LID SPN TRAP

1434877_at

Nptx1 neuronal pentraxin 1

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
0.727 0.404 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.665
Model: OLS Adj. R-squared: 0.612
Method: Least Squares F-statistic: 12.55
Date: Thu, 03 Apr 2025 Prob (F-statistic): 9.37e-05
Time: 22:47:16 Log-Likelihood: -100.54
No. Observations: 23 AIC: 209.1
Df Residuals: 19 BIC: 213.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -111.2912 179.176 -0.621 0.542 -486.311 263.728
C(dose)[T.1] 176.6784 310.769 0.569 0.576 -473.768 827.125
expression 16.5421 17.899 0.924 0.367 -20.920 54.005
expression:C(dose)[T.1] -12.6514 29.482 -0.429 0.673 -74.357 49.054
Omnibus: 0.003 Durbin-Watson: 1.973
Prob(Omnibus): 0.999 Jarque-Bera (JB): 0.171
Skew: 0.018 Prob(JB): 0.918
Kurtosis: 2.580 Cond. No. 914.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.661
Model: OLS Adj. R-squared: 0.628
Method: Least Squares F-statistic: 19.53
Date: Thu, 03 Apr 2025 Prob (F-statistic): 1.98e-05
Time: 22:47:16 Log-Likelihood: -100.65
No. Observations: 23 AIC: 207.3
Df Residuals: 20 BIC: 210.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -64.6370 139.488 -0.463 0.648 -355.604 226.330
C(dose)[T.1] 43.4685 14.427 3.013 0.007 13.375 73.562
expression 11.8789 13.929 0.853 0.404 -17.177 40.935
Omnibus: 0.014 Durbin-Watson: 2.028
Prob(Omnibus): 0.993 Jarque-Bera (JB): 0.119
Skew: -0.034 Prob(JB): 0.942
Kurtosis: 2.654 Cond. No. 342.

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: 22:47:16 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: 0.508
Model: OLS Adj. R-squared: 0.484
Method: Least Squares F-statistic: 21.65
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.000136
Time: 22:47:16 Log-Likelihood: -104.96
No. Observations: 23 AIC: 213.9
Df Residuals: 21 BIC: 216.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -394.0466 101.939 -3.866 0.001 -606.040 -182.053
expression 45.5452 9.788 4.653 0.000 25.190 65.900
Omnibus: 2.011 Durbin-Watson: 2.297
Prob(Omnibus): 0.366 Jarque-Bera (JB): 1.104
Skew: 0.124 Prob(JB): 0.576
Kurtosis: 1.956 Cond. No. 212.

CP101

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

F-statistic p-value df difference
2.557 0.136 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.573
Model: OLS Adj. R-squared: 0.457
Method: Least Squares F-statistic: 4.928
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0208
Time: 22:47:16 Log-Likelihood: -68.911
No. Observations: 15 AIC: 145.8
Df Residuals: 11 BIC: 148.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -282.3209 200.313 -1.409 0.186 -723.207 158.565
C(dose)[T.1] 302.5508 316.312 0.956 0.359 -393.648 998.750
expression 40.8035 23.337 1.748 0.108 -10.561 92.168
expression:C(dose)[T.1] -30.1747 35.666 -0.846 0.416 -108.674 48.325
Omnibus: 2.245 Durbin-Watson: 0.992
Prob(Omnibus): 0.325 Jarque-Bera (JB): 1.478
Skew: -0.752 Prob(JB): 0.478
Kurtosis: 2.677 Cond. No. 506.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.546
Model: OLS Adj. R-squared: 0.470
Method: Least Squares F-statistic: 7.204
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.00880
Time: 22:47:16 Log-Likelihood: -69.384
No. Observations: 15 AIC: 144.8
Df Residuals: 12 BIC: 146.9
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -171.5829 149.830 -1.145 0.274 -498.034 154.868
C(dose)[T.1] 35.3186 16.719 2.112 0.056 -1.110 71.747
expression 27.8843 17.437 1.599 0.136 -10.109 65.877
Omnibus: 1.818 Durbin-Watson: 0.938
Prob(Omnibus): 0.403 Jarque-Bera (JB): 1.387
Skew: -0.588 Prob(JB): 0.500
Kurtosis: 2.085 Cond. No. 189.

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: 22:47:16 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: 0.377
Model: OLS Adj. R-squared: 0.329
Method: Least Squares F-statistic: 7.854
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0150
Time: 22:47:16 Log-Likelihood: -71.755
No. Observations: 15 AIC: 147.5
Df Residuals: 13 BIC: 148.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -321.7122 148.432 -2.167 0.049 -642.379 -1.045
expression 47.0046 16.772 2.803 0.015 10.771 83.238
Omnibus: 2.349 Durbin-Watson: 1.528
Prob(Omnibus): 0.309 Jarque-Bera (JB): 1.267
Skew: 0.388 Prob(JB): 0.531
Kurtosis: 1.807 Cond. No. 166.