LID SPN TRAP

1423937_at

Kctd5 potassium channel tetramerisation domain containing 5

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
0.034 0.856 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: 0.599
Method: Least Squares F-statistic: 11.94
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000127
Time: 04:06:45 Log-Likelihood: -100.92
No. Observations: 23 AIC: 209.8
Df Residuals: 19 BIC: 214.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 80.1019 61.135 1.310 0.206 -47.855 208.059
C(dose)[T.1] 8.6284 98.948 0.087 0.931 -198.472 215.729
expression -5.0387 11.835 -0.426 0.675 -29.810 19.733
expression:C(dose)[T.1] 8.6260 18.937 0.456 0.654 -31.009 48.261
Omnibus: 0.168 Durbin-Watson: 1.832
Prob(Omnibus): 0.920 Jarque-Bera (JB): 0.380
Skew: 0.060 Prob(JB): 0.827
Kurtosis: 2.382 Cond. No. 148.

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: 18.54
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.79e-05
Time: 04:06:46 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 62.7864 46.922 1.338 0.196 -35.091 160.664
C(dose)[T.1] 53.5140 8.815 6.071 0.000 35.127 71.901
expression -1.6692 9.054 -0.184 0.856 -20.556 17.217
Omnibus: 0.297 Durbin-Watson: 1.881
Prob(Omnibus): 0.862 Jarque-Bera (JB): 0.471
Skew: 0.082 Prob(JB): 0.790
Kurtosis: 2.318 Cond. No. 58.2

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: 04:06:46 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: 0.004
Model: OLS Adj. R-squared: -0.043
Method: Least Squares F-statistic: 0.08478
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.774
Time: 04:06:46 Log-Likelihood: -113.06
No. Observations: 23 AIC: 230.1
Df Residuals: 21 BIC: 232.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 57.3388 77.193 0.743 0.466 -103.192 217.870
expression 4.3122 14.810 0.291 0.774 -26.486 35.110
Omnibus: 3.269 Durbin-Watson: 2.509
Prob(Omnibus): 0.195 Jarque-Bera (JB): 1.510
Skew: 0.254 Prob(JB): 0.470
Kurtosis: 1.852 Cond. No. 57.9

CP101

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

F-statistic p-value df difference
0.100 0.757 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.454
Model: OLS Adj. R-squared: 0.305
Method: Least Squares F-statistic: 3.046
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0742
Time: 04:06:46 Log-Likelihood: -70.764
No. Observations: 15 AIC: 149.5
Df Residuals: 11 BIC: 152.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 46.7199 202.988 0.230 0.822 -400.054 493.494
C(dose)[T.1] 28.6747 245.073 0.117 0.909 -510.727 568.077
expression 3.4181 33.447 0.102 0.920 -70.197 77.034
expression:C(dose)[T.1] 3.9872 41.508 0.096 0.925 -87.372 95.347
Omnibus: 2.430 Durbin-Watson: 0.794
Prob(Omnibus): 0.297 Jarque-Bera (JB): 1.739
Skew: -0.800 Prob(JB): 0.419
Kurtosis: 2.530 Cond. No. 255.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.453
Model: OLS Adj. R-squared: 0.362
Method: Least Squares F-statistic: 4.976
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0267
Time: 04:06:46 Log-Likelihood: -70.771
No. Observations: 15 AIC: 147.5
Df Residuals: 12 BIC: 149.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 31.0355 115.512 0.269 0.793 -220.644 282.715
C(dose)[T.1] 52.1450 18.232 2.860 0.014 12.421 91.869
expression 6.0069 18.972 0.317 0.757 -35.330 47.344
Omnibus: 2.524 Durbin-Watson: 0.837
Prob(Omnibus): 0.283 Jarque-Bera (JB): 1.785
Skew: -0.815 Prob(JB): 0.410
Kurtosis: 2.557 Cond. No. 89.0

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: 04:06:46 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: 0.081
Model: OLS Adj. R-squared: 0.010
Method: Least Squares F-statistic: 1.141
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.305
Time: 04:06:46 Log-Likelihood: -74.669
No. Observations: 15 AIC: 153.3
Df Residuals: 13 BIC: 154.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 219.5083 118.201 1.857 0.086 -35.850 474.866
expression -21.7091 20.322 -1.068 0.305 -65.611 22.193
Omnibus: 0.358 Durbin-Watson: 1.333
Prob(Omnibus): 0.836 Jarque-Bera (JB): 0.479
Skew: 0.040 Prob(JB): 0.787
Kurtosis: 2.128 Cond. No. 72.6