LID SPN TRAP

1447474_at

Ramp1 receptor (calcitonin) activity modifying protein 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
0.865 0.363 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.672
Model: OLS Adj. R-squared: 0.621
Method: Least Squares F-statistic: 12.99
Date: Thu, 21 Nov 2024 Prob (F-statistic): 7.57e-05
Time: 05:24:05 Log-Likelihood: -100.28
No. Observations: 23 AIC: 208.6
Df Residuals: 19 BIC: 213.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 45.1942 58.367 0.774 0.448 -76.969 167.358
C(dose)[T.1] -4.1877 82.445 -0.051 0.960 -176.748 168.372
expression 1.6234 10.456 0.155 0.878 -20.261 23.508
expression:C(dose)[T.1] 10.5321 14.872 0.708 0.487 -20.594 41.659
Omnibus: 0.011 Durbin-Watson: 2.021
Prob(Omnibus): 0.994 Jarque-Bera (JB): 0.128
Skew: -0.034 Prob(JB): 0.938
Kurtosis: 2.642 Cond. No. 141.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.664
Model: OLS Adj. R-squared: 0.630
Method: Least Squares F-statistic: 19.73
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.86e-05
Time: 05:24:05 Log-Likelihood: -100.58
No. Observations: 23 AIC: 207.2
Df Residuals: 20 BIC: 210.6
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 16.2862 41.197 0.395 0.697 -69.649 102.221
C(dose)[T.1] 53.8736 8.605 6.260 0.000 35.923 71.824
expression 6.8297 7.342 0.930 0.363 -8.486 22.145
Omnibus: 0.052 Durbin-Watson: 1.908
Prob(Omnibus): 0.974 Jarque-Bera (JB): 0.273
Skew: -0.017 Prob(JB): 0.872
Kurtosis: 2.467 Cond. No. 55.2

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: 05:24:05 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: 0.004
Model: OLS Adj. R-squared: -0.043
Method: Least Squares F-statistic: 0.09293
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.763
Time: 05:24:05 Log-Likelihood: -113.05
No. Observations: 23 AIC: 230.1
Df Residuals: 21 BIC: 232.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 59.0409 68.209 0.866 0.396 -82.807 200.888
expression 3.7492 12.299 0.305 0.763 -21.828 29.326
Omnibus: 3.804 Durbin-Watson: 2.475
Prob(Omnibus): 0.149 Jarque-Bera (JB): 1.607
Skew: 0.252 Prob(JB): 0.448
Kurtosis: 1.807 Cond. No. 54.2

CP101

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

F-statistic p-value df difference
0.164 0.693 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.618
Model: OLS Adj. R-squared: 0.514
Method: Least Squares F-statistic: 5.938
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0116
Time: 05:24:05 Log-Likelihood: -68.078
No. Observations: 15 AIC: 144.2
Df Residuals: 11 BIC: 147.0
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -1.4055 66.634 -0.021 0.984 -148.067 145.256
C(dose)[T.1] 283.8492 108.540 2.615 0.024 44.955 522.743
expression 14.0828 13.479 1.045 0.319 -15.583 43.749
expression:C(dose)[T.1] -45.0177 20.836 -2.161 0.054 -90.876 0.841
Omnibus: 0.719 Durbin-Watson: 1.320
Prob(Omnibus): 0.698 Jarque-Bera (JB): 0.227
Skew: -0.299 Prob(JB): 0.893
Kurtosis: 2.916 Cond. No. 110.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.456
Model: OLS Adj. R-squared: 0.366
Method: Least Squares F-statistic: 5.034
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0259
Time: 05:24:05 Log-Likelihood: -70.731
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 90.6771 58.531 1.549 0.147 -36.850 218.205
C(dose)[T.1] 51.4434 16.589 3.101 0.009 15.300 87.587
expression -4.7564 11.745 -0.405 0.693 -30.346 20.833
Omnibus: 2.406 Durbin-Watson: 0.766
Prob(Omnibus): 0.300 Jarque-Bera (JB): 1.635
Skew: -0.787 Prob(JB): 0.442
Kurtosis: 2.631 Cond. No. 40.7

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: 05:24:05 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: 0.020
Model: OLS Adj. R-squared: -0.055
Method: Least Squares F-statistic: 0.2707
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.612
Time: 05:24:05 Log-Likelihood: -75.146
No. Observations: 15 AIC: 154.3
Df Residuals: 13 BIC: 155.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 55.5005 74.045 0.750 0.467 -104.465 215.466
expression 7.4256 14.273 0.520 0.612 -23.409 38.260
Omnibus: 1.236 Durbin-Watson: 1.627
Prob(Omnibus): 0.539 Jarque-Bera (JB): 0.816
Skew: 0.179 Prob(JB): 0.665
Kurtosis: 1.915 Cond. No. 39.6