LID SPN TRAP

1458820_at

Zfp560 zinc finger protein 560

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
0.001 0.974 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.663
Model: OLS Adj. R-squared: 0.610
Method: Least Squares F-statistic: 12.48
Date: Thu, 21 Nov 2024 Prob (F-statistic): 9.71e-05
Time: 04:45:55 Log-Likelihood: -100.59
No. Observations: 23 AIC: 209.2
Df Residuals: 19 BIC: 213.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -25.2777 100.611 -0.251 0.804 -235.859 185.304
C(dose)[T.1] 158.4875 117.650 1.347 0.194 -87.756 404.731
expression 26.3100 33.241 0.791 0.438 -43.265 95.885
expression:C(dose)[T.1] -34.5134 38.481 -0.897 0.381 -115.055 46.029
Omnibus: 0.458 Durbin-Watson: 1.855
Prob(Omnibus): 0.795 Jarque-Bera (JB): 0.558
Skew: -0.048 Prob(JB): 0.757
Kurtosis: 2.243 Cond. No. 132.

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: 18.50
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.83e-05
Time: 04:45:55 Log-Likelihood: -101.06
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 52.5293 50.708 1.036 0.313 -53.246 158.305
C(dose)[T.1] 53.2774 8.950 5.953 0.000 34.607 71.947
expression 0.5558 16.664 0.033 0.974 -34.205 35.316
Omnibus: 0.314 Durbin-Watson: 1.887
Prob(Omnibus): 0.855 Jarque-Bera (JB): 0.480
Skew: 0.059 Prob(JB): 0.787
Kurtosis: 2.302 Cond. No. 39.9

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: 04:45:55 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: 0.027
Model: OLS Adj. R-squared: -0.019
Method: Least Squares F-statistic: 0.5907
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.451
Time: 04:45:55 Log-Likelihood: -112.79
No. Observations: 23 AIC: 229.6
Df Residuals: 21 BIC: 231.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 17.0736 81.816 0.209 0.837 -153.072 187.219
expression 20.3886 26.528 0.769 0.451 -34.779 75.556
Omnibus: 2.061 Durbin-Watson: 2.440
Prob(Omnibus): 0.357 Jarque-Bera (JB): 1.462
Skew: 0.404 Prob(JB): 0.481
Kurtosis: 2.066 Cond. No. 39.2

CP101

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

F-statistic p-value df difference
0.032 0.860 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.628
Model: OLS Adj. R-squared: 0.526
Method: Least Squares F-statistic: 6.177
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0102
Time: 04:45:55 Log-Likelihood: -67.893
No. Observations: 15 AIC: 143.8
Df Residuals: 11 BIC: 146.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 111.5589 55.304 2.017 0.069 -10.164 233.281
C(dose)[T.1] -219.8391 118.700 -1.852 0.091 -481.097 41.419
expression -11.3562 14.003 -0.811 0.435 -42.177 19.464
expression:C(dose)[T.1] 73.3878 32.075 2.288 0.043 2.792 143.983
Omnibus: 1.020 Durbin-Watson: 1.318
Prob(Omnibus): 0.601 Jarque-Bera (JB): 0.720
Skew: -0.074 Prob(JB): 0.698
Kurtosis: 1.937 Cond. No. 83.7

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.450
Model: OLS Adj. R-squared: 0.359
Method: Least Squares F-statistic: 4.914
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0276
Time: 04:45:55 Log-Likelihood: -70.813
No. Observations: 15 AIC: 147.6
Df Residuals: 12 BIC: 149.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 57.2025 58.089 0.985 0.344 -69.363 183.768
C(dose)[T.1] 49.8816 16.175 3.084 0.009 14.639 85.124
expression 2.6315 14.653 0.180 0.860 -29.296 34.559
Omnibus: 3.070 Durbin-Watson: 0.818
Prob(Omnibus): 0.215 Jarque-Bera (JB): 2.091
Skew: -0.899 Prob(JB): 0.352
Kurtosis: 2.662 Cond. No. 30.3

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: 04:45:55 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: 0.015
Model: OLS Adj. R-squared: -0.061
Method: Least Squares F-statistic: 0.1921
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.668
Time: 04:45:55 Log-Likelihood: -75.190
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 123.7463 69.375 1.784 0.098 -26.130 273.622
expression -8.0273 18.317 -0.438 0.668 -47.600 31.545
Omnibus: 2.065 Durbin-Watson: 1.524
Prob(Omnibus): 0.356 Jarque-Bera (JB): 1.022
Skew: 0.198 Prob(JB): 0.600
Kurtosis: 1.784 Cond. No. 27.8