LID SPN TRAP

1460369_at

Zfp958 zinc finger protein 958

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
0.462 0.504 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.661
Model: OLS Adj. R-squared: 0.607
Method: Least Squares F-statistic: 12.33
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000105
Time: 05:26:48 Log-Likelihood: -100.68
No. Observations: 23 AIC: 209.4
Df Residuals: 19 BIC: 213.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 41.0040 53.678 0.764 0.454 -71.346 153.354
C(dose)[T.1] 17.5167 83.869 0.209 0.837 -158.024 193.057
expression 2.4999 10.096 0.248 0.807 -18.632 23.632
expression:C(dose)[T.1] 7.3608 16.380 0.449 0.658 -26.922 41.644
Omnibus: 0.098 Durbin-Watson: 1.793
Prob(Omnibus): 0.952 Jarque-Bera (JB): 0.253
Skew: 0.129 Prob(JB): 0.881
Kurtosis: 2.556 Cond. No. 125.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.657
Model: OLS Adj. R-squared: 0.623
Method: Least Squares F-statistic: 19.15
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.25e-05
Time: 05:26:48 Log-Likelihood: -100.80
No. Observations: 23 AIC: 207.6
Df Residuals: 20 BIC: 211.0
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 26.2317 41.580 0.631 0.535 -60.504 112.967
C(dose)[T.1] 54.9799 9.001 6.108 0.000 36.205 73.755
expression 5.2967 7.790 0.680 0.504 -10.953 21.546
Omnibus: 0.089 Durbin-Watson: 1.709
Prob(Omnibus): 0.957 Jarque-Bera (JB): 0.310
Skew: 0.050 Prob(JB): 0.856
Kurtosis: 2.440 Cond. No. 51.7

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: 05:26:48 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: 0.017
Model: OLS Adj. R-squared: -0.030
Method: Least Squares F-statistic: 0.3638
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.553
Time: 05:26:48 Log-Likelihood: -112.91
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 118.1009 64.042 1.844 0.079 -15.081 251.283
expression -7.4770 12.397 -0.603 0.553 -33.258 18.304
Omnibus: 2.828 Durbin-Watson: 2.570
Prob(Omnibus): 0.243 Jarque-Bera (JB): 1.485
Skew: 0.299 Prob(JB): 0.476
Kurtosis: 1.908 Cond. No. 47.9

CP101

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

F-statistic p-value df difference
0.608 0.451 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.478
Model: OLS Adj. R-squared: 0.336
Method: Least Squares F-statistic: 3.360
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0588
Time: 05:26:48 Log-Likelihood: -70.422
No. Observations: 15 AIC: 148.8
Df Residuals: 11 BIC: 151.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 18.9749 73.210 0.259 0.800 -142.159 180.109
C(dose)[T.1] 70.2100 99.449 0.706 0.495 -148.676 289.096
expression 11.0924 16.545 0.670 0.516 -25.323 47.508
expression:C(dose)[T.1] -5.2945 21.695 -0.244 0.812 -53.045 42.456
Omnibus: 3.002 Durbin-Watson: 0.746
Prob(Omnibus): 0.223 Jarque-Bera (JB): 2.155
Skew: -0.899 Prob(JB): 0.341
Kurtosis: 2.535 Cond. No. 83.1

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.475
Model: OLS Adj. R-squared: 0.388
Method: Least Squares F-statistic: 5.437
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0209
Time: 05:26:48 Log-Likelihood: -70.462
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 32.4257 46.259 0.701 0.497 -68.364 133.215
C(dose)[T.1] 46.2751 15.806 2.928 0.013 11.838 80.712
expression 8.0131 10.274 0.780 0.451 -14.372 30.398
Omnibus: 2.832 Durbin-Watson: 0.735
Prob(Omnibus): 0.243 Jarque-Bera (JB): 2.072
Skew: -0.872 Prob(JB): 0.355
Kurtosis: 2.476 Cond. No. 29.4

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: 05:26:48 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: 0.101
Model: OLS Adj. R-squared: 0.031
Method: Least Squares F-statistic: 1.454
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.249
Time: 05:26:48 Log-Likelihood: -74.505
No. Observations: 15 AIC: 153.0
Df Residuals: 13 BIC: 154.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 24.5807 58.094 0.423 0.679 -100.924 150.085
expression 15.1417 12.556 1.206 0.249 -11.984 42.268
Omnibus: 0.456 Durbin-Watson: 1.625
Prob(Omnibus): 0.796 Jarque-Bera (JB): 0.543
Skew: 0.178 Prob(JB): 0.762
Kurtosis: 2.139 Cond. No. 29.2