LID SPN TRAP

1460573_at

AI848100 expressed sequence AI848100

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
0.087 0.771 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.663
Model: OLS Adj. R-squared: 0.610
Method: Least Squares F-statistic: 12.45
Date: Thu, 21 Nov 2024 Prob (F-statistic): 9.83e-05
Time: 03:37:34 Log-Likelihood: -100.60
No. Observations: 23 AIC: 209.2
Df Residuals: 19 BIC: 213.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 139.5194 104.737 1.332 0.199 -79.699 358.737
C(dose)[T.1] -66.1432 145.475 -0.455 0.654 -370.625 238.339
expression -10.0308 12.294 -0.816 0.425 -35.763 15.701
expression:C(dose)[T.1] 13.7694 16.513 0.834 0.415 -20.793 48.332
Omnibus: 1.166 Durbin-Watson: 1.899
Prob(Omnibus): 0.558 Jarque-Bera (JB): 0.833
Skew: -0.027 Prob(JB): 0.659
Kurtosis: 2.069 Cond. No. 392.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.651
Model: OLS Adj. R-squared: 0.616
Method: Least Squares F-statistic: 18.62
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.71e-05
Time: 03:37:34 Log-Likelihood: -101.01
No. Observations: 23 AIC: 208.0
Df Residuals: 20 BIC: 211.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 74.6080 69.536 1.073 0.296 -70.441 219.657
C(dose)[T.1] 54.8597 10.164 5.397 0.000 33.658 76.062
expression -2.3986 8.145 -0.294 0.771 -19.389 14.591
Omnibus: 0.278 Durbin-Watson: 1.860
Prob(Omnibus): 0.870 Jarque-Bera (JB): 0.458
Skew: 0.042 Prob(JB): 0.795
Kurtosis: 2.314 Cond. No. 143.

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: 03:37:34 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: 0.142
Model: OLS Adj. R-squared: 0.101
Method: Least Squares F-statistic: 3.464
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0768
Time: 03:37:34 Log-Likelihood: -111.35
No. Observations: 23 AIC: 226.7
Df Residuals: 21 BIC: 229.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -96.1302 94.717 -1.015 0.322 -293.105 100.845
expression 19.9634 10.726 1.861 0.077 -2.343 42.270
Omnibus: 1.801 Durbin-Watson: 2.414
Prob(Omnibus): 0.406 Jarque-Bera (JB): 1.028
Skew: 0.078 Prob(JB): 0.598
Kurtosis: 1.976 Cond. No. 127.

CP101

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

F-statistic p-value df difference
0.258 0.621 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.462
Model: OLS Adj. R-squared: 0.315
Method: Least Squares F-statistic: 3.148
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0687
Time: 03:37:34 Log-Likelihood: -70.651
No. Observations: 15 AIC: 149.3
Df Residuals: 11 BIC: 152.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 13.0702 113.795 0.115 0.911 -237.390 263.531
C(dose)[T.1] 76.4105 178.471 0.428 0.677 -316.402 469.223
expression 7.6499 15.927 0.480 0.640 -27.406 42.706
expression:C(dose)[T.1] -4.2442 23.437 -0.181 0.860 -55.829 47.341
Omnibus: 2.934 Durbin-Watson: 0.959
Prob(Omnibus): 0.231 Jarque-Bera (JB): 1.963
Skew: -0.872 Prob(JB): 0.375
Kurtosis: 2.689 Cond. No. 223.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.460
Model: OLS Adj. R-squared: 0.370
Method: Least Squares F-statistic: 5.119
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0247
Time: 03:37:34 Log-Likelihood: -70.674
No. Observations: 15 AIC: 147.3
Df Residuals: 12 BIC: 149.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 26.9976 80.419 0.336 0.743 -148.220 202.215
C(dose)[T.1] 44.2779 18.339 2.414 0.033 4.321 84.235
expression 5.6899 11.204 0.508 0.621 -18.721 30.101
Omnibus: 2.500 Durbin-Watson: 0.916
Prob(Omnibus): 0.286 Jarque-Bera (JB): 1.700
Skew: -0.804 Prob(JB): 0.427
Kurtosis: 2.634 Cond. No. 80.8

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: 03:37:35 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: 0.198
Model: OLS Adj. R-squared: 0.137
Method: Least Squares F-statistic: 3.214
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0963
Time: 03:37:35 Log-Likelihood: -73.643
No. Observations: 15 AIC: 151.3
Df Residuals: 13 BIC: 152.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -57.4802 84.798 -0.678 0.510 -240.674 125.714
expression 19.9751 11.142 1.793 0.096 -4.095 44.046
Omnibus: 0.178 Durbin-Watson: 1.572
Prob(Omnibus): 0.915 Jarque-Bera (JB): 0.139
Skew: 0.161 Prob(JB): 0.933
Kurtosis: 2.655 Cond. No. 72.1