LID SPN TRAP

1458474_at

Fam108a family with sequence similarity 108, member A

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
0.266 0.612 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.688
Model: OLS Adj. R-squared: 0.638
Method: Least Squares F-statistic: 13.94
Date: Thu, 21 Nov 2024 Prob (F-statistic): 4.86e-05
Time: 03:50:22 Log-Likelihood: -99.727
No. Observations: 23 AIC: 207.5
Df Residuals: 19 BIC: 212.0
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 93.4551 39.449 2.369 0.029 10.887 176.024
C(dose)[T.1] -109.0143 114.072 -0.956 0.351 -347.771 129.742
expression -8.3473 8.297 -1.006 0.327 -25.713 9.018
expression:C(dose)[T.1] 33.3912 23.268 1.435 0.168 -15.310 82.092
Omnibus: 0.833 Durbin-Watson: 1.462
Prob(Omnibus): 0.659 Jarque-Bera (JB): 0.457
Skew: -0.342 Prob(JB): 0.796
Kurtosis: 2.899 Cond. No. 156.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.654
Model: OLS Adj. R-squared: 0.619
Method: Least Squares F-statistic: 18.87
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.48e-05
Time: 03:50:22 Log-Likelihood: -100.91
No. Observations: 23 AIC: 207.8
Df Residuals: 20 BIC: 211.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 73.4932 37.881 1.940 0.067 -5.524 152.511
C(dose)[T.1] 54.2143 8.877 6.108 0.000 35.698 72.731
expression -4.1017 7.954 -0.516 0.612 -20.694 12.491
Omnibus: 0.247 Durbin-Watson: 1.776
Prob(Omnibus): 0.884 Jarque-Bera (JB): 0.438
Skew: 0.007 Prob(JB): 0.803
Kurtosis: 2.324 Cond. No. 44.1

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: 03:50:22 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: 0.008
Model: OLS Adj. R-squared: -0.040
Method: Least Squares F-statistic: 0.1631
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.690
Time: 03:50:22 Log-Likelihood: -113.02
No. Observations: 23 AIC: 230.0
Df Residuals: 21 BIC: 232.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 54.6961 62.367 0.877 0.390 -75.003 184.395
expression 5.2084 12.896 0.404 0.690 -21.610 32.027
Omnibus: 3.589 Durbin-Watson: 2.587
Prob(Omnibus): 0.166 Jarque-Bera (JB): 1.514
Skew: 0.211 Prob(JB): 0.469
Kurtosis: 1.816 Cond. No. 43.7

CP101

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

F-statistic p-value df difference
0.012 0.915 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.496
Model: OLS Adj. R-squared: 0.358
Method: Least Squares F-statistic: 3.603
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0495
Time: 03:50:22 Log-Likelihood: -70.167
No. Observations: 15 AIC: 148.3
Df Residuals: 11 BIC: 151.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 32.6006 74.283 0.439 0.669 -130.895 196.096
C(dose)[T.1] 182.3255 133.296 1.368 0.199 -111.058 475.709
expression 7.8116 16.461 0.475 0.644 -28.418 44.041
expression:C(dose)[T.1] -29.4377 29.296 -1.005 0.337 -93.918 35.042
Omnibus: 1.146 Durbin-Watson: 0.857
Prob(Omnibus): 0.564 Jarque-Bera (JB): 0.916
Skew: -0.368 Prob(JB): 0.633
Kurtosis: 2.040 Cond. No. 99.6

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.449
Model: OLS Adj. R-squared: 0.358
Method: Least Squares F-statistic: 4.896
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0279
Time: 03:50:22 Log-Likelihood: -70.826
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 74.0358 61.811 1.198 0.254 -60.640 208.711
C(dose)[T.1] 49.3253 15.776 3.127 0.009 14.951 83.699
expression -1.4819 13.622 -0.109 0.915 -31.162 28.198
Omnibus: 2.659 Durbin-Watson: 0.785
Prob(Omnibus): 0.265 Jarque-Bera (JB): 1.875
Skew: -0.838 Prob(JB): 0.392
Kurtosis: 2.566 Cond. No. 37.7

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: 03:50:22 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: 0.001
Model: OLS Adj. R-squared: -0.076
Method: Least Squares F-statistic: 0.009536
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.924
Time: 03:50:22 Log-Likelihood: -75.295
No. Observations: 15 AIC: 154.6
Df Residuals: 13 BIC: 156.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 85.9328 79.846 1.076 0.301 -86.564 258.430
expression 1.7168 17.580 0.098 0.924 -36.263 39.697
Omnibus: 0.798 Durbin-Watson: 1.627
Prob(Omnibus): 0.671 Jarque-Bera (JB): 0.650
Skew: 0.061 Prob(JB): 0.722
Kurtosis: 1.987 Cond. No. 37.4