LID SPN TRAP

1455401_at

Camkk2 calcium/calmodulin-dependent protein kinase kinase 2, 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
0.016 0.899 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.667
Model: OLS Adj. R-squared: 0.614
Method: Least Squares F-statistic: 12.69
Date: Thu, 21 Nov 2024 Prob (F-statistic): 8.76e-05
Time: 03:53:03 Log-Likelihood: -100.46
No. Observations: 23 AIC: 208.9
Df Residuals: 19 BIC: 213.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 239.7481 268.815 0.892 0.384 -322.888 802.384
C(dose)[T.1] -287.8500 341.163 -0.844 0.409 -1001.913 426.213
expression -18.9201 27.405 -0.690 0.498 -76.279 38.439
expression:C(dose)[T.1] 35.8161 35.645 1.005 0.328 -38.790 110.422
Omnibus: 0.012 Durbin-Watson: 1.915
Prob(Omnibus): 0.994 Jarque-Bera (JB): 0.111
Skew: -0.018 Prob(JB): 0.946
Kurtosis: 2.661 Cond. No. 1.01e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.649
Model: OLS Adj. R-squared: 0.614
Method: Least Squares F-statistic: 18.52
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.81e-05
Time: 03:53:03 Log-Likelihood: -101.05
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 32.1391 172.001 0.187 0.854 -326.648 390.927
C(dose)[T.1] 54.6748 13.616 4.015 0.001 26.272 83.078
expression 2.2505 17.529 0.128 0.899 -34.313 38.814
Omnibus: 0.318 Durbin-Watson: 1.883
Prob(Omnibus): 0.853 Jarque-Bera (JB): 0.482
Skew: 0.049 Prob(JB): 0.786
Kurtosis: 2.297 Cond. No. 379.

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: 03:53:03 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: 0.367
Model: OLS Adj. R-squared: 0.336
Method: Least Squares F-statistic: 12.16
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00220
Time: 03:53:03 Log-Likelihood: -107.85
No. Observations: 23 AIC: 219.7
Df Residuals: 21 BIC: 222.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 571.1282 141.053 4.049 0.001 277.792 864.464
expression -51.6067 14.801 -3.487 0.002 -82.387 -20.827
Omnibus: 2.434 Durbin-Watson: 2.169
Prob(Omnibus): 0.296 Jarque-Bera (JB): 1.419
Skew: 0.316 Prob(JB): 0.492
Kurtosis: 1.961 Cond. No. 237.

CP101

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

F-statistic p-value df difference
3.310 0.094 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.604
Model: OLS Adj. R-squared: 0.496
Method: Least Squares F-statistic: 5.584
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0142
Time: 03:53:03 Log-Likelihood: -68.360
No. Observations: 15 AIC: 144.7
Df Residuals: 11 BIC: 147.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -645.5025 629.915 -1.025 0.327 -2031.936 740.931
C(dose)[T.1] -1280.2715 1334.166 -0.960 0.358 -4216.751 1656.208
expression 68.4614 60.482 1.132 0.282 -64.658 201.581
expression:C(dose)[T.1] 127.2217 127.885 0.995 0.341 -154.251 408.694
Omnibus: 4.648 Durbin-Watson: 1.053
Prob(Omnibus): 0.098 Jarque-Bera (JB): 2.591
Skew: -1.008 Prob(JB): 0.274
Kurtosis: 3.281 Cond. No. 2.41e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.568
Model: OLS Adj. R-squared: 0.496
Method: Least Squares F-statistic: 7.888
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00650
Time: 03:53:03 Log-Likelihood: -69.006
No. Observations: 15 AIC: 144.0
Df Residuals: 12 BIC: 146.1
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -941.8300 554.797 -1.698 0.115 -2150.628 266.968
C(dose)[T.1] 46.9035 13.991 3.352 0.006 16.419 77.388
expression 96.9172 53.267 1.819 0.094 -19.142 212.976
Omnibus: 3.030 Durbin-Watson: 1.187
Prob(Omnibus): 0.220 Jarque-Bera (JB): 1.623
Skew: -0.805 Prob(JB): 0.444
Kurtosis: 3.062 Cond. No. 841.

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: 03:53:03 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: 0.163
Model: OLS Adj. R-squared: 0.099
Method: Least Squares F-statistic: 2.538
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.135
Time: 03:53:03 Log-Likelihood: -73.962
No. Observations: 15 AIC: 151.9
Df Residuals: 13 BIC: 153.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -1084.5085 739.571 -1.466 0.166 -2682.255 513.238
expression 113.0010 70.928 1.593 0.135 -40.230 266.232
Omnibus: 4.252 Durbin-Watson: 1.982
Prob(Omnibus): 0.119 Jarque-Bera (JB): 1.311
Skew: 0.077 Prob(JB): 0.519
Kurtosis: 1.560 Cond. No. 837.