LID SPN TRAP

1457810_at

Zbtb37 zinc finger and BTB domain containing 37

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
0.006 0.939 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.660
Model: OLS Adj. R-squared: 0.607
Method: Least Squares F-statistic: 12.30
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000106
Time: 05:01:53 Log-Likelihood: -100.69
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 16.2173 59.555 0.272 0.788 -108.433 140.867
C(dose)[T.1] 114.9030 78.873 1.457 0.161 -50.181 279.987
expression 11.3067 17.631 0.641 0.529 -25.594 48.208
expression:C(dose)[T.1] -18.4056 23.444 -0.785 0.442 -67.474 30.663
Omnibus: 0.840 Durbin-Watson: 2.193
Prob(Omnibus): 0.657 Jarque-Bera (JB): 0.739
Skew: 0.111 Prob(JB): 0.691
Kurtosis: 2.150 Cond. No. 87.1

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: 05:01:53 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 51.1935 39.142 1.308 0.206 -30.456 132.843
C(dose)[T.1] 53.3722 8.780 6.079 0.000 35.057 71.687
expression 0.8973 11.509 0.078 0.939 -23.109 24.904
Omnibus: 0.281 Durbin-Watson: 1.905
Prob(Omnibus): 0.869 Jarque-Bera (JB): 0.460
Skew: 0.047 Prob(JB): 0.795
Kurtosis: 2.314 Cond. No. 33.0

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: 05:01:53 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: 0.001
Model: OLS Adj. R-squared: -0.047
Method: Least Squares F-statistic: 0.02023
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.888
Time: 05:01:53 Log-Likelihood: -113.09
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 88.7126 63.654 1.394 0.178 -43.662 221.087
expression -2.6921 18.928 -0.142 0.888 -42.054 36.670
Omnibus: 3.183 Durbin-Watson: 2.492
Prob(Omnibus): 0.204 Jarque-Bera (JB): 1.535
Skew: 0.283 Prob(JB): 0.464
Kurtosis: 1.868 Cond. No. 32.3

CP101

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

F-statistic p-value df difference
0.097 0.761 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.454
Model: OLS Adj. R-squared: 0.305
Method: Least Squares F-statistic: 3.051
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0740
Time: 05:01:53 Log-Likelihood: -70.760
No. Observations: 15 AIC: 149.5
Df Residuals: 11 BIC: 152.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 93.6549 82.216 1.139 0.279 -87.302 274.612
C(dose)[T.1] 29.2494 126.666 0.231 0.822 -249.540 308.038
expression -5.8355 18.099 -0.322 0.753 -45.672 34.001
expression:C(dose)[T.1] 4.2205 30.554 0.138 0.893 -63.029 71.470
Omnibus: 2.745 Durbin-Watson: 0.894
Prob(Omnibus): 0.254 Jarque-Bera (JB): 1.952
Skew: -0.854 Prob(JB): 0.377
Kurtosis: 2.549 Cond. No. 85.9

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.453
Model: OLS Adj. R-squared: 0.362
Method: Least Squares F-statistic: 4.973
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0267
Time: 05:01:53 Log-Likelihood: -70.773
No. Observations: 15 AIC: 147.5
Df Residuals: 12 BIC: 149.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 86.9990 63.835 1.363 0.198 -52.085 226.083
C(dose)[T.1] 46.5562 17.819 2.613 0.023 7.731 85.381
expression -4.3545 13.973 -0.312 0.761 -34.800 26.091
Omnibus: 2.530 Durbin-Watson: 0.862
Prob(Omnibus): 0.282 Jarque-Bera (JB): 1.878
Skew: -0.819 Prob(JB): 0.391
Kurtosis: 2.432 Cond. No. 36.9

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: 05:01:53 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: 0.142
Model: OLS Adj. R-squared: 0.076
Method: Least Squares F-statistic: 2.154
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.166
Time: 05:01:53 Log-Likelihood: -74.150
No. Observations: 15 AIC: 152.3
Df Residuals: 13 BIC: 153.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 184.2275 62.414 2.952 0.011 49.389 319.065
expression -21.7126 14.793 -1.468 0.166 -53.671 10.246
Omnibus: 0.887 Durbin-Watson: 1.608
Prob(Omnibus): 0.642 Jarque-Bera (JB): 0.673
Skew: 0.004 Prob(JB): 0.714
Kurtosis: 1.962 Cond. No. 29.5