LID SPN TRAP

1457716_at

Otud7b OTU domain containing 7B

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
2.168 0.156 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.695
Model: OLS Adj. R-squared: 0.647
Method: Least Squares F-statistic: 14.42
Date: Thu, 03 Apr 2025 Prob (F-statistic): 3.90e-05
Time: 22:55:11 Log-Likelihood: -99.456
No. Observations: 23 AIC: 206.9
Df Residuals: 19 BIC: 211.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 31.7689 100.383 0.316 0.755 -178.336 241.874
C(dose)[T.1] -50.7252 125.830 -0.403 0.691 -314.090 212.639
expression 3.0050 13.421 0.224 0.825 -25.085 31.095
expression:C(dose)[T.1] 14.2916 16.942 0.844 0.409 -21.169 49.752
Omnibus: 0.208 Durbin-Watson: 1.929
Prob(Omnibus): 0.901 Jarque-Bera (JB): 0.353
Skew: 0.185 Prob(JB): 0.838
Kurtosis: 2.519 Cond. No. 311.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.683
Model: OLS Adj. R-squared: 0.652
Method: Least Squares F-statistic: 21.58
Date: Thu, 03 Apr 2025 Prob (F-statistic): 1.01e-05
Time: 22:55:12 Log-Likelihood: -99.879
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 -35.1955 60.996 -0.577 0.570 -162.431 92.040
C(dose)[T.1] 55.1765 8.423 6.551 0.000 37.606 72.747
expression 11.9727 8.132 1.472 0.156 -4.990 28.935
Omnibus: 0.219 Durbin-Watson: 1.644
Prob(Omnibus): 0.896 Jarque-Bera (JB): 0.339
Skew: 0.196 Prob(JB): 0.844
Kurtosis: 2.553 Cond. No. 111.

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, 03 Apr 2025 Prob (F-statistic): 3.51e-06
Time: 22:55:12 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: 0.004
Model: OLS Adj. R-squared: -0.043
Method: Least Squares F-statistic: 0.08559
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.773
Time: 22:55:12 Log-Likelihood: -113.06
No. Observations: 23 AIC: 230.1
Df Residuals: 21 BIC: 232.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 49.6087 103.167 0.481 0.636 -164.939 264.156
expression 4.0721 13.919 0.293 0.773 -24.874 33.018
Omnibus: 3.575 Durbin-Watson: 2.477
Prob(Omnibus): 0.167 Jarque-Bera (JB): 1.574
Skew: 0.258 Prob(JB): 0.455
Kurtosis: 1.827 Cond. No. 108.

CP101

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

F-statistic p-value df difference
2.040 0.179 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.529
Model: OLS Adj. R-squared: 0.400
Method: Least Squares F-statistic: 4.118
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0348
Time: 22:55:12 Log-Likelihood: -69.654
No. Observations: 15 AIC: 147.3
Df Residuals: 11 BIC: 150.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 236.1882 147.077 1.606 0.137 -87.527 559.903
C(dose)[T.1] 29.5602 249.558 0.118 0.908 -519.713 578.834
expression -19.7847 17.194 -1.151 0.274 -57.627 18.058
expression:C(dose)[T.1] 1.3692 30.230 0.045 0.965 -65.167 67.905
Omnibus: 5.064 Durbin-Watson: 0.715
Prob(Omnibus): 0.080 Jarque-Bera (JB): 2.711
Skew: -1.015 Prob(JB): 0.258
Kurtosis: 3.466 Cond. No. 342.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.529
Model: OLS Adj. R-squared: 0.450
Method: Least Squares F-statistic: 6.735
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0109
Time: 22:55:12 Log-Likelihood: -69.655
No. Observations: 15 AIC: 145.3
Df Residuals: 12 BIC: 147.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 232.4103 115.990 2.004 0.068 -20.310 485.131
C(dose)[T.1] 40.8388 15.683 2.604 0.023 6.667 75.010
expression -19.3417 13.541 -1.428 0.179 -48.845 10.162
Omnibus: 5.245 Durbin-Watson: 0.705
Prob(Omnibus): 0.073 Jarque-Bera (JB): 2.812
Skew: -1.030 Prob(JB): 0.245
Kurtosis: 3.507 Cond. No. 135.

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, 03 Apr 2025 Prob (F-statistic): 0.00629
Time: 22:55:12 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: 0.263
Model: OLS Adj. R-squared: 0.206
Method: Least Squares F-statistic: 4.631
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0508
Time: 22:55:12 Log-Likelihood: -73.015
No. Observations: 15 AIC: 150.0
Df Residuals: 13 BIC: 151.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 363.3654 125.627 2.892 0.013 91.965 634.766
expression -32.4963 15.100 -2.152 0.051 -65.119 0.126
Omnibus: 2.347 Durbin-Watson: 1.618
Prob(Omnibus): 0.309 Jarque-Bera (JB): 1.015
Skew: -0.048 Prob(JB): 0.602
Kurtosis: 1.729 Cond. No. 122.