LID SPN TRAP

1455753_at

Fam163b family with sequence similarity 163, member B

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
0.699 0.413 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.661
Model: OLS Adj. R-squared: 0.608
Method: Least Squares F-statistic: 12.36
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000103
Time: 04:48:30 Log-Likelihood: -100.66
No. Observations: 23 AIC: 209.3
Df Residuals: 19 BIC: 213.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 110.8682 86.308 1.285 0.214 -69.777 291.513
C(dose)[T.1] 32.7963 113.037 0.290 0.775 -203.792 269.385
expression -8.3797 12.732 -0.658 0.518 -35.029 18.270
expression:C(dose)[T.1] 2.1960 17.807 0.123 0.903 -35.075 39.467
Omnibus: 0.430 Durbin-Watson: 1.874
Prob(Omnibus): 0.806 Jarque-Bera (JB): 0.544
Skew: 0.047 Prob(JB): 0.762
Kurtosis: 2.253 Cond. No. 218.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.661
Model: OLS Adj. R-squared: 0.627
Method: Least Squares F-statistic: 19.49
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.01e-05
Time: 04:48:30 Log-Likelihood: -100.67
No. Observations: 23 AIC: 207.3
Df Residuals: 20 BIC: 210.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 103.2767 58.988 1.751 0.095 -19.770 226.324
C(dose)[T.1] 46.6570 11.753 3.970 0.001 22.140 71.174
expression -7.2570 8.679 -0.836 0.413 -25.362 10.848
Omnibus: 0.381 Durbin-Watson: 1.892
Prob(Omnibus): 0.827 Jarque-Bera (JB): 0.519
Skew: 0.062 Prob(JB): 0.772
Kurtosis: 2.275 Cond. No. 90.2

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: 04:48:30 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: 0.394
Model: OLS Adj. R-squared: 0.365
Method: Least Squares F-statistic: 13.64
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00135
Time: 04:48:30 Log-Likelihood: -107.35
No. Observations: 23 AIC: 218.7
Df Residuals: 21 BIC: 221.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 273.6370 52.809 5.182 0.000 163.814 383.460
expression -30.6772 8.307 -3.693 0.001 -47.952 -13.402
Omnibus: 2.248 Durbin-Watson: 2.236
Prob(Omnibus): 0.325 Jarque-Bera (JB): 1.305
Skew: 0.581 Prob(JB): 0.521
Kurtosis: 3.102 Cond. No. 61.2

CP101

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

F-statistic p-value df difference
0.001 0.972 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.489
Model: OLS Adj. R-squared: 0.350
Method: Least Squares F-statistic: 3.514
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0527
Time: 04:48:30 Log-Likelihood: -70.259
No. Observations: 15 AIC: 148.5
Df Residuals: 11 BIC: 151.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -37.0755 188.412 -0.197 0.848 -451.767 377.617
C(dose)[T.1] 337.2978 308.433 1.094 0.298 -341.558 1016.153
expression 13.3857 24.088 0.556 0.590 -39.631 66.403
expression:C(dose)[T.1] -36.0892 38.604 -0.935 0.370 -121.057 48.879
Omnibus: 2.298 Durbin-Watson: 0.704
Prob(Omnibus): 0.317 Jarque-Bera (JB): 1.478
Skew: -0.541 Prob(JB): 0.478
Kurtosis: 1.908 Cond. No. 401.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.449
Model: OLS Adj. R-squared: 0.357
Method: Least Squares F-statistic: 4.886
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0280
Time: 04:48:30 Log-Likelihood: -70.832
No. Observations: 15 AIC: 147.7
Df Residuals: 12 BIC: 149.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 72.6208 146.635 0.495 0.629 -246.869 392.111
C(dose)[T.1] 49.3824 16.587 2.977 0.012 13.243 85.522
expression -0.6651 18.724 -0.036 0.972 -41.462 40.132
Omnibus: 2.712 Durbin-Watson: 0.813
Prob(Omnibus): 0.258 Jarque-Bera (JB): 1.889
Skew: -0.845 Prob(JB): 0.389
Kurtosis: 2.595 Cond. No. 152.

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: 04:48:30 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: 0.042
Model: OLS Adj. R-squared: -0.032
Method: Least Squares F-statistic: 0.5658
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.465
Time: 04:48:30 Log-Likelihood: -74.981
No. Observations: 15 AIC: 154.0
Df Residuals: 13 BIC: 155.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -41.0349 179.359 -0.229 0.823 -428.516 346.447
expression 16.9303 22.508 0.752 0.465 -31.696 65.557
Omnibus: 1.513 Durbin-Watson: 1.421
Prob(Omnibus): 0.469 Jarque-Bera (JB): 0.864
Skew: 0.127 Prob(JB): 0.649
Kurtosis: 1.852 Cond. No. 146.