LID SPN TRAP

1460001_at

Pgpep1 pyroglutamyl-peptidase I

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
0.008 0.929 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: 12.48
Date: Thu, 21 Nov 2024 Prob (F-statistic): 9.73e-05
Time: 05:12:47 Log-Likelihood: -100.59
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 29.9655 38.192 0.785 0.442 -49.972 109.903
C(dose)[T.1] 106.9940 61.009 1.754 0.096 -20.700 234.688
expression 5.9197 9.206 0.643 0.528 -13.349 25.189
expression:C(dose)[T.1] -12.7350 14.286 -0.891 0.384 -42.636 17.167
Omnibus: 0.192 Durbin-Watson: 1.859
Prob(Omnibus): 0.908 Jarque-Bera (JB): 0.266
Skew: -0.185 Prob(JB): 0.875
Kurtosis: 2.625 Cond. No. 77.6

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.649
Model: OLS Adj. R-squared: 0.614
Method: Least Squares F-statistic: 18.51
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.82e-05
Time: 05:12:47 Log-Likelihood: -101.06
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 51.6236 29.316 1.761 0.094 -9.528 112.776
C(dose)[T.1] 53.1979 8.903 5.975 0.000 34.626 71.769
expression 0.6311 7.004 0.090 0.929 -13.978 15.240
Omnibus: 0.337 Durbin-Watson: 1.890
Prob(Omnibus): 0.845 Jarque-Bera (JB): 0.493
Skew: 0.051 Prob(JB): 0.781
Kurtosis: 2.290 Cond. No. 30.2

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: 05:12:47 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: 0.023
Model: OLS Adj. R-squared: -0.024
Method: Least Squares F-statistic: 0.4936
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.490
Time: 05:12:47 Log-Likelihood: -112.84
No. Observations: 23 AIC: 229.7
Df Residuals: 21 BIC: 231.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 46.5642 47.726 0.976 0.340 -52.687 145.815
expression 7.8921 11.233 0.703 0.490 -15.469 31.253
Omnibus: 1.639 Durbin-Watson: 2.356
Prob(Omnibus): 0.441 Jarque-Bera (JB): 1.222
Skew: 0.340 Prob(JB): 0.543
Kurtosis: 2.099 Cond. No. 30.0

CP101

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

F-statistic p-value df difference
0.131 0.724 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.456
Model: OLS Adj. R-squared: 0.308
Method: Least Squares F-statistic: 3.074
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0727
Time: 05:12:47 Log-Likelihood: -70.734
No. Observations: 15 AIC: 149.5
Df Residuals: 11 BIC: 152.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 93.9046 135.733 0.692 0.503 -204.841 392.650
C(dose)[T.1] 81.8902 225.706 0.363 0.724 -414.885 578.665
expression -4.3208 22.066 -0.196 0.848 -52.887 44.245
expression:C(dose)[T.1] -6.4077 39.395 -0.163 0.874 -93.115 80.299
Omnibus: 2.689 Durbin-Watson: 0.939
Prob(Omnibus): 0.261 Jarque-Bera (JB): 1.695
Skew: -0.816 Prob(JB): 0.428
Kurtosis: 2.775 Cond. No. 203.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.455
Model: OLS Adj. R-squared: 0.364
Method: Least Squares F-statistic: 5.003
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0263
Time: 05:12:47 Log-Likelihood: -70.752
No. Observations: 15 AIC: 147.5
Df Residuals: 12 BIC: 149.6
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 106.2229 107.975 0.984 0.345 -129.035 341.480
C(dose)[T.1] 45.3194 18.979 2.388 0.034 3.967 86.671
expression -6.3311 17.522 -0.361 0.724 -44.509 31.847
Omnibus: 2.834 Durbin-Watson: 0.970
Prob(Omnibus): 0.242 Jarque-Bera (JB): 1.840
Skew: -0.848 Prob(JB): 0.398
Kurtosis: 2.737 Cond. No. 83.6

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: 05:12:47 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: 0.196
Model: OLS Adj. R-squared: 0.134
Method: Least Squares F-statistic: 3.161
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0988
Time: 05:12:47 Log-Likelihood: -73.668
No. Observations: 15 AIC: 151.3
Df Residuals: 13 BIC: 152.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 267.6163 98.258 2.724 0.017 55.342 479.891
expression -29.9865 16.865 -1.778 0.099 -66.422 6.449
Omnibus: 0.033 Durbin-Watson: 1.593
Prob(Omnibus): 0.983 Jarque-Bera (JB): 0.201
Skew: -0.090 Prob(JB): 0.904
Kurtosis: 2.462 Cond. No. 64.6