LID SPN TRAP

1460381_at

Zfp772 zinc finger protein 772

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
0.463 0.504 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.726
Model: OLS Adj. R-squared: 0.683
Method: Least Squares F-statistic: 16.77
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.44e-05
Time: 05:12:21 Log-Likelihood: -98.224
No. Observations: 23 AIC: 204.4
Df Residuals: 19 BIC: 209.0
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 189.5620 68.685 2.760 0.012 45.802 333.322
C(dose)[T.1] -175.7938 104.809 -1.677 0.110 -395.162 43.574
expression -20.0360 10.135 -1.977 0.063 -41.248 1.176
expression:C(dose)[T.1] 34.2629 15.691 2.184 0.042 1.422 67.104
Omnibus: 0.959 Durbin-Watson: 1.640
Prob(Omnibus): 0.619 Jarque-Bera (JB): 0.941
Skew: 0.371 Prob(JB): 0.625
Kurtosis: 2.344 Cond. No. 226.

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: 05:12:21 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 92.9982 57.294 1.623 0.120 -26.515 212.511
C(dose)[T.1] 52.3954 8.780 5.968 0.000 34.081 70.709
expression -5.7419 8.434 -0.681 0.504 -23.336 11.852
Omnibus: 0.066 Durbin-Watson: 1.921
Prob(Omnibus): 0.968 Jarque-Bera (JB): 0.166
Skew: -0.105 Prob(JB): 0.920
Kurtosis: 2.641 Cond. No. 90.8

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: 05:12:21 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: 0.046
Model: OLS Adj. R-squared: 0.001
Method: Least Squares F-statistic: 1.018
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.325
Time: 05:12:21 Log-Likelihood: -112.56
No. Observations: 23 AIC: 229.1
Df Residuals: 21 BIC: 231.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 171.0142 90.779 1.884 0.074 -17.770 359.799
expression -13.6731 13.554 -1.009 0.325 -41.861 14.515
Omnibus: 5.313 Durbin-Watson: 2.400
Prob(Omnibus): 0.070 Jarque-Bera (JB): 2.314
Skew: 0.467 Prob(JB): 0.314
Kurtosis: 1.757 Cond. No. 88.2

CP101

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

F-statistic p-value df difference
0.639 0.439 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.833
Model: OLS Adj. R-squared: 0.787
Method: Least Squares F-statistic: 18.29
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000139
Time: 05:12:21 Log-Likelihood: -61.876
No. Observations: 15 AIC: 131.8
Df Residuals: 11 BIC: 134.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 371.6912 160.032 2.323 0.040 19.462 723.920
C(dose)[T.1] -1212.1381 260.525 -4.653 0.001 -1785.549 -638.727
expression -41.3327 21.721 -1.903 0.084 -89.141 6.475
expression:C(dose)[T.1] 171.5171 35.399 4.845 0.001 93.605 249.429
Omnibus: 0.346 Durbin-Watson: 1.431
Prob(Omnibus): 0.841 Jarque-Bera (JB): 0.473
Skew: 0.026 Prob(JB): 0.789
Kurtosis: 2.131 Cond. No. 545.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.477
Model: OLS Adj. R-squared: 0.389
Method: Least Squares F-statistic: 5.465
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0205
Time: 05:12:21 Log-Likelihood: -70.444
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 -103.7035 214.297 -0.484 0.637 -570.616 363.209
C(dose)[T.1] 49.4205 15.339 3.222 0.007 16.000 82.841
expression 23.2475 29.071 0.800 0.439 -40.094 86.589
Omnibus: 3.163 Durbin-Watson: 0.693
Prob(Omnibus): 0.206 Jarque-Bera (JB): 2.267
Skew: -0.925 Prob(JB): 0.322
Kurtosis: 2.545 Cond. No. 211.

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: 05:12:21 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: 0.024
Model: OLS Adj. R-squared: -0.051
Method: Least Squares F-statistic: 0.3189
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.582
Time: 05:12:21 Log-Likelihood: -75.118
No. Observations: 15 AIC: 154.2
Df Residuals: 13 BIC: 155.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -64.7569 280.730 -0.231 0.821 -671.237 541.723
expression 21.5362 38.138 0.565 0.582 -60.856 103.929
Omnibus: 0.444 Durbin-Watson: 1.645
Prob(Omnibus): 0.801 Jarque-Bera (JB): 0.329
Skew: -0.311 Prob(JB): 0.848
Kurtosis: 2.625 Cond. No. 210.