 15 AIC: 153.9
Df Residuals: 13 BIC: 155.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 8.8340 104.681 0.084 0.934 -217.316 234.984
expression 11.4275 14.038 0.814 0.430 -18.899 41.754
Omnibus: 0.351 Durbin-Watson: 1.312
Prob(Omnibus): 0.839 Jarque-Bera (JB): 0.487
Skew: -0.172 Prob(JB): 0.784
Kurtosis: 2.187 Cond. No. 80.2