LID SPN TRAP

1458377_at

Med26 mediator complex subunit 26

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
0.786 0.386 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.713
Model: OLS Adj. R-squared: 0.668
Method: Least Squares F-statistic: 15.75
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.18e-05
Time: 04:02:56 Log-Likelihood: -98.740
No. Observations: 23 AIC: 205.5
Df Residuals: 19 BIC: 210.0
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 13.2245 66.235 0.200 0.844 -125.407 151.856
C(dose)[T.1] 223.4996 93.470 2.391 0.027 27.864 419.136
expression 6.1448 9.895 0.621 0.542 -14.565 26.855
expression:C(dose)[T.1] -25.8998 14.101 -1.837 0.082 -55.413 3.613
Omnibus: 1.692 Durbin-Watson: 1.769
Prob(Omnibus): 0.429 Jarque-Bera (JB): 0.605
Skew: -0.341 Prob(JB): 0.739
Kurtosis: 3.409 Cond. No. 202.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.662
Model: OLS Adj. R-squared: 0.629
Method: Least Squares F-statistic: 19.61
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.93e-05
Time: 04:02:56 Log-Likelihood: -100.62
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 98.2897 50.084 1.962 0.064 -6.185 202.764
C(dose)[T.1] 52.4737 8.657 6.061 0.000 34.414 70.533
expression -6.6092 7.456 -0.886 0.386 -22.162 8.944
Omnibus: 0.008 Durbin-Watson: 1.908
Prob(Omnibus): 0.996 Jarque-Bera (JB): 0.138
Skew: -0.034 Prob(JB): 0.933
Kurtosis: 2.627 Cond. No. 79.3

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: 04:02:56 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: 0.042
Model: OLS Adj. R-squared: -0.004
Method: Least Squares F-statistic: 0.9221
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.348
Time: 04:02:56 Log-Likelihood: -112.61
No. Observations: 23 AIC: 229.2
Df Residuals: 21 BIC: 231.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 156.9822 80.770 1.944 0.065 -10.989 324.954
expression -11.6940 12.178 -0.960 0.348 -37.019 13.631
Omnibus: 3.538 Durbin-Watson: 2.496
Prob(Omnibus): 0.171 Jarque-Bera (JB): 1.380
Skew: 0.024 Prob(JB): 0.502
Kurtosis: 1.801 Cond. No. 77.6

CP101

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

F-statistic p-value df difference
3.629 0.081 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.577
Model: OLS Adj. R-squared: 0.462
Method: Least Squares F-statistic: 5.007
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0198
Time: 04:02:56 Log-Likelihood: -68.843
No. Observations: 15 AIC: 145.7
Df Residuals: 11 BIC: 148.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 194.9433 173.242 1.125 0.284 -186.360 576.247
C(dose)[T.1] 68.0067 194.314 0.350 0.733 -359.676 495.689
expression -20.4274 27.702 -0.737 0.476 -81.398 40.543
expression:C(dose)[T.1] -3.5613 31.192 -0.114 0.911 -72.214 65.092
Omnibus: 1.437 Durbin-Watson: 0.584
Prob(Omnibus): 0.488 Jarque-Bera (JB): 0.963
Skew: -0.313 Prob(JB): 0.618
Kurtosis: 1.928 Cond. No. 259.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.577
Model: OLS Adj. R-squared: 0.506
Method: Least Squares F-statistic: 8.176
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00575
Time: 04:02:56 Log-Likelihood: -68.852
No. Observations: 15 AIC: 143.7
Df Residuals: 12 BIC: 145.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 212.4772 76.809 2.766 0.017 45.125 379.829
C(dose)[T.1] 45.8832 13.901 3.301 0.006 15.595 76.171
expression -23.2362 12.198 -1.905 0.081 -49.814 3.341
Omnibus: 1.604 Durbin-Watson: 0.611
Prob(Omnibus): 0.448 Jarque-Bera (JB): 1.007
Skew: -0.312 Prob(JB): 0.604
Kurtosis: 1.895 Cond. No. 71.2

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: 04:02:56 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: 0.193
Model: OLS Adj. R-squared: 0.130
Method: Least Squares F-statistic: 3.099
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.102
Time: 04:02:56 Log-Likelihood: -73.696
No. Observations: 15 AIC: 151.4
Df Residuals: 13 BIC: 152.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 268.0122 99.455 2.695 0.018 53.153 482.871
expression -28.2739 16.061 -1.760 0.102 -62.971 6.423
Omnibus: 0.436 Durbin-Watson: 1.681
Prob(Omnibus): 0.804 Jarque-Bera (JB): 0.539
Skew: 0.263 Prob(JB): 0.764
Kurtosis: 2.235 Cond. No. 69.2