LID SPN TRAP

1457325_at

Kcns2 K+ voltage-gated channel, subfamily S, 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
0.096 0.760 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.683
Model: OLS Adj. R-squared: 0.633
Method: Least Squares F-statistic: 13.67
Date: Thu, 21 Nov 2024 Prob (F-statistic): 5.48e-05
Time: 03:42:03 Log-Likelihood: -99.878
No. Observations: 23 AIC: 207.8
Df Residuals: 19 BIC: 212.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -41.8212 165.703 -0.252 0.803 -388.641 304.998
C(dose)[T.1] 429.6015 270.158 1.590 0.128 -135.847 995.050
expression 11.2746 19.442 0.580 0.569 -29.419 51.968
expression:C(dose)[T.1] -45.7362 32.649 -1.401 0.177 -114.071 22.598
Omnibus: 0.085 Durbin-Watson: 1.914
Prob(Omnibus): 0.958 Jarque-Bera (JB): 0.310
Skew: 0.028 Prob(JB): 0.857
Kurtosis: 2.434 Cond. No. 651.

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: 18.63
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.70e-05
Time: 03:42:03 Log-Likelihood: -101.01
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 96.3226 136.331 0.707 0.488 -188.058 380.703
C(dose)[T.1] 51.4308 10.703 4.805 0.000 29.105 73.757
expression -4.9445 15.990 -0.309 0.760 -38.300 28.411
Omnibus: 0.417 Durbin-Watson: 1.961
Prob(Omnibus): 0.812 Jarque-Bera (JB): 0.542
Skew: 0.090 Prob(JB): 0.763
Kurtosis: 2.270 Cond. No. 265.

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: 03:42:03 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: 0.247
Model: OLS Adj. R-squared: 0.212
Method: Least Squares F-statistic: 6.906
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0157
Time: 03:42:03 Log-Likelihood: -109.84
No. Observations: 23 AIC: 223.7
Df Residuals: 21 BIC: 225.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 489.7389 156.154 3.136 0.005 164.998 814.479
expression -49.2048 18.724 -2.628 0.016 -88.144 -10.266
Omnibus: 2.760 Durbin-Watson: 2.460
Prob(Omnibus): 0.252 Jarque-Bera (JB): 1.236
Skew: 0.040 Prob(JB): 0.539
Kurtosis: 1.867 Cond. No. 211.

CP101

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

F-statistic p-value df difference
1.964 0.186 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.548
Model: OLS Adj. R-squared: 0.424
Method: Least Squares F-statistic: 4.438
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0282
Time: 03:42:03 Log-Likelihood: -69.351
No. Observations: 15 AIC: 146.7
Df Residuals: 11 BIC: 149.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -460.8728 376.931 -1.223 0.247 -1290.493 368.747
C(dose)[T.1] 387.4617 474.239 0.817 0.431 -656.331 1431.254
expression 57.3967 40.934 1.402 0.188 -32.699 147.492
expression:C(dose)[T.1] -36.9233 51.339 -0.719 0.487 -149.921 76.074
Omnibus: 2.389 Durbin-Watson: 0.747
Prob(Omnibus): 0.303 Jarque-Bera (JB): 1.810
Skew: -0.789 Prob(JB): 0.405
Kurtosis: 2.364 Cond. No. 844.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.526
Model: OLS Adj. R-squared: 0.447
Method: Least Squares F-statistic: 6.667
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0113
Time: 03:42:03 Log-Likelihood: -69.696
No. Observations: 15 AIC: 145.4
Df Residuals: 12 BIC: 147.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -244.8167 223.036 -1.098 0.294 -730.770 241.136
C(dose)[T.1] 46.5604 14.711 3.165 0.008 14.507 78.614
expression 33.9235 24.204 1.402 0.186 -18.812 86.659
Omnibus: 2.765 Durbin-Watson: 0.676
Prob(Omnibus): 0.251 Jarque-Bera (JB): 2.093
Skew: -0.852 Prob(JB): 0.351
Kurtosis: 2.334 Cond. No. 287.

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: 03:42:03 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: 0.131
Model: OLS Adj. R-squared: 0.064
Method: Least Squares F-statistic: 1.958
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.185
Time: 03:42:03 Log-Likelihood: -74.248
No. Observations: 15 AIC: 152.5
Df Residuals: 13 BIC: 153.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -310.5317 288.994 -1.075 0.302 -934.866 313.803
expression 43.7168 31.240 1.399 0.185 -23.773 111.207
Omnibus: 3.044 Durbin-Watson: 1.654
Prob(Omnibus): 0.218 Jarque-Bera (JB): 1.294
Skew: 0.300 Prob(JB): 0.524
Kurtosis: 1.692 Cond. No. 286.