LID SPN TRAP

1437950_at

Fam149a family with sequence similarity 149, 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
0.006 0.939 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: 0.607
Method: Least Squares F-statistic: 12.33
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000105
Time: 04:10:34 Log-Likelihood: -100.68
No. Observations: 23 AIC: 209.4
Df Residuals: 19 BIC: 213.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 6.5349 77.524 0.084 0.934 -155.725 168.795
C(dose)[T.1] 136.5426 104.017 1.313 0.205 -81.168 354.253
expression 17.4275 28.251 0.617 0.545 -41.703 76.558
expression:C(dose)[T.1] -32.1302 40.183 -0.800 0.434 -116.233 51.973
Omnibus: 1.019 Durbin-Watson: 1.911
Prob(Omnibus): 0.601 Jarque-Bera (JB): 0.783
Skew: -0.014 Prob(JB): 0.676
Kurtosis: 2.096 Cond. No. 90.8

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.649
Model: OLS Adj. R-squared: 0.614
Method: Least Squares F-statistic: 18.50
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.83e-05
Time: 04:10:34 Log-Likelihood: -101.06
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 49.9818 54.796 0.912 0.373 -64.320 164.283
C(dose)[T.1] 53.8297 10.825 4.973 0.000 31.250 76.410
expression 1.5450 19.908 0.078 0.939 -39.982 43.072
Omnibus: 0.235 Durbin-Watson: 1.882
Prob(Omnibus): 0.889 Jarque-Bera (JB): 0.430
Skew: 0.048 Prob(JB): 0.807
Kurtosis: 2.337 Cond. No. 37.8

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: 04:10:34 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: 0.215
Model: OLS Adj. R-squared: 0.178
Method: Least Squares F-statistic: 5.764
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0257
Time: 04:10:34 Log-Likelihood: -110.32
No. Observations: 23 AIC: 224.6
Df Residuals: 21 BIC: 226.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 225.6721 61.129 3.692 0.001 98.548 352.796
expression -56.5048 23.536 -2.401 0.026 -105.450 -7.560
Omnibus: 1.197 Durbin-Watson: 2.317
Prob(Omnibus): 0.550 Jarque-Bera (JB): 0.960
Skew: 0.247 Prob(JB): 0.619
Kurtosis: 2.129 Cond. No. 28.5

CP101

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

F-statistic p-value df difference
2.432 0.145 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.542
Model: OLS Adj. R-squared: 0.417
Method: Least Squares F-statistic: 4.338
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0301
Time: 04:10:34 Log-Likelihood: -69.444
No. Observations: 15 AIC: 146.9
Df Residuals: 11 BIC: 149.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 170.4521 142.916 1.193 0.258 -144.104 485.008
C(dose)[T.1] 61.3711 168.128 0.365 0.722 -308.676 431.418
expression -36.4636 50.434 -0.723 0.485 -147.469 74.542
expression:C(dose)[T.1] -5.1338 59.603 -0.086 0.933 -136.318 126.051
Omnibus: 0.563 Durbin-Watson: 1.073
Prob(Omnibus): 0.755 Jarque-Bera (JB): 0.597
Skew: -0.201 Prob(JB): 0.742
Kurtosis: 2.108 Cond. No. 104.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.542
Model: OLS Adj. R-squared: 0.465
Method: Least Squares F-statistic: 7.090
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00927
Time: 04:10:34 Log-Likelihood: -69.449
No. Observations: 15 AIC: 144.9
Df Residuals: 12 BIC: 147.0
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 180.8379 73.480 2.461 0.030 20.740 340.936
C(dose)[T.1] 46.9477 14.425 3.255 0.007 15.519 78.377
expression -40.1395 25.741 -1.559 0.145 -96.224 15.945
Omnibus: 0.607 Durbin-Watson: 1.094
Prob(Omnibus): 0.738 Jarque-Bera (JB): 0.618
Skew: -0.206 Prob(JB): 0.734
Kurtosis: 2.095 Cond. No. 32.9

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: 04:10:34 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: 0.137
Model: OLS Adj. R-squared: 0.071
Method: Least Squares F-statistic: 2.065
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.174
Time: 04:10:34 Log-Likelihood: -74.195
No. Observations: 15 AIC: 152.4
Df Residuals: 13 BIC: 153.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 229.2902 94.859 2.417 0.031 24.360 434.221
expression -48.5149 33.764 -1.437 0.174 -121.458 24.428
Omnibus: 0.569 Durbin-Watson: 1.670
Prob(Omnibus): 0.752 Jarque-Bera (JB): 0.620
Skew: 0.331 Prob(JB): 0.734
Kurtosis: 2.256 Cond. No. 31.8