LID SPN TRAP

1441728_at

Scn1a sodium channel, voltage-gated, type I, alpha

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
0.043 0.837 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.652
Model: OLS Adj. R-squared: 0.597
Method: Least Squares F-statistic: 11.88
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.000131
Time: 19:23:36 Log-Likelihood: -100.96
No. Observations: 23 AIC: 209.9
Df Residuals: 19 BIC: 214.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 60.2312 74.974 0.803 0.432 -96.692 217.154
C(dose)[T.1] 141.0562 240.913 0.586 0.565 -363.180 645.292
expression -1.0397 12.898 -0.081 0.937 -28.035 25.955
expression:C(dose)[T.1] -15.2000 41.692 -0.365 0.719 -102.462 72.062
Omnibus: 0.690 Durbin-Watson: 1.845
Prob(Omnibus): 0.708 Jarque-Bera (JB): 0.683
Skew: 0.126 Prob(JB): 0.711
Kurtosis: 2.195 Cond. No. 365.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.650
Model: OLS Adj. R-squared: 0.615
Method: Least Squares F-statistic: 18.56
Date: Tue, 28 Jan 2025 Prob (F-statistic): 2.77e-05
Time: 19:23:36 Log-Likelihood: -101.04
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 68.6582 69.759 0.984 0.337 -76.857 214.173
C(dose)[T.1] 53.2854 8.764 6.080 0.000 35.004 71.567
expression -2.4943 11.996 -0.208 0.837 -27.518 22.529
Omnibus: 0.309 Durbin-Watson: 1.820
Prob(Omnibus): 0.857 Jarque-Bera (JB): 0.480
Skew: 0.098 Prob(JB): 0.787
Kurtosis: 2.320 Cond. No. 95.4

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: Tue, 28 Jan 2025 Prob (F-statistic): 3.51e-06
Time: 19:23:36 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: 0.003
Model: OLS Adj. R-squared: -0.045
Method: Least Squares F-statistic: 0.05345
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.819
Time: 19:23:36 Log-Likelihood: -113.08
No. Observations: 23 AIC: 230.2
Df Residuals: 21 BIC: 232.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 106.1246 114.447 0.927 0.364 -131.880 344.129
expression -4.5662 19.750 -0.231 0.819 -45.639 36.506
Omnibus: 3.586 Durbin-Watson: 2.462
Prob(Omnibus): 0.166 Jarque-Bera (JB): 1.638
Skew: 0.298 Prob(JB): 0.441
Kurtosis: 1.836 Cond. No. 94.7

CP101

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

F-statistic p-value df difference
0.004 0.948 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.537
Model: OLS Adj. R-squared: 0.411
Method: Least Squares F-statistic: 4.261
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.0317
Time: 19:23:36 Log-Likelihood: -69.517
No. Observations: 15 AIC: 147.0
Df Residuals: 11 BIC: 149.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -70.3041 120.530 -0.583 0.571 -335.590 194.981
C(dose)[T.1] 260.2680 146.362 1.778 0.103 -61.874 582.410
expression 22.5753 19.673 1.148 0.276 -20.725 65.876
expression:C(dose)[T.1] -34.8673 24.036 -1.451 0.175 -87.771 18.036
Omnibus: 0.533 Durbin-Watson: 1.364
Prob(Omnibus): 0.766 Jarque-Bera (JB): 0.364
Skew: -0.339 Prob(JB): 0.834
Kurtosis: 2.648 Cond. No. 174.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.449
Model: OLS Adj. R-squared: 0.357
Method: Least Squares F-statistic: 4.889
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.0280
Time: 19:23:36 Log-Likelihood: -70.830
No. Observations: 15 AIC: 147.7
Df Residuals: 12 BIC: 149.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 72.2053 72.973 0.989 0.342 -86.789 231.199
C(dose)[T.1] 49.0910 15.817 3.104 0.009 14.629 83.553
expression -0.7829 11.811 -0.066 0.948 -26.518 24.952
Omnibus: 2.515 Durbin-Watson: 0.818
Prob(Omnibus): 0.284 Jarque-Bera (JB): 1.754
Skew: -0.812 Prob(JB): 0.416
Kurtosis: 2.586 Cond. No. 58.1

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: Tue, 28 Jan 2025 Prob (F-statistic): 0.00629
Time: 19:23:36 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: 0.007
Model: OLS Adj. R-squared: -0.070
Method: Least Squares F-statistic: 0.08687
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.773
Time: 19:23:36 Log-Likelihood: -75.250
No. Observations: 15 AIC: 154.5
Df Residuals: 13 BIC: 155.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 120.6051 91.960 1.312 0.212 -78.062 319.272
expression -4.4680 15.160 -0.295 0.773 -37.218 28.282
Omnibus: 0.245 Durbin-Watson: 1.588
Prob(Omnibus): 0.885 Jarque-Bera (JB): 0.422
Skew: 0.033 Prob(JB): 0.810
Kurtosis: 2.181 Cond. No. 56.6