LID SPN TRAP

1452919_a_at

Pgp phosphoglycolate phosphatase

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
0.127 0.726 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.652
Model: OLS Adj. R-squared: 0.598
Method: Least Squares F-statistic: 11.89
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.000130
Time: 23:03:52 Log-Likelihood: -100.95
No. Observations: 23 AIC: 209.9
Df Residuals: 19 BIC: 214.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -24.7941 182.925 -0.136 0.894 -407.660 358.072
C(dose)[T.1] 131.5674 300.433 0.438 0.666 -497.247 760.382
expression 8.8271 20.427 0.432 0.671 -33.927 51.581
expression:C(dose)[T.1] -8.7390 34.012 -0.257 0.800 -79.926 62.448
Omnibus: 0.454 Durbin-Watson: 1.860
Prob(Omnibus): 0.797 Jarque-Bera (JB): 0.554
Skew: 0.033 Prob(JB): 0.758
Kurtosis: 2.242 Cond. No. 736.

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: 18.68
Date: Thu, 03 Apr 2025 Prob (F-statistic): 2.66e-05
Time: 23:03:52 Log-Likelihood: -100.99
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 3.4180 142.850 0.024 0.981 -294.561 301.397
C(dose)[T.1] 54.4120 9.249 5.883 0.000 35.118 73.706
expression 5.6749 15.947 0.356 0.726 -27.589 38.939
Omnibus: 0.190 Durbin-Watson: 1.865
Prob(Omnibus): 0.909 Jarque-Bera (JB): 0.399
Skew: -0.000 Prob(JB): 0.819
Kurtosis: 2.355 Cond. No. 294.

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: 23:03:52 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: 0.048
Model: OLS Adj. R-squared: 0.002
Method: Least Squares F-statistic: 1.055
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.316
Time: 23:03:53 Log-Likelihood: -112.54
No. Observations: 23 AIC: 229.1
Df Residuals: 21 BIC: 231.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 300.8411 215.445 1.396 0.177 -147.201 748.883
expression -24.9593 24.305 -1.027 0.316 -75.505 25.586
Omnibus: 2.113 Durbin-Watson: 2.577
Prob(Omnibus): 0.348 Jarque-Bera (JB): 1.224
Skew: 0.232 Prob(JB): 0.542
Kurtosis: 1.970 Cond. No. 275.

CP101

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

F-statistic p-value df difference
1.664 0.221 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.691
Model: OLS Adj. R-squared: 0.607
Method: Least Squares F-statistic: 8.206
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.00379
Time: 23:03:53 Log-Likelihood: -66.488
No. Observations: 15 AIC: 141.0
Df Residuals: 11 BIC: 143.8
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 374.4360 472.966 0.792 0.445 -666.556 1415.428
C(dose)[T.1] -1659.6632 680.509 -2.439 0.033 -3157.454 -161.873
expression -33.3502 51.369 -0.649 0.530 -146.412 79.712
expression:C(dose)[T.1] 183.0411 73.264 2.498 0.030 21.789 344.294
Omnibus: 2.997 Durbin-Watson: 0.683
Prob(Omnibus): 0.223 Jarque-Bera (JB): 1.695
Skew: -0.823 Prob(JB): 0.428
Kurtosis: 2.977 Cond. No. 1.38e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.516
Model: OLS Adj. R-squared: 0.435
Method: Least Squares F-statistic: 6.394
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0129
Time: 23:03:53 Log-Likelihood: -69.859
No. Observations: 15 AIC: 145.7
Df Residuals: 12 BIC: 147.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -453.9296 404.302 -1.123 0.284 -1334.828 426.969
C(dose)[T.1] 40.1688 16.326 2.460 0.030 4.597 75.740
expression 56.6350 43.904 1.290 0.221 -39.023 152.293
Omnibus: 2.324 Durbin-Watson: 0.879
Prob(Omnibus): 0.313 Jarque-Bera (JB): 1.741
Skew: -0.703 Prob(JB): 0.419
Kurtosis: 2.099 Cond. No. 517.

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: 23:03:53 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: 0.272
Model: OLS Adj. R-squared: 0.216
Method: Least Squares F-statistic: 4.850
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0463
Time: 23:03:53 Log-Likelihood: -72.922
No. Observations: 15 AIC: 149.8
Df Residuals: 13 BIC: 151.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -862.6935 434.364 -1.986 0.069 -1801.079 75.692
expression 102.9386 46.744 2.202 0.046 1.955 203.922
Omnibus: 2.558 Durbin-Watson: 1.768
Prob(Omnibus): 0.278 Jarque-Bera (JB): 1.465
Skew: -0.764 Prob(JB): 0.481
Kurtosis: 2.901 Cond. No. 471.