LID SPN TRAP

1460269_at

Pnmt phenylethanolamine-N-methyltransferase

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
0.009 0.926 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: 12.59
Date: Sun, 26 Oct 2025 Prob (F-statistic): 9.19e-05
Time: 00:42:06 Log-Likelihood: -100.52
No. Observations: 23 AIC: 209.0
Df Residuals: 19 BIC: 213.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -25.5160 137.034 -0.186 0.854 -312.330 261.299
C(dose)[T.1] 309.9934 268.987 1.152 0.263 -253.003 872.989
expression 9.6979 16.653 0.582 0.567 -25.157 44.553
expression:C(dose)[T.1] -30.3363 31.713 -0.957 0.351 -96.713 36.040
Omnibus: 2.909 Durbin-Watson: 1.920
Prob(Omnibus): 0.234 Jarque-Bera (JB): 1.348
Skew: 0.178 Prob(JB): 0.510
Kurtosis: 1.869 Cond. No. 621.

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: Sun, 26 Oct 2025 Prob (F-statistic): 2.82e-05
Time: 00:42:06 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 43.2490 116.416 0.372 0.714 -199.591 286.089
C(dose)[T.1] 52.8676 10.084 5.243 0.000 31.833 73.902
expression 1.3331 14.142 0.094 0.926 -28.167 30.833
Omnibus: 0.284 Durbin-Watson: 1.912
Prob(Omnibus): 0.868 Jarque-Bera (JB): 0.462
Skew: 0.045 Prob(JB): 0.794
Kurtosis: 2.312 Cond. No. 227.

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: Sun, 26 Oct 2025 Prob (F-statistic): 3.51e-06
Time: 00:42:06 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: 0.167
Model: OLS Adj. R-squared: 0.127
Method: Least Squares F-statistic: 4.214
Date: Sun, 26 Oct 2025 Prob (F-statistic): 0.0528
Time: 00:42:06 Log-Likelihood: -111.00
No. Observations: 23 AIC: 226.0
Df Residuals: 21 BIC: 228.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -238.7059 155.261 -1.537 0.139 -561.588 84.176
expression 37.9563 18.491 2.053 0.053 -0.497 76.410
Omnibus: 0.937 Durbin-Watson: 2.401
Prob(Omnibus): 0.626 Jarque-Bera (JB): 0.862
Skew: 0.257 Prob(JB): 0.650
Kurtosis: 2.203 Cond. No. 201.

CP101

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

F-statistic p-value df difference
5.129 0.043 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.617
Model: OLS Adj. R-squared: 0.512
Method: Least Squares F-statistic: 5.895
Date: Sun, 26 Oct 2025 Prob (F-statistic): 0.0119
Time: 00:42:06 Log-Likelihood: -68.112
No. Observations: 15 AIC: 144.2
Df Residuals: 11 BIC: 147.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -140.1622 166.324 -0.843 0.417 -506.239 225.915
C(dose)[T.1] 94.3468 189.406 0.498 0.628 -322.533 511.227
expression 26.4773 21.175 1.250 0.237 -20.129 73.084
expression:C(dose)[T.1] -6.6437 23.864 -0.278 0.786 -59.168 45.881
Omnibus: 0.664 Durbin-Watson: 1.260
Prob(Omnibus): 0.718 Jarque-Bera (JB): 0.680
Skew: -0.362 Prob(JB): 0.712
Kurtosis: 2.249 Cond. No. 343.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.614
Model: OLS Adj. R-squared: 0.549
Method: Least Squares F-statistic: 9.537
Date: Sun, 26 Oct 2025 Prob (F-statistic): 0.00332
Time: 00:42:06 Log-Likelihood: -68.164
No. Observations: 15 AIC: 142.3
Df Residuals: 12 BIC: 144.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -99.1495 74.183 -1.337 0.206 -260.781 62.482
C(dose)[T.1] 41.7636 13.577 3.076 0.010 12.182 71.345
expression 21.2463 9.382 2.265 0.043 0.805 41.688
Omnibus: 0.604 Durbin-Watson: 1.133
Prob(Omnibus): 0.740 Jarque-Bera (JB): 0.591
Skew: -0.382 Prob(JB): 0.744
Kurtosis: 2.399 Cond. No. 92.6

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: Sun, 26 Oct 2025 Prob (F-statistic): 0.00629
Time: 00:42:06 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: 0.309
Model: OLS Adj. R-squared: 0.256
Method: Least Squares F-statistic: 5.822
Date: Sun, 26 Oct 2025 Prob (F-statistic): 0.0313
Time: 00:42:06 Log-Likelihood: -72.525
No. Observations: 15 AIC: 149.0
Df Residuals: 13 BIC: 150.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -132.8757 94.270 -1.410 0.182 -336.534 70.783
expression 28.2228 11.697 2.413 0.031 2.953 53.493
Omnibus: 0.396 Durbin-Watson: 1.425
Prob(Omnibus): 0.820 Jarque-Bera (JB): 0.424
Skew: 0.312 Prob(JB): 0.809
Kurtosis: 2.462 Cond. No. 91.3