LID SPN TRAP

1439985_at

Abcc12 ATP-binding cassette, sub-family C (CFTR/MRP), member 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
2.438 0.134 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.688
Model: OLS Adj. R-squared: 0.638
Method: Least Squares F-statistic: 13.95
Date: Thu, 21 Nov 2024 Prob (F-statistic): 4.83e-05
Time: 04:03:57 Log-Likelihood: -99.722
No. Observations: 23 AIC: 207.4
Df Residuals: 19 BIC: 212.0
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -2.6090 44.652 -0.058 0.954 -96.066 90.848
C(dose)[T.1] 67.4368 68.046 0.991 0.334 -74.986 209.859
expression 10.5240 8.199 1.284 0.215 -6.636 27.685
expression:C(dose)[T.1] -2.2134 12.868 -0.172 0.865 -29.147 24.720
Omnibus: 0.114 Durbin-Watson: 2.611
Prob(Omnibus): 0.945 Jarque-Bera (JB): 0.331
Skew: -0.071 Prob(JB): 0.847
Kurtosis: 2.430 Cond. No. 110.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.687
Model: OLS Adj. R-squared: 0.656
Method: Least Squares F-statistic: 21.97
Date: Thu, 21 Nov 2024 Prob (F-statistic): 8.97e-06
Time: 04:03:57 Log-Likelihood: -99.740
No. Observations: 23 AIC: 205.5
Df Residuals: 20 BIC: 208.9
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 2.2419 33.767 0.066 0.948 -68.196 72.680
C(dose)[T.1] 55.8275 8.432 6.621 0.000 38.239 73.416
expression 9.6255 6.164 1.562 0.134 -3.232 22.483
Omnibus: 0.065 Durbin-Watson: 2.600
Prob(Omnibus): 0.968 Jarque-Bera (JB): 0.282
Skew: -0.052 Prob(JB): 0.868
Kurtosis: 2.468 Cond. No. 45.2

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: 04:03:57 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: 0.002
Model: OLS Adj. R-squared: -0.046
Method: Least Squares F-statistic: 0.03262
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.858
Time: 04:03:57 Log-Likelihood: -113.09
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 69.6626 56.134 1.241 0.228 -47.075 186.400
expression 1.9061 10.553 0.181 0.858 -20.041 23.853
Omnibus: 3.515 Durbin-Watson: 2.554
Prob(Omnibus): 0.172 Jarque-Bera (JB): 1.621
Skew: 0.296 Prob(JB): 0.445
Kurtosis: 1.841 Cond. No. 42.8

CP101

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

F-statistic p-value df difference
0.624 0.445 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: 4.442
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0282
Time: 04:03:57 Log-Likelihood: -69.348
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 38.5235 85.228 0.452 0.660 -149.062 226.109
C(dose)[T.1] 211.7006 123.109 1.720 0.113 -59.259 482.661
expression 5.6998 16.669 0.342 0.739 -30.988 42.387
expression:C(dose)[T.1] -31.4856 23.832 -1.321 0.213 -83.940 20.969
Omnibus: 1.641 Durbin-Watson: 0.681
Prob(Omnibus): 0.440 Jarque-Bera (JB): 1.221
Skew: -0.502 Prob(JB): 0.543
Kurtosis: 2.027 Cond. No. 117.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.476
Model: OLS Adj. R-squared: 0.389
Method: Least Squares F-statistic: 5.451
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0207
Time: 04:03:57 Log-Likelihood: -70.453
No. Observations: 15 AIC: 146.9
Df Residuals: 12 BIC: 149.0
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 116.6313 63.265 1.844 0.090 -21.211 254.473
C(dose)[T.1] 50.2620 15.405 3.263 0.007 16.698 83.826
expression -9.7023 12.278 -0.790 0.445 -36.453 17.049
Omnibus: 2.160 Durbin-Watson: 0.832
Prob(Omnibus): 0.340 Jarque-Bera (JB): 1.626
Skew: -0.668 Prob(JB): 0.444
Kurtosis: 2.096 Cond. No. 44.5

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: 04:03:57 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: 0.1473
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.707
Time: 04:03:57 Log-Likelihood: -75.216
No. Observations: 15 AIC: 154.4
Df Residuals: 13 BIC: 155.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 125.4486 83.423 1.504 0.157 -54.777 305.674
expression -6.1955 16.143 -0.384 0.707 -41.070 28.679
Omnibus: 0.694 Durbin-Watson: 1.608
Prob(Omnibus): 0.707 Jarque-Bera (JB): 0.611
Skew: -0.004 Prob(JB): 0.737
Kurtosis: 2.011 Cond. No. 44.2