LID SPN TRAP

1460589_at

Zfp597 zinc finger protein 597

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
0.015 0.904 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.650
Model: OLS Adj. R-squared: 0.595
Method: Least Squares F-statistic: 11.76
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000140
Time: 05:09:14 Log-Likelihood: -101.04
No. Observations: 23 AIC: 210.1
Df Residuals: 19 BIC: 214.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 51.9846 152.101 0.342 0.736 -266.367 370.336
C(dose)[T.1] 97.5188 249.783 0.390 0.701 -425.283 620.321
expression 0.3192 21.816 0.015 0.988 -45.343 45.981
expression:C(dose)[T.1] -6.2574 35.516 -0.176 0.862 -80.594 68.079
Omnibus: 0.340 Durbin-Watson: 1.876
Prob(Omnibus): 0.843 Jarque-Bera (JB): 0.496
Skew: 0.068 Prob(JB): 0.780
Kurtosis: 2.293 Cond. No. 490.

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: 05:09:14 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 68.4315 117.140 0.584 0.566 -175.918 312.781
C(dose)[T.1] 53.5405 8.925 5.999 0.000 34.924 72.157
expression -2.0418 16.793 -0.122 0.904 -37.072 32.988
Omnibus: 0.413 Durbin-Watson: 1.873
Prob(Omnibus): 0.813 Jarque-Bera (JB): 0.535
Skew: 0.052 Prob(JB): 0.765
Kurtosis: 2.260 Cond. No. 192.

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: 05:09:14 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: 0.018
Model: OLS Adj. R-squared: -0.028
Method: Least Squares F-statistic: 0.3910
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.538
Time: 05:09:14 Log-Likelihood: -112.89
No. Observations: 23 AIC: 229.8
Df Residuals: 21 BIC: 232.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -38.4125 189.046 -0.203 0.841 -431.555 354.730
expression 16.8425 26.934 0.625 0.538 -39.170 72.855
Omnibus: 2.893 Durbin-Watson: 2.507
Prob(Omnibus): 0.235 Jarque-Bera (JB): 1.522
Skew: 0.313 Prob(JB): 0.467
Kurtosis: 1.906 Cond. No. 189.

CP101

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

F-statistic p-value df difference
0.331 0.576 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.509
Model: OLS Adj. R-squared: 0.375
Method: Least Squares F-statistic: 3.797
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0432
Time: 05:09:14 Log-Likelihood: -69.969
No. Observations: 15 AIC: 147.9
Df Residuals: 11 BIC: 150.8
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 281.5234 208.551 1.350 0.204 -177.495 740.542
C(dose)[T.1] -339.2483 387.126 -0.876 0.400 -1191.307 512.811
expression -29.8552 29.039 -1.028 0.326 -93.770 34.060
expression:C(dose)[T.1] 54.0026 53.680 1.006 0.336 -64.147 172.152
Omnibus: 1.799 Durbin-Watson: 0.871
Prob(Omnibus): 0.407 Jarque-Bera (JB): 1.395
Skew: -0.603 Prob(JB): 0.498
Kurtosis: 2.119 Cond. No. 449.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.464
Model: OLS Adj. R-squared: 0.374
Method: Least Squares F-statistic: 5.185
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0238
Time: 05:09:14 Log-Likelihood: -70.629
No. Observations: 15 AIC: 147.3
Df Residuals: 12 BIC: 149.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 168.1944 175.596 0.958 0.357 -214.397 550.786
C(dose)[T.1] 49.8871 15.574 3.203 0.008 15.955 83.819
expression -14.0517 24.436 -0.575 0.576 -67.292 39.189
Omnibus: 3.914 Durbin-Watson: 0.878
Prob(Omnibus): 0.141 Jarque-Bera (JB): 2.169
Skew: -0.928 Prob(JB): 0.338
Kurtosis: 3.156 Cond. No. 167.

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: 05:09:14 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: 0.005
Model: OLS Adj. R-squared: -0.072
Method: Least Squares F-statistic: 0.06320
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.805
Time: 05:09:14 Log-Likelihood: -75.264
No. Observations: 15 AIC: 154.5
Df Residuals: 13 BIC: 155.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 151.3534 229.680 0.659 0.521 -344.841 647.547
expression -8.0150 31.881 -0.251 0.805 -76.889 60.859
Omnibus: 0.988 Durbin-Watson: 1.690
Prob(Omnibus): 0.610 Jarque-Bera (JB): 0.718
Skew: 0.107 Prob(JB): 0.698
Kurtosis: 1.950 Cond. No. 166.