LID SPN TRAP

1438407_at

Dsel dermatan sulfate epimerase-like

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
0.040 0.844 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.650
Model: OLS Adj. R-squared: 0.595
Method: Least Squares F-statistic: 11.75
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.000140
Time: 22:48:43 Log-Likelihood: -101.04
No. Observations: 23 AIC: 210.1
Df Residuals: 19 BIC: 214.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 91.5821 187.874 0.487 0.632 -301.642 484.807
C(dose)[T.1] 30.2786 301.603 0.100 0.921 -600.983 661.540
expression -4.2474 21.339 -0.199 0.844 -48.911 40.417
expression:C(dose)[T.1] 2.6106 34.388 0.076 0.940 -69.364 74.586
Omnibus: 0.481 Durbin-Watson: 1.825
Prob(Omnibus): 0.786 Jarque-Bera (JB): 0.569
Skew: 0.044 Prob(JB): 0.752
Kurtosis: 2.235 Cond. No. 737.

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: 18.55
Date: Thu, 03 Apr 2025 Prob (F-statistic): 2.78e-05
Time: 22:48:43 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 82.7362 143.665 0.576 0.571 -216.944 382.417
C(dose)[T.1] 53.1652 8.804 6.039 0.000 34.801 71.530
expression -3.2421 16.312 -0.199 0.844 -37.269 30.785
Omnibus: 0.539 Durbin-Watson: 1.842
Prob(Omnibus): 0.764 Jarque-Bera (JB): 0.599
Skew: 0.061 Prob(JB): 0.741
Kurtosis: 2.219 Cond. No. 292.

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, 03 Apr 2025 Prob (F-statistic): 3.51e-06
Time: 22:48:43 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: 0.011
Model: OLS Adj. R-squared: -0.036
Method: Least Squares F-statistic: 0.2357
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.632
Time: 22:48:43 Log-Likelihood: -112.98
No. Observations: 23 AIC: 230.0
Df Residuals: 21 BIC: 232.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 193.0965 233.669 0.826 0.418 -292.845 679.038
expression -12.9224 26.620 -0.485 0.632 -68.281 42.437
Omnibus: 2.497 Durbin-Watson: 2.505
Prob(Omnibus): 0.287 Jarque-Bera (JB): 1.404
Skew: 0.297 Prob(JB): 0.496
Kurtosis: 1.945 Cond. No. 290.

CP101

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

F-statistic p-value df difference
0.442 0.519 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.548
Model: OLS Adj. R-squared: 0.425
Method: Least Squares F-statistic: 4.448
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0280
Time: 22:48:43 Log-Likelihood: -69.342
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 -565.6348 408.348 -1.385 0.193 -1464.402 333.132
C(dose)[T.1] 711.6367 475.356 1.497 0.163 -334.615 1757.889
expression 72.1396 46.516 1.551 0.149 -30.241 174.521
expression:C(dose)[T.1] -75.4915 54.160 -1.394 0.191 -194.697 43.714
Omnibus: 2.825 Durbin-Watson: 1.265
Prob(Omnibus): 0.244 Jarque-Bera (JB): 1.465
Skew: -0.765 Prob(JB): 0.481
Kurtosis: 3.072 Cond. No. 841.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.468
Model: OLS Adj. R-squared: 0.380
Method: Least Squares F-statistic: 5.286
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0226
Time: 22:48:43 Log-Likelihood: -70.562
No. Observations: 15 AIC: 147.1
Df Residuals: 12 BIC: 149.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -76.9630 217.437 -0.354 0.730 -550.717 396.791
C(dose)[T.1] 49.3828 15.460 3.194 0.008 15.698 83.067
expression 16.4539 24.744 0.665 0.519 -37.459 70.367
Omnibus: 3.555 Durbin-Watson: 0.754
Prob(Omnibus): 0.169 Jarque-Bera (JB): 2.190
Skew: -0.935 Prob(JB): 0.335
Kurtosis: 2.906 Cond. No. 251.

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, 03 Apr 2025 Prob (F-statistic): 0.00629
Time: 22:48:43 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: 0.016
Model: OLS Adj. R-squared: -0.059
Method: Least Squares F-statistic: 0.2158
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.650
Time: 22:48:44 Log-Likelihood: -75.177
No. Observations: 15 AIC: 154.4
Df Residuals: 13 BIC: 155.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -38.0625 283.718 -0.134 0.895 -650.997 574.872
expression 15.0213 32.332 0.465 0.650 -54.829 84.871
Omnibus: 1.633 Durbin-Watson: 1.743
Prob(Omnibus): 0.442 Jarque-Bera (JB): 0.899
Skew: 0.145 Prob(JB): 0.638
Kurtosis: 1.836 Cond. No. 250.