LID SPN TRAP

1443453_at

Zfp651 zinc finger protein 651

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
1.298 0.268 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.676
Model: OLS Adj. R-squared: 0.625
Method: Least Squares F-statistic: 13.23
Date: Tue, 28 Jan 2025 Prob (F-statistic): 6.74e-05
Time: 17:29:56 Log-Likelihood: -100.13
No. Observations: 23 AIC: 208.3
Df Residuals: 19 BIC: 212.8
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 82.7365 89.369 0.926 0.366 -104.315 269.788
C(dose)[T.1] 127.0774 122.475 1.038 0.312 -129.266 383.421
expression -9.0728 28.359 -0.320 0.753 -68.428 50.282
expression:C(dose)[T.1] -22.5674 38.364 -0.588 0.563 -102.864 57.729
Omnibus: 0.172 Durbin-Watson: 1.852
Prob(Omnibus): 0.918 Jarque-Bera (JB): 0.279
Skew: -0.176 Prob(JB): 0.870
Kurtosis: 2.590 Cond. No. 132.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.670
Model: OLS Adj. R-squared: 0.637
Method: Least Squares F-statistic: 20.34
Date: Tue, 28 Jan 2025 Prob (F-statistic): 1.51e-05
Time: 17:29:56 Log-Likelihood: -100.34
No. Observations: 23 AIC: 206.7
Df Residuals: 20 BIC: 210.1
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 121.5100 59.356 2.047 0.054 -2.304 245.324
C(dose)[T.1] 55.2180 8.657 6.378 0.000 37.159 73.277
expression -21.4040 18.784 -1.139 0.268 -60.587 17.779
Omnibus: 0.257 Durbin-Watson: 1.956
Prob(Omnibus): 0.879 Jarque-Bera (JB): 0.436
Skew: -0.162 Prob(JB): 0.804
Kurtosis: 2.409 Cond. No. 49.6

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: Tue, 28 Jan 2025 Prob (F-statistic): 3.51e-06
Time: 17:29:57 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: 0.000
Model: OLS Adj. R-squared: -0.048
Method: Least Squares F-statistic: 0.002113
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.964
Time: 17:29:57 Log-Likelihood: -113.10
No. Observations: 23 AIC: 230.2
Df Residuals: 21 BIC: 232.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 75.1264 100.139 0.750 0.461 -133.124 283.377
expression 1.4408 31.346 0.046 0.964 -63.746 66.627
Omnibus: 3.220 Durbin-Watson: 2.481
Prob(Omnibus): 0.200 Jarque-Bera (JB): 1.563
Skew: 0.296 Prob(JB): 0.458
Kurtosis: 1.868 Cond. No. 48.7

CP101

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

F-statistic p-value df difference
2.504 0.140 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.546
Model: OLS Adj. R-squared: 0.422
Method: Least Squares F-statistic: 4.408
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.0288
Time: 17:29:57 Log-Likelihood: -69.379
No. Observations: 15 AIC: 146.8
Df Residuals: 11 BIC: 149.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -16.3801 74.017 -0.221 0.829 -179.291 146.530
C(dose)[T.1] 23.0849 131.042 0.176 0.863 -265.337 311.507
expression 22.6043 19.746 1.145 0.277 -20.856 66.065
expression:C(dose)[T.1] 7.8234 35.749 0.219 0.831 -70.859 86.506
Omnibus: 1.166 Durbin-Watson: 0.914
Prob(Omnibus): 0.558 Jarque-Bera (JB): 0.974
Skew: -0.535 Prob(JB): 0.614
Kurtosis: 2.356 Cond. No. 85.2

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.544
Model: OLS Adj. R-squared: 0.468
Method: Least Squares F-statistic: 7.156
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.00900
Time: 17:29:57 Log-Likelihood: -69.412
No. Observations: 15 AIC: 144.8
Df Residuals: 12 BIC: 146.9
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -25.2297 59.484 -0.424 0.679 -154.835 104.375
C(dose)[T.1] 51.5741 14.395 3.583 0.004 20.209 82.939
expression 24.9911 15.794 1.582 0.140 -9.421 59.403
Omnibus: 1.258 Durbin-Watson: 0.923
Prob(Omnibus): 0.533 Jarque-Bera (JB): 1.042
Skew: -0.554 Prob(JB): 0.594
Kurtosis: 2.337 Cond. No. 33.2

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: Tue, 28 Jan 2025 Prob (F-statistic): 0.00629
Time: 17:29:57 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: 0.056
Model: OLS Adj. R-squared: -0.017
Method: Least Squares F-statistic: 0.7727
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.395
Time: 17:29:57 Log-Likelihood: -74.867
No. Observations: 15 AIC: 153.7
Df Residuals: 13 BIC: 155.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 23.8762 80.006 0.298 0.770 -148.966 196.718
expression 19.0846 21.711 0.879 0.395 -27.819 65.988
Omnibus: 2.015 Durbin-Watson: 1.737
Prob(Omnibus): 0.365 Jarque-Bera (JB): 1.061
Skew: 0.263 Prob(JB): 0.588
Kurtosis: 1.808 Cond. No. 32.0