LID SPN TRAP

1423860_at

Ptgds prostaglandin D2 synthase (brain)

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
0.022 0.884 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.675
Model: OLS Adj. R-squared: 0.624
Method: Least Squares F-statistic: 13.17
Date: Thu, 03 Apr 2025 Prob (F-statistic): 6.94e-05
Time: 22:51:22 Log-Likelihood: -100.17
No. Observations: 23 AIC: 208.3
Df Residuals: 19 BIC: 212.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 122.1742 73.876 1.654 0.115 -32.450 276.798
C(dose)[T.1] -55.1419 88.866 -0.621 0.542 -241.140 130.857
expression -11.7464 12.726 -0.923 0.368 -38.382 14.889
expression:C(dose)[T.1] 19.0917 15.519 1.230 0.234 -13.391 51.574
Omnibus: 0.206 Durbin-Watson: 1.973
Prob(Omnibus): 0.902 Jarque-Bera (JB): 0.260
Skew: -0.190 Prob(JB): 0.878
Kurtosis: 2.644 Cond. No. 169.

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: 18.53
Date: Thu, 03 Apr 2025 Prob (F-statistic): 2.80e-05
Time: 22:51:22 Log-Likelihood: -101.05
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 47.8980 43.112 1.111 0.280 -42.031 137.827
C(dose)[T.1] 53.6322 8.989 5.966 0.000 34.880 72.384
expression 1.0906 7.377 0.148 0.884 -14.297 16.478
Omnibus: 0.259 Durbin-Watson: 1.869
Prob(Omnibus): 0.878 Jarque-Bera (JB): 0.446
Skew: 0.043 Prob(JB): 0.800
Kurtosis: 2.323 Cond. No. 58.0

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: 22:51:23 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: 0.026
Model: OLS Adj. R-squared: -0.021
Method: Least Squares F-statistic: 0.5504
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.466
Time: 22:51:23 Log-Likelihood: -112.81
No. Observations: 23 AIC: 229.6
Df Residuals: 21 BIC: 231.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 128.8302 66.582 1.935 0.067 -9.635 267.296
expression -8.6822 11.703 -0.742 0.466 -33.020 15.655
Omnibus: 1.837 Durbin-Watson: 2.557
Prob(Omnibus): 0.399 Jarque-Bera (JB): 1.490
Skew: 0.468 Prob(JB): 0.475
Kurtosis: 2.177 Cond. No. 54.8

CP101

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

F-statistic p-value df difference
0.048 0.830 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.457
Model: OLS Adj. R-squared: 0.309
Method: Least Squares F-statistic: 3.091
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0718
Time: 22:51:23 Log-Likelihood: -70.715
No. Observations: 15 AIC: 149.4
Df Residuals: 11 BIC: 152.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 122.5414 236.974 0.517 0.615 -399.034 644.117
C(dose)[T.1] -42.5987 259.775 -0.164 0.873 -614.359 529.162
expression -7.6512 32.857 -0.233 0.820 -79.969 64.666
expression:C(dose)[T.1] 13.1510 36.488 0.360 0.725 -67.159 93.461
Omnibus: 2.996 Durbin-Watson: 0.858
Prob(Omnibus): 0.224 Jarque-Bera (JB): 1.825
Skew: -0.852 Prob(JB): 0.402
Kurtosis: 2.861 Cond. No. 342.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.451
Model: OLS Adj. R-squared: 0.359
Method: Least Squares F-statistic: 4.928
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0274
Time: 22:51:23 Log-Likelihood: -70.803
No. Observations: 15 AIC: 147.6
Df Residuals: 12 BIC: 149.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 45.7295 99.790 0.458 0.655 -171.694 263.153
C(dose)[T.1] 50.8033 17.339 2.930 0.013 13.025 88.582
expression 3.0124 13.762 0.219 0.830 -26.972 32.997
Omnibus: 2.985 Durbin-Watson: 0.882
Prob(Omnibus): 0.225 Jarque-Bera (JB): 1.935
Skew: -0.871 Prob(JB): 0.380
Kurtosis: 2.753 Cond. No. 90.8

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: 22:51:23 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: 0.058
Model: OLS Adj. R-squared: -0.014
Method: Least Squares F-statistic: 0.8030
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.386
Time: 22:51:23 Log-Likelihood: -74.851
No. Observations: 15 AIC: 153.7
Df Residuals: 13 BIC: 155.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 190.9358 108.991 1.752 0.103 -44.525 426.397
expression -14.0589 15.689 -0.896 0.386 -47.952 19.834
Omnibus: 0.953 Durbin-Watson: 1.255
Prob(Omnibus): 0.621 Jarque-Bera (JB): 0.734
Skew: 0.182 Prob(JB): 0.693
Kurtosis: 1.979 Cond. No. 78.4