LID SPN TRAP

1448705_at

Zbtb22 zinc finger and BTB domain containing 22

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
2.113 0.162 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.683
Model: OLS Adj. R-squared: 0.633
Method: Least Squares F-statistic: 13.66
Date: Thu, 21 Nov 2024 Prob (F-statistic): 5.51e-05
Time: 04:30:29 Log-Likelihood: -99.884
No. Observations: 23 AIC: 207.8
Df Residuals: 19 BIC: 212.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -31.1739 168.866 -0.185 0.855 -384.614 322.267
C(dose)[T.1] 21.6724 190.236 0.114 0.910 -376.497 419.842
expression 11.1119 21.963 0.506 0.619 -34.858 57.082
expression:C(dose)[T.1] 4.8941 25.004 0.196 0.847 -47.439 57.227
Omnibus: 1.421 Durbin-Watson: 1.822
Prob(Omnibus): 0.491 Jarque-Bera (JB): 0.926
Skew: 0.085 Prob(JB): 0.630
Kurtosis: 2.032 Cond. No. 497.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.683
Model: OLS Adj. R-squared: 0.651
Method: Least Squares F-statistic: 21.51
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.04e-05
Time: 04:30:29 Log-Likelihood: -99.908
No. Observations: 23 AIC: 205.8
Df Residuals: 20 BIC: 209.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -60.1900 78.903 -0.763 0.454 -224.778 104.398
C(dose)[T.1] 58.8629 9.166 6.422 0.000 39.744 77.982
expression 14.8881 10.241 1.454 0.162 -6.475 36.251
Omnibus: 1.605 Durbin-Watson: 1.889
Prob(Omnibus): 0.448 Jarque-Bera (JB): 0.980
Skew: 0.089 Prob(JB): 0.613
Kurtosis: 2.005 Cond. No. 145.

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: 04:30:29 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: 0.028
Model: OLS Adj. R-squared: -0.018
Method: Least Squares F-statistic: 0.6059
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.445
Time: 04:30:29 Log-Likelihood: -112.78
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 172.7011 119.671 1.443 0.164 -76.169 421.571
expression -12.3873 15.914 -0.778 0.445 -45.483 20.709
Omnibus: 2.130 Durbin-Watson: 2.330
Prob(Omnibus): 0.345 Jarque-Bera (JB): 1.421
Skew: 0.368 Prob(JB): 0.491
Kurtosis: 2.030 Cond. No. 129.

CP101

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

F-statistic p-value df difference
0.012 0.915 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.612
Model: OLS Adj. R-squared: 0.506
Method: Least Squares F-statistic: 5.780
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0127
Time: 04:30:29 Log-Likelihood: -68.202
No. Observations: 15 AIC: 144.4
Df Residuals: 11 BIC: 147.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 242.0013 138.524 1.747 0.108 -62.888 546.890
C(dose)[T.1] -489.6805 251.300 -1.949 0.077 -1042.788 63.427
expression -21.7178 17.187 -1.264 0.233 -59.547 16.112
expression:C(dose)[T.1] 68.3255 31.836 2.146 0.055 -1.745 138.396
Omnibus: 0.642 Durbin-Watson: 1.012
Prob(Omnibus): 0.725 Jarque-Bera (JB): 0.617
Skew: -0.395 Prob(JB): 0.734
Kurtosis: 2.396 Cond. No. 358.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.449
Model: OLS Adj. R-squared: 0.358
Method: Least Squares F-statistic: 4.896
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0279
Time: 04:30:29 Log-Likelihood: -70.826
No. Observations: 15 AIC: 147.7
Df Residuals: 12 BIC: 149.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 81.9226 133.117 0.615 0.550 -208.114 371.959
C(dose)[T.1] 48.7964 16.152 3.021 0.011 13.604 83.989
expression -1.8031 16.499 -0.109 0.915 -37.751 34.144
Omnibus: 2.797 Durbin-Watson: 0.846
Prob(Omnibus): 0.247 Jarque-Bera (JB): 1.863
Skew: -0.849 Prob(JB): 0.394
Kurtosis: 2.688 Cond. No. 137.

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: 04:30:30 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: 0.030
Model: OLS Adj. R-squared: -0.044
Method: Least Squares F-statistic: 0.4088
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.534
Time: 04:30:30 Log-Likelihood: -75.068
No. Observations: 15 AIC: 154.1
Df Residuals: 13 BIC: 155.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 197.4032 162.552 1.214 0.246 -153.769 548.575
expression -13.0982 20.486 -0.639 0.534 -57.354 31.158
Omnibus: 1.980 Durbin-Watson: 1.799
Prob(Omnibus): 0.372 Jarque-Bera (JB): 1.017
Skew: 0.216 Prob(JB): 0.601
Kurtosis: 1.800 Cond. No. 131.