LID SPN TRAP

1456226_x_at

Ddr1 discoidin domain receptor family, member 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
0.914 0.350 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.668
Model: OLS Adj. R-squared: 0.616
Method: Least Squares F-statistic: 12.77
Date: Thu, 21 Nov 2024 Prob (F-statistic): 8.43e-05
Time: 04:12:30 Log-Likelihood: -100.41
No. Observations: 23 AIC: 208.8
Df Residuals: 19 BIC: 213.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 6.0769 128.651 0.047 0.963 -263.193 275.347
C(dose)[T.1] -33.9745 188.405 -0.180 0.859 -428.311 360.362
expression 6.5218 17.413 0.375 0.712 -29.924 42.968
expression:C(dose)[T.1] 12.5089 26.009 0.481 0.636 -41.928 66.946
Omnibus: 0.447 Durbin-Watson: 1.906
Prob(Omnibus): 0.800 Jarque-Bera (JB): 0.390
Skew: -0.278 Prob(JB): 0.823
Kurtosis: 2.689 Cond. No. 404.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.664
Model: OLS Adj. R-squared: 0.631
Method: Least Squares F-statistic: 19.80
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.81e-05
Time: 04:12:30 Log-Likelihood: -100.55
No. Observations: 23 AIC: 207.1
Df Residuals: 20 BIC: 210.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -35.3033 93.792 -0.376 0.711 -230.951 160.344
C(dose)[T.1] 56.5270 9.202 6.143 0.000 37.332 75.722
expression 12.1288 12.683 0.956 0.350 -14.328 38.586
Omnibus: 0.524 Durbin-Watson: 2.067
Prob(Omnibus): 0.770 Jarque-Bera (JB): 0.623
Skew: -0.273 Prob(JB): 0.732
Kurtosis: 2.406 Cond. No. 162.

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: 04:12:30 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: 0.031
Model: OLS Adj. R-squared: -0.015
Method: Least Squares F-statistic: 0.6761
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.420
Time: 04:12:30 Log-Likelihood: -112.74
No. Observations: 23 AIC: 229.5
Df Residuals: 21 BIC: 231.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 196.6272 142.362 1.381 0.182 -99.431 492.685
expression -16.1160 19.600 -0.822 0.420 -56.877 24.645
Omnibus: 1.924 Durbin-Watson: 2.347
Prob(Omnibus): 0.382 Jarque-Bera (JB): 1.445
Skew: 0.421 Prob(JB): 0.486
Kurtosis: 2.106 Cond. No. 148.

CP101

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

F-statistic p-value df difference
2.403 0.147 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.555
Model: OLS Adj. R-squared: 0.433
Method: Least Squares F-statistic: 4.567
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0260
Time: 04:12:30 Log-Likelihood: -69.233
No. Observations: 15 AIC: 146.5
Df Residuals: 11 BIC: 149.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -135.9177 236.301 -0.575 0.577 -656.012 384.177
C(dose)[T.1] -191.3936 401.447 -0.477 0.643 -1074.973 692.185
expression 24.0526 27.921 0.861 0.407 -37.402 85.507
expression:C(dose)[T.1] 27.4754 46.874 0.586 0.570 -75.693 130.644
Omnibus: 1.209 Durbin-Watson: 0.583
Prob(Omnibus): 0.546 Jarque-Bera (JB): 1.030
Skew: -0.513 Prob(JB): 0.597
Kurtosis: 2.228 Cond. No. 590.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.541
Model: OLS Adj. R-squared: 0.464
Method: Least Squares F-statistic: 7.064
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00938
Time: 04:12:30 Log-Likelihood: -69.464
No. Observations: 15 AIC: 144.9
Df Residuals: 12 BIC: 147.1
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -218.3371 184.645 -1.182 0.260 -620.644 183.970
C(dose)[T.1] 43.7475 14.791 2.958 0.012 11.522 75.973
expression 33.8014 21.805 1.550 0.147 -13.708 81.311
Omnibus: 1.671 Durbin-Watson: 0.646
Prob(Omnibus): 0.434 Jarque-Bera (JB): 1.218
Skew: -0.490 Prob(JB): 0.544
Kurtosis: 2.006 Cond. No. 224.

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: 04:12:30 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: 0.206
Model: OLS Adj. R-squared: 0.145
Method: Least Squares F-statistic: 3.371
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0893
Time: 04:12:30 Log-Likelihood: -73.571
No. Observations: 15 AIC: 151.1
Df Residuals: 13 BIC: 152.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -325.9078 228.701 -1.425 0.178 -819.987 168.172
expression 49.1292 26.758 1.836 0.089 -8.679 106.937
Omnibus: 2.158 Durbin-Watson: 1.477
Prob(Omnibus): 0.340 Jarque-Bera (JB): 0.997
Skew: 0.113 Prob(JB): 0.607
Kurtosis: 1.757 Cond. No. 219.