LID SPN TRAP

1435598_at

BB319198 expressed sequence BB319198

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
0.004 0.952 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.684
Model: OLS Adj. R-squared: 0.634
Method: Least Squares F-statistic: 13.68
Date: Thu, 10 Apr 2025 Prob (F-statistic): 5.47e-05
Time: 06:29:09 Log-Likelihood: -99.873
No. Observations: 23 AIC: 207.7
Df Residuals: 19 BIC: 212.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -66.0165 117.588 -0.561 0.581 -312.131 180.098
C(dose)[T.1] 306.4427 176.462 1.737 0.099 -62.896 675.782
expression 14.2027 13.874 1.024 0.319 -14.835 43.241
expression:C(dose)[T.1] -29.3097 20.390 -1.437 0.167 -71.986 13.367
Omnibus: 0.187 Durbin-Watson: 1.917
Prob(Omnibus): 0.911 Jarque-Bera (JB): 0.363
Skew: 0.160 Prob(JB): 0.834
Kurtosis: 2.474 Cond. No. 461.

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, 10 Apr 2025 Prob (F-statistic): 2.83e-05
Time: 06:29:09 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 48.8496 88.534 0.552 0.587 -135.830 233.529
C(dose)[T.1] 53.1275 9.425 5.637 0.000 33.468 72.788
expression 0.6331 10.434 0.061 0.952 -21.133 22.399
Omnibus: 0.291 Durbin-Watson: 1.886
Prob(Omnibus): 0.865 Jarque-Bera (JB): 0.466
Skew: 0.054 Prob(JB): 0.792
Kurtosis: 2.311 Cond. No. 177.

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, 10 Apr 2025 Prob (F-statistic): 3.51e-06
Time: 06:29:09 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: 0.092
Model: OLS Adj. R-squared: 0.048
Method: Least Squares F-statistic: 2.119
Date: Thu, 10 Apr 2025 Prob (F-statistic): 0.160
Time: 06:29:09 Log-Likelihood: -112.00
No. Observations: 23 AIC: 228.0
Df Residuals: 21 BIC: 230.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -111.6376 131.631 -0.848 0.406 -385.380 162.105
expression 22.1906 15.244 1.456 0.160 -9.511 53.892
Omnibus: 3.902 Durbin-Watson: 2.346
Prob(Omnibus): 0.142 Jarque-Bera (JB): 2.037
Skew: 0.457 Prob(JB): 0.361
Kurtosis: 1.864 Cond. No. 167.

CP101

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

F-statistic p-value df difference
1.493 0.245 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.511
Model: OLS Adj. R-squared: 0.377
Method: Least Squares F-statistic: 3.829
Date: Thu, 10 Apr 2025 Prob (F-statistic): 0.0423
Time: 06:29:09 Log-Likelihood: -69.938
No. Observations: 15 AIC: 147.9
Df Residuals: 11 BIC: 150.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -109.5932 335.796 -0.326 0.750 -848.674 629.488
C(dose)[T.1] 3.2240 396.744 0.008 0.994 -870.003 876.451
expression 21.0454 39.899 0.527 0.608 -66.771 108.862
expression:C(dose)[T.1] 7.2912 48.073 0.152 0.882 -98.517 113.100
Omnibus: 0.184 Durbin-Watson: 1.047
Prob(Omnibus): 0.912 Jarque-Bera (JB): 0.313
Skew: -0.209 Prob(JB): 0.855
Kurtosis: 2.430 Cond. No. 611.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.510
Model: OLS Adj. R-squared: 0.428
Method: Least Squares F-statistic: 6.239
Date: Thu, 10 Apr 2025 Prob (F-statistic): 0.0139
Time: 06:29:09 Log-Likelihood: -69.953
No. Observations: 15 AIC: 145.9
Df Residuals: 12 BIC: 148.0
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -151.8389 179.755 -0.845 0.415 -543.491 239.813
C(dose)[T.1] 63.3242 18.814 3.366 0.006 22.332 104.317
expression 26.0678 21.331 1.222 0.245 -20.409 72.545
Omnibus: 0.297 Durbin-Watson: 1.016
Prob(Omnibus): 0.862 Jarque-Bera (JB): 0.412
Skew: -0.259 Prob(JB): 0.814
Kurtosis: 2.375 Cond. No. 201.

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, 10 Apr 2025 Prob (F-statistic): 0.00629
Time: 06:29:09 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: 0.047
Model: OLS Adj. R-squared: -0.026
Method: Least Squares F-statistic: 0.6410
Date: Thu, 10 Apr 2025 Prob (F-statistic): 0.438
Time: 06:29:09 Log-Likelihood: -74.939
No. Observations: 15 AIC: 153.9
Df Residuals: 13 BIC: 155.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 240.2731 183.380 1.310 0.213 -155.895 636.441
expression -18.0497 22.544 -0.801 0.438 -66.753 30.654
Omnibus: 0.877 Durbin-Watson: 1.483
Prob(Omnibus): 0.645 Jarque-Bera (JB): 0.689
Skew: -0.122 Prob(JB): 0.709
Kurtosis: 1.979 Cond. No. 153.