LID SPN TRAP

1459716_at

AI835735 expressed sequence AI835735

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
4.652 0.043 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.743
Model: OLS Adj. R-squared: 0.703
Method: Least Squares F-statistic: 18.35
Date: Thu, 21 Nov 2024 Prob (F-statistic): 7.72e-06
Time: 05:09:29 Log-Likelihood: -97.459
No. Observations: 23 AIC: 202.9
Df Residuals: 19 BIC: 207.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 6.3687 54.955 0.116 0.909 -108.653 121.391
C(dose)[T.1] -95.7374 96.325 -0.994 0.333 -297.348 105.873
expression 7.9799 9.124 0.875 0.393 -11.116 27.076
expression:C(dose)[T.1] 21.6155 14.964 1.445 0.165 -9.704 52.935
Omnibus: 2.082 Durbin-Watson: 2.099
Prob(Omnibus): 0.353 Jarque-Bera (JB): 1.772
Skew: 0.630 Prob(JB): 0.412
Kurtosis: 2.486 Cond. No. 201.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.715
Model: OLS Adj. R-squared: 0.687
Method: Least Squares F-statistic: 25.12
Date: Thu, 21 Nov 2024 Prob (F-statistic): 3.50e-06
Time: 05:09:29 Log-Likelihood: -98.658
No. Observations: 23 AIC: 203.3
Df Residuals: 20 BIC: 206.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -41.8055 44.850 -0.932 0.362 -135.360 51.749
C(dose)[T.1] 42.7900 9.290 4.606 0.000 23.411 62.169
expression 16.0157 7.425 2.157 0.043 0.526 31.505
Omnibus: 0.205 Durbin-Watson: 2.192
Prob(Omnibus): 0.903 Jarque-Bera (JB): 0.407
Skew: 0.095 Prob(JB): 0.816
Kurtosis: 2.377 Cond. No. 74.5

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: 05:09:29 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: 0.413
Model: OLS Adj. R-squared: 0.385
Method: Least Squares F-statistic: 14.79
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000938
Time: 05:09:29 Log-Likelihood: -106.97
No. Observations: 23 AIC: 217.9
Df Residuals: 21 BIC: 220.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -134.9330 56.084 -2.406 0.025 -251.565 -18.301
expression 34.0178 8.845 3.846 0.001 15.624 52.412
Omnibus: 8.584 Durbin-Watson: 2.653
Prob(Omnibus): 0.014 Jarque-Bera (JB): 7.876
Skew: -0.752 Prob(JB): 0.0195
Kurtosis: 5.440 Cond. No. 65.9

CP101

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

F-statistic p-value df difference
0.419 0.530 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.520
Model: OLS Adj. R-squared: 0.390
Method: Least Squares F-statistic: 3.978
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0382
Time: 05:09:29 Log-Likelihood: -69.789
No. Observations: 15 AIC: 147.6
Df Residuals: 11 BIC: 150.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 53.5074 111.674 0.479 0.641 -192.286 299.301
C(dose)[T.1] -223.5910 251.209 -0.890 0.392 -776.499 329.317
expression 2.1797 17.397 0.125 0.903 -36.111 40.471
expression:C(dose)[T.1] 45.3805 41.146 1.103 0.294 -45.181 135.942
Omnibus: 2.632 Durbin-Watson: 1.211
Prob(Omnibus): 0.268 Jarque-Bera (JB): 1.825
Skew: -0.831 Prob(JB): 0.402
Kurtosis: 2.601 Cond. No. 245.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.467
Model: OLS Adj. R-squared: 0.379
Method: Least Squares F-statistic: 5.265
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0228
Time: 05:09:29 Log-Likelihood: -70.576
No. Observations: 15 AIC: 147.2
Df Residuals: 12 BIC: 149.3
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 1.6926 102.221 0.017 0.987 -221.028 224.413
C(dose)[T.1] 52.8853 16.489 3.207 0.008 16.959 88.812
expression 10.2926 15.907 0.647 0.530 -24.366 44.951
Omnibus: 2.410 Durbin-Watson: 0.865
Prob(Omnibus): 0.300 Jarque-Bera (JB): 1.747
Skew: -0.798 Prob(JB): 0.418
Kurtosis: 2.500 Cond. No. 84.9

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: 05:09:29 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: 0.011
Model: OLS Adj. R-squared: -0.065
Method: Least Squares F-statistic: 0.1413
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.713
Time: 05:09:29 Log-Likelihood: -75.219
No. Observations: 15 AIC: 154.4
Df Residuals: 13 BIC: 155.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 139.1881 121.503 1.146 0.273 -123.304 401.680
expression -7.3474 19.543 -0.376 0.713 -49.568 34.873
Omnibus: 0.621 Durbin-Watson: 1.516
Prob(Omnibus): 0.733 Jarque-Bera (JB): 0.586
Skew: 0.028 Prob(JB): 0.746
Kurtosis: 2.033 Cond. No. 76.7