LID SPN TRAP

1457584_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
0.044 0.836 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.653
Model: OLS Adj. R-squared: 0.599
Method: Least Squares F-statistic: 11.93
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000128
Time: 05:19:19 Log-Likelihood: -100.92
No. Observations: 23 AIC: 209.8
Df Residuals: 19 BIC: 214.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 60.9966 36.425 1.675 0.110 -15.241 137.235
C(dose)[T.1] 30.5650 51.728 0.591 0.562 -77.703 138.833
expression -1.9344 10.229 -0.189 0.852 -23.344 19.475
expression:C(dose)[T.1] 5.9392 13.664 0.435 0.669 -22.660 34.538
Omnibus: 0.342 Durbin-Watson: 1.973
Prob(Omnibus): 0.843 Jarque-Bera (JB): 0.503
Skew: 0.133 Prob(JB): 0.778
Kurtosis: 2.326 Cond. No. 62.6

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.650
Model: OLS Adj. R-squared: 0.615
Method: Least Squares F-statistic: 18.56
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.77e-05
Time: 05:19:19 Log-Likelihood: -101.04
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 49.3162 24.086 2.048 0.054 -0.926 99.559
C(dose)[T.1] 52.6653 9.327 5.647 0.000 33.210 72.121
expression 1.3941 6.643 0.210 0.836 -12.463 15.251
Omnibus: 0.229 Durbin-Watson: 1.944
Prob(Omnibus): 0.892 Jarque-Bera (JB): 0.426
Skew: 0.075 Prob(JB): 0.808
Kurtosis: 2.350 Cond. No. 22.6

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: 05:19:19 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: 2.117
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.160
Time: 05:19:19 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 26.3557 37.316 0.706 0.488 -51.247 103.959
expression 14.2690 9.807 1.455 0.160 -6.127 34.665
Omnibus: 0.633 Durbin-Watson: 2.577
Prob(Omnibus): 0.729 Jarque-Bera (JB): 0.706
Skew: 0.267 Prob(JB): 0.702
Kurtosis: 2.328 Cond. No. 22.0

CP101

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

F-statistic p-value df difference
2.491 0.140 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.547
Model: OLS Adj. R-squared: 0.424
Method: Least Squares F-statistic: 4.434
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0283
Time: 05:19:19 Log-Likelihood: -69.355
No. Observations: 15 AIC: 146.7
Df Residuals: 11 BIC: 149.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 135.8950 50.426 2.695 0.021 24.907 246.883
C(dose)[T.1] 24.4157 82.298 0.297 0.772 -156.720 205.552
expression -19.0149 13.675 -1.390 0.192 -49.113 11.083
expression:C(dose)[T.1] 6.8673 22.496 0.305 0.766 -42.646 56.381
Omnibus: 0.917 Durbin-Watson: 1.293
Prob(Omnibus): 0.632 Jarque-Bera (JB): 0.576
Skew: -0.453 Prob(JB): 0.750
Kurtosis: 2.683 Cond. No. 54.6

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.544
Model: OLS Adj. R-squared: 0.467
Method: Least Squares F-statistic: 7.144
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00905
Time: 05:19:19 Log-Likelihood: -69.418
No. Observations: 15 AIC: 144.8
Df Residuals: 12 BIC: 147.0
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 126.7579 39.019 3.249 0.007 41.743 211.773
C(dose)[T.1] 49.1235 14.323 3.430 0.005 17.916 80.331
expression -16.4773 10.440 -1.578 0.140 -39.224 6.269
Omnibus: 0.484 Durbin-Watson: 1.275
Prob(Omnibus): 0.785 Jarque-Bera (JB): 0.407
Skew: -0.339 Prob(JB): 0.816
Kurtosis: 2.561 Cond. No. 21.7

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: 05:19:19 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: 0.096
Model: OLS Adj. R-squared: 0.027
Method: Least Squares F-statistic: 1.382
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.261
Time: 05:19:20 Log-Likelihood: -74.542
No. Observations: 15 AIC: 153.1
Df Residuals: 13 BIC: 154.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 153.3728 51.700 2.967 0.011 41.683 265.063
expression -16.5928 14.115 -1.176 0.261 -47.086 13.900
Omnibus: 2.139 Durbin-Watson: 1.854
Prob(Omnibus): 0.343 Jarque-Bera (JB): 1.362
Skew: 0.493 Prob(JB): 0.506
Kurtosis: 1.902 Cond. No. 21.0