LID SPN TRAP

1436610_at

Ankrd12 ankyrin repeat domain 12

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
0.596 0.449 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.714
Model: OLS Adj. R-squared: 0.668
Method: Least Squares F-statistic: 15.77
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.16e-05
Time: 04:07:32 Log-Likelihood: -98.729
No. Observations: 23 AIC: 205.5
Df Residuals: 19 BIC: 210.0
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 89.2362 57.324 1.557 0.136 -30.744 209.216
C(dose)[T.1] -100.3392 83.258 -1.205 0.243 -274.600 73.922
expression -5.5904 9.105 -0.614 0.546 -24.647 13.466
expression:C(dose)[T.1] 26.5246 13.976 1.898 0.073 -2.727 55.776
Omnibus: 1.230 Durbin-Watson: 1.910
Prob(Omnibus): 0.541 Jarque-Bera (JB): 1.058
Skew: 0.480 Prob(JB): 0.589
Kurtosis: 2.572 Cond. No. 159.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.659
Model: OLS Adj. R-squared: 0.625
Method: Least Squares F-statistic: 19.34
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.11e-05
Time: 04:07:32 Log-Likelihood: -100.73
No. Observations: 23 AIC: 207.5
Df Residuals: 20 BIC: 210.9
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 18.7022 46.397 0.403 0.691 -78.080 115.484
C(dose)[T.1] 56.7264 9.694 5.852 0.000 36.505 76.948
expression 5.6667 7.343 0.772 0.449 -9.651 20.984
Omnibus: 1.456 Durbin-Watson: 1.719
Prob(Omnibus): 0.483 Jarque-Bera (JB): 0.921
Skew: 0.020 Prob(JB): 0.631
Kurtosis: 2.021 Cond. No. 66.9

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: 04:07:32 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: 0.076
Model: OLS Adj. R-squared: 0.032
Method: Least Squares F-statistic: 1.721
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.204
Time: 04:07:32 Log-Likelihood: -112.20
No. Observations: 23 AIC: 228.4
Df Residuals: 21 BIC: 230.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 162.2412 63.293 2.563 0.018 30.616 293.867
expression -13.8006 10.521 -1.312 0.204 -35.680 8.079
Omnibus: 3.061 Durbin-Watson: 2.402
Prob(Omnibus): 0.216 Jarque-Bera (JB): 2.086
Skew: 0.551 Prob(JB): 0.352
Kurtosis: 2.019 Cond. No. 56.4

CP101

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

F-statistic p-value df difference
0.174 0.684 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.459
Model: OLS Adj. R-squared: 0.311
Method: Least Squares F-statistic: 3.108
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0708
Time: 04:07:32 Log-Likelihood: -70.696
No. Observations: 15 AIC: 149.4
Df Residuals: 11 BIC: 152.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 195.5721 402.886 0.485 0.637 -691.173 1082.317
C(dose)[T.1] -39.0149 421.968 -0.092 0.928 -967.760 889.730
expression -18.3673 57.722 -0.318 0.756 -145.413 108.678
expression:C(dose)[T.1] 12.6263 60.453 0.209 0.838 -120.431 145.683
Omnibus: 3.030 Durbin-Watson: 0.760
Prob(Omnibus): 0.220 Jarque-Bera (JB): 2.212
Skew: -0.905 Prob(JB): 0.331
Kurtosis: 2.487 Cond. No. 587.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.457
Model: OLS Adj. R-squared: 0.366
Method: Least Squares F-statistic: 5.042
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0257
Time: 04:07:32 Log-Likelihood: -70.725
No. Observations: 15 AIC: 147.5
Df Residuals: 12 BIC: 149.6
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 115.2621 115.379 0.999 0.338 -136.126 366.651
C(dose)[T.1] 49.0518 15.631 3.138 0.009 14.995 83.109
expression -6.8562 16.457 -0.417 0.684 -42.712 29.000
Omnibus: 2.754 Durbin-Watson: 0.723
Prob(Omnibus): 0.252 Jarque-Bera (JB): 2.052
Skew: -0.857 Prob(JB): 0.358
Kurtosis: 2.413 Cond. No. 106.

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: 04:07:32 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: 0.011
Model: OLS Adj. R-squared: -0.065
Method: Least Squares F-statistic: 0.1408
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.714
Time: 04:07:32 Log-Likelihood: -75.219
No. Observations: 15 AIC: 154.4
Df Residuals: 13 BIC: 155.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 149.4123 148.908 1.003 0.334 -172.284 471.108
expression -8.0031 21.329 -0.375 0.714 -54.081 38.075
Omnibus: 0.609 Durbin-Watson: 1.670
Prob(Omnibus): 0.737 Jarque-Bera (JB): 0.584
Skew: 0.049 Prob(JB): 0.747
Kurtosis: 2.039 Cond. No. 105.