LID SPN TRAP

1458413_at

Fbxw8 F-box and WD-40 domain protein 8

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
0.055 0.817 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.658
Model: OLS Adj. R-squared: 0.604
Method: Least Squares F-statistic: 12.19
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000112
Time: 04:05:04 Log-Likelihood: -100.76
No. Observations: 23 AIC: 209.5
Df Residuals: 19 BIC: 214.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 6.0641 81.656 0.074 0.942 -164.844 176.972
C(dose)[T.1] 145.0129 138.309 1.048 0.308 -144.472 434.497
expression 7.3141 12.370 0.591 0.561 -18.577 33.205
expression:C(dose)[T.1] -13.6172 20.333 -0.670 0.511 -56.174 28.940
Omnibus: 0.441 Durbin-Watson: 2.004
Prob(Omnibus): 0.802 Jarque-Bera (JB): 0.552
Skew: -0.074 Prob(JB): 0.759
Kurtosis: 2.255 Cond. No. 264.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.650
Model: OLS Adj. R-squared: 0.615
Method: Least Squares F-statistic: 18.57
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.76e-05
Time: 04:05:04 Log-Likelihood: -101.03
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 39.2406 64.012 0.613 0.547 -94.286 172.767
C(dose)[T.1] 52.6002 9.303 5.654 0.000 33.195 72.005
expression 2.2739 9.681 0.235 0.817 -17.921 22.468
Omnibus: 0.358 Durbin-Watson: 1.881
Prob(Omnibus): 0.836 Jarque-Bera (JB): 0.505
Skew: 0.040 Prob(JB): 0.777
Kurtosis: 2.279 Cond. No. 101.

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: 04:05:04 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: 0.091
Model: OLS Adj. R-squared: 0.047
Method: Least Squares F-statistic: 2.091
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.163
Time: 04:05:04 Log-Likelihood: -112.01
No. Observations: 23 AIC: 228.0
Df Residuals: 21 BIC: 230.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -59.9787 96.843 -0.619 0.542 -261.374 141.417
expression 20.7346 14.338 1.446 0.163 -9.082 50.551
Omnibus: 1.028 Durbin-Watson: 2.540
Prob(Omnibus): 0.598 Jarque-Bera (JB): 0.968
Skew: 0.341 Prob(JB): 0.616
Kurtosis: 2.262 Cond. No. 97.1

CP101

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

F-statistic p-value df difference
1.136 0.307 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.510
Model: OLS Adj. R-squared: 0.376
Method: Least Squares F-statistic: 3.814
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0427
Time: 04:05:04 Log-Likelihood: -69.952
No. Observations: 15 AIC: 147.9
Df Residuals: 11 BIC: 150.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -175.0331 236.113 -0.741 0.474 -694.715 344.649
C(dose)[T.1] 202.9571 284.515 0.713 0.490 -423.256 829.170
expression 36.7934 35.789 1.028 0.326 -41.977 115.564
expression:C(dose)[T.1] -23.5237 42.922 -0.548 0.595 -117.994 70.947
Omnibus: 1.754 Durbin-Watson: 0.912
Prob(Omnibus): 0.416 Jarque-Bera (JB): 1.171
Skew: -0.429 Prob(JB): 0.557
Kurtosis: 1.934 Cond. No. 363.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.496
Model: OLS Adj. R-squared: 0.413
Method: Least Squares F-statistic: 5.916
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0163
Time: 04:05:04 Log-Likelihood: -70.154
No. Observations: 15 AIC: 146.3
Df Residuals: 12 BIC: 148.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -67.2586 126.820 -0.530 0.606 -343.577 209.059
C(dose)[T.1] 47.2611 15.153 3.119 0.009 14.246 80.276
expression 20.4387 19.173 1.066 0.307 -21.335 62.212
Omnibus: 1.726 Durbin-Watson: 0.967
Prob(Omnibus): 0.422 Jarque-Bera (JB): 1.114
Skew: -0.386 Prob(JB): 0.573
Kurtosis: 1.911 Cond. No. 115.

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: 04:05:04 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: 0.088
Model: OLS Adj. R-squared: 0.018
Method: Least Squares F-statistic: 1.258
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.282
Time: 04:05:04 Log-Likelihood: -74.607
No. Observations: 15 AIC: 153.2
Df Residuals: 13 BIC: 154.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -89.6287 163.695 -0.548 0.593 -443.269 264.012
expression 27.6034 24.608 1.122 0.282 -25.560 80.766
Omnibus: 1.227 Durbin-Watson: 1.641
Prob(Omnibus): 0.541 Jarque-Bera (JB): 0.984
Skew: 0.416 Prob(JB): 0.611
Kurtosis: 2.060 Cond. No. 115.