LID SPN TRAP

1429617_at

Cyld cylindromatosis (turban tumor syndrome)

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
0.286 0.599 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.656
Model: OLS Adj. R-squared: 0.601
Method: Least Squares F-statistic: 12.06
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000120
Time: 04:54:03 Log-Likelihood: -100.84
No. Observations: 23 AIC: 209.7
Df Residuals: 19 BIC: 214.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -181.2166 498.777 -0.363 0.720 -1225.169 862.736
C(dose)[T.1] 220.4681 530.462 0.416 0.682 -889.801 1330.738
expression 27.0635 57.333 0.472 0.642 -92.936 147.063
expression:C(dose)[T.1] -18.7280 61.419 -0.305 0.764 -147.279 109.823
Omnibus: 0.208 Durbin-Watson: 1.786
Prob(Omnibus): 0.901 Jarque-Bera (JB): 0.411
Skew: 0.001 Prob(JB): 0.814
Kurtosis: 2.345 Cond. No. 1.55e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.654
Model: OLS Adj. R-squared: 0.619
Method: Least Squares F-statistic: 18.90
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.46e-05
Time: 04:54:03 Log-Likelihood: -100.90
No. Observations: 23 AIC: 207.8
Df Residuals: 20 BIC: 211.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -39.2564 174.868 -0.224 0.825 -404.024 325.511
C(dose)[T.1] 58.7721 13.383 4.392 0.000 30.856 86.688
expression 10.7443 20.090 0.535 0.599 -31.163 52.652
Omnibus: 0.142 Durbin-Watson: 1.777
Prob(Omnibus): 0.931 Jarque-Bera (JB): 0.361
Skew: -0.035 Prob(JB): 0.835
Kurtosis: 2.390 Cond. No. 346.

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: 04:54:03 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: 0.320
Model: OLS Adj. R-squared: 0.288
Method: Least Squares F-statistic: 9.899
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00487
Time: 04:54:03 Log-Likelihood: -108.66
No. Observations: 23 AIC: 221.3
Df Residuals: 21 BIC: 223.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 555.4476 151.323 3.671 0.001 240.754 870.141
expression -56.2525 17.879 -3.146 0.005 -93.435 -19.071
Omnibus: 0.366 Durbin-Watson: 2.325
Prob(Omnibus): 0.833 Jarque-Bera (JB): 0.518
Skew: 0.198 Prob(JB): 0.772
Kurtosis: 2.381 Cond. No. 218.

CP101

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

F-statistic p-value df difference
0.207 0.658 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.481
Model: OLS Adj. R-squared: 0.339
Method: Least Squares F-statistic: 3.397
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0573
Time: 04:54:03 Log-Likelihood: -70.382
No. Observations: 15 AIC: 148.8
Df Residuals: 11 BIC: 151.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -465.2220 645.827 -0.720 0.486 -1886.679 956.235
C(dose)[T.1] 592.7675 778.514 0.761 0.462 -1120.730 2306.265
expression 60.6861 73.569 0.825 0.427 -101.237 222.610
expression:C(dose)[T.1] -61.9492 89.101 -0.695 0.501 -258.059 134.161
Omnibus: 1.974 Durbin-Watson: 0.893
Prob(Omnibus): 0.373 Jarque-Bera (JB): 1.392
Skew: -0.713 Prob(JB): 0.499
Kurtosis: 2.560 Cond. No. 1.24e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.458
Model: OLS Adj. R-squared: 0.368
Method: Least Squares F-statistic: 5.072
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0253
Time: 04:54:03 Log-Likelihood: -70.705
No. Observations: 15 AIC: 147.4
Df Residuals: 12 BIC: 149.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -94.5335 356.535 -0.265 0.795 -871.356 682.289
C(dose)[T.1] 51.6177 16.490 3.130 0.009 15.689 87.546
expression 18.4527 40.600 0.454 0.658 -70.007 106.913
Omnibus: 2.801 Durbin-Watson: 0.864
Prob(Omnibus): 0.247 Jarque-Bera (JB): 1.725
Skew: -0.826 Prob(JB): 0.422
Kurtosis: 2.822 Cond. No. 405.

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: 04:54:03 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: 0.016
Model: OLS Adj. R-squared: -0.060
Method: Least Squares F-statistic: 0.2064
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.657
Time: 04:54:03 Log-Likelihood: -75.182
No. Observations: 15 AIC: 154.4
Df Residuals: 13 BIC: 155.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 290.4799 433.337 0.670 0.514 -645.688 1226.647
expression -22.6036 49.754 -0.454 0.657 -130.092 84.884
Omnibus: 0.210 Durbin-Watson: 1.547
Prob(Omnibus): 0.900 Jarque-Bera (JB): 0.402
Skew: -0.016 Prob(JB): 0.818
Kurtosis: 2.199 Cond. No. 379.