LID SPN TRAP

1451342_at

Spon1 spondin 1, (f-spondin) extracellular matrix protein

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
0.223 0.642 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.685
Model: OLS Adj. R-squared: 0.635
Method: Least Squares F-statistic: 13.76
Date: Thu, 21 Nov 2024 Prob (F-statistic): 5.26e-05
Time: 03:51:01 Log-Likelihood: -99.827
No. Observations: 23 AIC: 207.7
Df Residuals: 19 BIC: 212.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -53.0598 130.854 -0.405 0.690 -326.940 220.820
C(dose)[T.1] 285.6746 166.472 1.716 0.102 -62.754 634.104
expression 14.4596 17.621 0.821 0.422 -22.421 51.341
expression:C(dose)[T.1] -30.5162 22.009 -1.387 0.182 -76.582 15.550
Omnibus: 1.215 Durbin-Watson: 1.830
Prob(Omnibus): 0.545 Jarque-Bera (JB): 0.855
Skew: 0.060 Prob(JB): 0.652
Kurtosis: 2.063 Cond. No. 418.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.653
Model: OLS Adj. R-squared: 0.618
Method: Least Squares F-statistic: 18.81
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.54e-05
Time: 03:51:01 Log-Likelihood: -100.94
No. Observations: 23 AIC: 207.9
Df Residuals: 20 BIC: 211.3
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 92.0460 80.339 1.146 0.265 -75.538 259.630
C(dose)[T.1] 55.2284 9.597 5.755 0.000 35.210 75.247
expression -5.1005 10.799 -0.472 0.642 -27.627 17.426
Omnibus: 0.328 Durbin-Watson: 1.859
Prob(Omnibus): 0.849 Jarque-Bera (JB): 0.491
Skew: 0.087 Prob(JB): 0.782
Kurtosis: 2.306 Cond. No. 143.

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: 03:51:01 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: 0.078
Model: OLS Adj. R-squared: 0.034
Method: Least Squares F-statistic: 1.781
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.196
Time: 03:51:01 Log-Likelihood: -112.17
No. Observations: 23 AIC: 228.3
Df Residuals: 21 BIC: 230.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -78.5105 118.763 -0.661 0.516 -325.491 168.470
expression 20.8309 15.609 1.335 0.196 -11.629 53.291
Omnibus: 2.380 Durbin-Watson: 2.452
Prob(Omnibus): 0.304 Jarque-Bera (JB): 1.765
Skew: 0.506 Prob(JB): 0.414
Kurtosis: 2.095 Cond. No. 133.

CP101

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

F-statistic p-value df difference
1.138 0.307 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.509
Model: OLS Adj. R-squared: 0.375
Method: Least Squares F-statistic: 3.805
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0430
Time: 03:51:01 Log-Likelihood: -69.961
No. Observations: 15 AIC: 147.9
Df Residuals: 11 BIC: 150.8
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -83.3279 147.374 -0.565 0.583 -407.695 241.039
C(dose)[T.1] 146.6540 176.577 0.831 0.424 -241.990 535.298
expression 19.3321 18.842 1.026 0.327 -22.140 60.804
expression:C(dose)[T.1] -12.2224 22.834 -0.535 0.603 -62.479 38.034
Omnibus: 1.810 Durbin-Watson: 0.902
Prob(Omnibus): 0.404 Jarque-Bera (JB): 1.431
Skew: -0.658 Prob(JB): 0.489
Kurtosis: 2.255 Cond. No. 256.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.497
Model: OLS Adj. R-squared: 0.413
Method: Least Squares F-statistic: 5.917
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0163
Time: 03:51:01 Log-Likelihood: -70.154
No. Observations: 15 AIC: 146.3
Df Residuals: 12 BIC: 148.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -18.4235 81.237 -0.227 0.824 -195.424 158.577
C(dose)[T.1] 52.5170 15.362 3.419 0.005 19.047 85.987
expression 11.0091 10.322 1.067 0.307 -11.480 33.498
Omnibus: 1.571 Durbin-Watson: 0.762
Prob(Omnibus): 0.456 Jarque-Bera (JB): 1.268
Skew: -0.588 Prob(JB): 0.531
Kurtosis: 2.198 Cond. No. 84.7

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: 03:51:01 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: 0.006
Model: OLS Adj. R-squared: -0.070
Method: Least Squares F-statistic: 0.07995
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.782
Time: 03:51:01 Log-Likelihood: -75.254
No. Observations: 15 AIC: 154.5
Df Residuals: 13 BIC: 155.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 64.2035 104.693 0.613 0.550 -161.973 290.380
expression 3.8578 13.644 0.283 0.782 -25.618 33.333
Omnibus: 0.932 Durbin-Watson: 1.672
Prob(Omnibus): 0.628 Jarque-Bera (JB): 0.702
Skew: 0.111 Prob(JB): 0.704
Kurtosis: 1.963 Cond. No. 80.6