LID SPN TRAP

1457728_at

Fam129c family with sequence similarity 129, member C

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
1.109 0.305 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.676
Model: OLS Adj. R-squared: 0.625
Method: Least Squares F-statistic: 13.23
Date: Thu, 21 Nov 2024 Prob (F-statistic): 6.74e-05
Time: 04:50:38 Log-Likelihood: -100.13
No. Observations: 23 AIC: 208.3
Df Residuals: 19 BIC: 212.8
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 49.0144 30.542 1.605 0.125 -14.911 112.940
C(dose)[T.1] 22.2700 42.542 0.523 0.607 -66.772 111.312
expression 1.1347 6.543 0.173 0.864 -12.560 14.830
expression:C(dose)[T.1] 6.3854 8.880 0.719 0.481 -12.201 24.972
Omnibus: 1.948 Durbin-Watson: 1.661
Prob(Omnibus): 0.378 Jarque-Bera (JB): 1.223
Skew: 0.268 Prob(JB): 0.543
Kurtosis: 2.006 Cond. No. 65.1

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.667
Model: OLS Adj. R-squared: 0.634
Method: Least Squares F-statistic: 20.07
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.65e-05
Time: 04:50:38 Log-Likelihood: -100.44
No. Observations: 23 AIC: 206.9
Df Residuals: 20 BIC: 210.3
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 33.1452 20.857 1.589 0.128 -10.361 76.651
C(dose)[T.1] 52.2126 8.603 6.069 0.000 34.267 70.158
expression 4.6014 4.370 1.053 0.305 -4.514 13.717
Omnibus: 1.725 Durbin-Watson: 1.712
Prob(Omnibus): 0.422 Jarque-Bera (JB): 1.074
Skew: 0.186 Prob(JB): 0.584
Kurtosis: 2.009 Cond. No. 24.6

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: 04:50:38 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: 0.055
Model: OLS Adj. R-squared: 0.010
Method: Least Squares F-statistic: 1.225
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.281
Time: 04:50:38 Log-Likelihood: -112.45
No. Observations: 23 AIC: 228.9
Df Residuals: 21 BIC: 231.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 42.6602 34.214 1.247 0.226 -28.493 113.813
expression 7.8939 7.134 1.107 0.281 -6.941 22.729
Omnibus: 2.334 Durbin-Watson: 2.285
Prob(Omnibus): 0.311 Jarque-Bera (JB): 1.276
Skew: 0.229 Prob(JB): 0.528
Kurtosis: 1.941 Cond. No. 24.4

CP101

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

F-statistic p-value df difference
0.000 0.993 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.449
Model: OLS Adj. R-squared: 0.299
Method: Least Squares F-statistic: 2.986
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0776
Time: 04:50:38 Log-Likelihood: -70.832
No. Observations: 15 AIC: 149.7
Df Residuals: 11 BIC: 152.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 68.6443 44.361 1.547 0.150 -28.993 166.282
C(dose)[T.1] 45.3271 86.285 0.525 0.610 -144.585 235.239
expression -0.2285 8.025 -0.028 0.978 -17.892 17.435
expression:C(dose)[T.1] 0.8068 17.848 0.045 0.965 -38.477 40.090
Omnibus: 2.768 Durbin-Watson: 0.808
Prob(Omnibus): 0.251 Jarque-Bera (JB): 1.893
Skew: -0.851 Prob(JB): 0.388
Kurtosis: 2.635 Cond. No. 64.6

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: 4.885
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0281
Time: 04:50:38 Log-Likelihood: -70.833
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 67.7763 38.290 1.770 0.102 -15.651 151.204
C(dose)[T.1] 49.1485 16.524 2.974 0.012 13.147 85.151
expression -0.0653 6.864 -0.010 0.993 -15.020 14.889
Omnibus: 2.707 Durbin-Watson: 0.810
Prob(Omnibus): 0.258 Jarque-Bera (JB): 1.866
Skew: -0.842 Prob(JB): 0.393
Kurtosis: 2.616 Cond. No. 26.3

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: 04:50:38 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: -0.031
Method: Least Squares F-statistic: 0.5753
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.462
Time: 04:50:38 Log-Likelihood: -74.975
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 124.6289 42.016 2.966 0.011 33.860 215.398
expression -6.2797 8.279 -0.758 0.462 -24.166 11.607
Omnibus: 0.820 Durbin-Watson: 1.480
Prob(Omnibus): 0.664 Jarque-Bera (JB): 0.655
Skew: 0.049 Prob(JB): 0.721
Kurtosis: 1.981 Cond. No. 22.2