LID SPN TRAP

1423630_at

Cygb cytoglobin

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
0.065 0.801 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.650
Model: OLS Adj. R-squared: 0.595
Method: Least Squares F-statistic: 11.77
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000138
Time: 05:02:30 Log-Likelihood: -101.02
No. Observations: 23 AIC: 210.0
Df Residuals: 19 BIC: 214.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 10.6743 223.415 0.048 0.962 -456.939 478.288
C(dose)[T.1] 47.5211 385.385 0.123 0.903 -759.099 854.141
expression 4.5018 23.094 0.195 0.848 -43.835 52.838
expression:C(dose)[T.1] 0.9387 41.608 0.023 0.982 -86.148 88.025
Omnibus: 0.231 Durbin-Watson: 1.847
Prob(Omnibus): 0.891 Jarque-Bera (JB): 0.427
Skew: 0.061 Prob(JB): 0.808
Kurtosis: 2.344 Cond. No. 974.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.650
Model: OLS Adj. R-squared: 0.615
Method: Least Squares F-statistic: 18.59
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.74e-05
Time: 05:02:30 Log-Likelihood: -101.03
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 7.8779 181.170 0.043 0.966 -370.036 385.792
C(dose)[T.1] 56.2090 14.235 3.949 0.001 26.515 85.902
expression 4.7910 18.724 0.256 0.801 -34.267 43.849
Omnibus: 0.240 Durbin-Watson: 1.845
Prob(Omnibus): 0.887 Jarque-Bera (JB): 0.433
Skew: 0.061 Prob(JB): 0.805
Kurtosis: 2.339 Cond. No. 395.

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: 05:02:30 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: 0.377
Model: OLS Adj. R-squared: 0.348
Method: Least Squares F-statistic: 12.73
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00181
Time: 05:02:30 Log-Likelihood: -107.65
No. Observations: 23 AIC: 219.3
Df Residuals: 21 BIC: 221.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 581.7889 140.807 4.132 0.000 288.965 874.613
expression -53.5046 14.993 -3.569 0.002 -84.685 -22.325
Omnibus: 2.170 Durbin-Watson: 2.383
Prob(Omnibus): 0.338 Jarque-Bera (JB): 1.294
Skew: 0.277 Prob(JB): 0.524
Kurtosis: 1.979 Cond. No. 235.

CP101

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

F-statistic p-value df difference
0.173 0.685 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.495
Model: OLS Adj. R-squared: 0.358
Method: Least Squares F-statistic: 3.599
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0496
Time: 05:02:30 Log-Likelihood: -70.171
No. Observations: 15 AIC: 148.3
Df Residuals: 11 BIC: 151.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -445.7584 509.477 -0.875 0.400 -1567.111 675.594
C(dose)[T.1] 562.4899 558.884 1.006 0.336 -667.605 1792.585
expression 52.9749 52.579 1.008 0.335 -62.750 168.700
expression:C(dose)[T.1] -52.9858 57.648 -0.919 0.378 -179.868 73.896
Omnibus: 2.545 Durbin-Watson: 1.110
Prob(Omnibus): 0.280 Jarque-Bera (JB): 1.650
Skew: -0.800 Prob(JB): 0.438
Kurtosis: 2.719 Cond. No. 1.07e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.457
Model: OLS Adj. R-squared: 0.366
Method: Least Squares F-statistic: 5.041
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0258
Time: 05:02:30 Log-Likelihood: -70.726
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 -18.7673 207.813 -0.090 0.930 -471.552 434.017
C(dose)[T.1] 49.0058 15.634 3.134 0.009 14.941 83.070
expression 8.8978 21.420 0.415 0.685 -37.771 55.567
Omnibus: 2.774 Durbin-Watson: 0.794
Prob(Omnibus): 0.250 Jarque-Bera (JB): 2.022
Skew: -0.862 Prob(JB): 0.364
Kurtosis: 2.485 Cond. No. 262.

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: 05:02:30 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: 0.012
Model: OLS Adj. R-squared: -0.064
Method: Least Squares F-statistic: 0.1535
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.702
Time: 05:02:30 Log-Likelihood: -75.212
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 -11.7448 269.248 -0.044 0.966 -593.419 569.929
expression 10.8685 27.741 0.392 0.702 -49.063 70.800
Omnibus: 1.053 Durbin-Watson: 1.599
Prob(Omnibus): 0.591 Jarque-Bera (JB): 0.736
Skew: 0.103 Prob(JB): 0.692
Kurtosis: 1.935 Cond. No. 261.