LID SPN TRAP

1450762_s_at

Zfp191 zinc finger protein 191

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
4.459 0.047 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.713
Model: OLS Adj. R-squared: 0.668
Method: Least Squares F-statistic: 15.75
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.18e-05
Time: 04:27:32 Log-Likelihood: -98.741
No. Observations: 23 AIC: 205.5
Df Residuals: 19 BIC: 210.0
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -206.2849 273.714 -0.754 0.460 -779.174 366.604
C(dose)[T.1] 84.1357 301.212 0.279 0.783 -546.309 714.580
expression 32.7268 34.380 0.952 0.353 -39.232 104.686
expression:C(dose)[T.1] -4.0804 37.781 -0.108 0.915 -83.156 74.995
Omnibus: 0.056 Durbin-Watson: 1.954
Prob(Omnibus): 0.972 Jarque-Bera (JB): 0.134
Skew: 0.088 Prob(JB): 0.935
Kurtosis: 2.670 Cond. No. 902.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.713
Model: OLS Adj. R-squared: 0.684
Method: Least Squares F-statistic: 24.85
Date: Thu, 21 Nov 2024 Prob (F-statistic): 3.79e-06
Time: 04:27:32 Log-Likelihood: -98.748
No. Observations: 23 AIC: 203.5
Df Residuals: 20 BIC: 206.9
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -179.3893 110.756 -1.620 0.121 -410.423 51.645
C(dose)[T.1] 51.6159 7.972 6.475 0.000 34.987 68.245
expression 29.3478 13.898 2.112 0.047 0.358 58.338
Omnibus: 0.070 Durbin-Watson: 1.944
Prob(Omnibus): 0.966 Jarque-Bera (JB): 0.128
Skew: 0.095 Prob(JB): 0.938
Kurtosis: 2.688 Cond. No. 227.

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: 04:27:32 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: 0.112
Model: OLS Adj. R-squared: 0.069
Method: Least Squares F-statistic: 2.637
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.119
Time: 04:27:32 Log-Likelihood: -111.74
No. Observations: 23 AIC: 227.5
Df Residuals: 21 BIC: 229.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -228.1931 189.745 -1.203 0.243 -622.790 166.403
expression 38.5482 23.739 1.624 0.119 -10.821 87.917
Omnibus: 2.943 Durbin-Watson: 2.547
Prob(Omnibus): 0.230 Jarque-Bera (JB): 1.431
Skew: 0.245 Prob(JB): 0.489
Kurtosis: 1.880 Cond. No. 226.

CP101

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

F-statistic p-value df difference
0.188 0.673 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.499
Model: OLS Adj. R-squared: 0.363
Method: Least Squares F-statistic: 3.656
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0476
Time: 04:27:32 Log-Likelihood: -70.112
No. Observations: 15 AIC: 148.2
Df Residuals: 11 BIC: 151.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -171.3492 428.878 -0.400 0.697 -1115.304 772.605
C(dose)[T.1] 537.7466 511.907 1.050 0.316 -588.953 1664.447
expression 31.3590 56.305 0.557 0.589 -92.568 155.285
expression:C(dose)[T.1] -65.1991 67.838 -0.961 0.357 -214.509 84.111
Omnibus: 1.174 Durbin-Watson: 1.318
Prob(Omnibus): 0.556 Jarque-Bera (JB): 0.659
Skew: -0.500 Prob(JB): 0.719
Kurtosis: 2.771 Cond. No. 722.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.457
Model: OLS Adj. R-squared: 0.367
Method: Least Squares F-statistic: 5.055
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0256
Time: 04:27:32 Log-Likelihood: -70.717
No. Observations: 15 AIC: 147.4
Df Residuals: 12 BIC: 149.6
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 170.6490 238.643 0.715 0.488 -349.309 690.607
C(dose)[T.1] 46.0325 17.243 2.670 0.020 8.464 83.601
expression -13.5561 31.305 -0.433 0.673 -81.765 54.653
Omnibus: 1.956 Durbin-Watson: 0.819
Prob(Omnibus): 0.376 Jarque-Bera (JB): 1.345
Skew: -0.706 Prob(JB): 0.510
Kurtosis: 2.600 Cond. No. 234.

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: 04:27:32 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: 0.135
Model: OLS Adj. R-squared: 0.068
Method: Least Squares F-statistic: 2.027
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.178
Time: 04:27:32 Log-Likelihood: -74.213
No. Observations: 15 AIC: 152.4
Df Residuals: 13 BIC: 153.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 460.4546 257.787 1.786 0.097 -96.460 1017.369
expression -48.9713 34.395 -1.424 0.178 -123.277 25.335
Omnibus: 0.200 Durbin-Watson: 1.607
Prob(Omnibus): 0.905 Jarque-Bera (JB): 0.044
Skew: 0.069 Prob(JB): 0.978
Kurtosis: 2.774 Cond. No. 208.