LID SPN TRAP

1456785_at

Med14 mediator complex subunit 14

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
0.257 0.618 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.694
Model: OLS Adj. R-squared: 0.646
Method: Least Squares F-statistic: 14.37
Date: Thu, 21 Nov 2024 Prob (F-statistic): 3.98e-05
Time: 03:41:06 Log-Likelihood: -99.481
No. Observations: 23 AIC: 207.0
Df Residuals: 19 BIC: 211.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 191.2617 233.893 0.818 0.424 -298.282 680.805
C(dose)[T.1] -545.7510 376.325 -1.450 0.163 -1333.408 241.906
expression -14.4571 24.665 -0.586 0.565 -66.081 37.167
expression:C(dose)[T.1] 62.4967 39.339 1.589 0.129 -19.840 144.834
Omnibus: 0.505 Durbin-Watson: 1.860
Prob(Omnibus): 0.777 Jarque-Bera (JB): 0.580
Skew: -0.042 Prob(JB): 0.748
Kurtosis: 2.226 Cond. No. 1.07e+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: 18.86
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.49e-05
Time: 03:41:06 Log-Likelihood: -100.92
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 -41.6400 189.063 -0.220 0.828 -436.019 352.739
C(dose)[T.1] 51.9440 9.137 5.685 0.000 32.885 71.003
expression 10.1106 19.933 0.507 0.618 -31.469 51.691
Omnibus: 0.145 Durbin-Watson: 1.902
Prob(Omnibus): 0.930 Jarque-Bera (JB): 0.302
Skew: 0.154 Prob(JB): 0.860
Kurtosis: 2.531 Cond. No. 420.

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: 03:41:06 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: 0.094
Model: OLS Adj. R-squared: 0.050
Method: Least Squares F-statistic: 2.167
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.156
Time: 03:41:06 Log-Likelihood: -111.98
No. Observations: 23 AIC: 228.0
Df Residuals: 21 BIC: 230.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -341.9951 286.541 -1.194 0.246 -937.891 253.900
expression 44.1773 30.009 1.472 0.156 -18.229 106.584
Omnibus: 5.109 Durbin-Watson: 2.225
Prob(Omnibus): 0.078 Jarque-Bera (JB): 1.694
Skew: 0.154 Prob(JB): 0.429
Kurtosis: 1.707 Cond. No. 403.

CP101

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

F-statistic p-value df difference
0.964 0.345 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.504
Model: OLS Adj. R-squared: 0.369
Method: Least Squares F-statistic: 3.728
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0453
Time: 03:41:06 Log-Likelihood: -70.039
No. Observations: 15 AIC: 148.1
Df Residuals: 11 BIC: 150.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 157.8761 896.791 0.176 0.863 -1815.948 2131.700
C(dose)[T.1] -526.3971 998.775 -0.527 0.609 -2724.686 1671.892
expression -10.9101 108.165 -0.101 0.921 -248.980 227.160
expression:C(dose)[T.1] 67.6198 119.748 0.565 0.584 -195.944 331.183
Omnibus: 2.928 Durbin-Watson: 0.706
Prob(Omnibus): 0.231 Jarque-Bera (JB): 1.912
Skew: -0.685 Prob(JB): 0.384
Kurtosis: 1.913 Cond. No. 1.68e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.490
Model: OLS Adj. R-squared: 0.405
Method: Least Squares F-statistic: 5.759
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0176
Time: 03:41:06 Log-Likelihood: -70.253
No. Observations: 15 AIC: 146.5
Df Residuals: 12 BIC: 148.6
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -299.5084 373.837 -0.801 0.439 -1114.029 515.012
C(dose)[T.1] 37.4845 19.276 1.945 0.076 -4.514 79.483
expression 44.2611 45.074 0.982 0.345 -53.946 142.468
Omnibus: 2.730 Durbin-Watson: 0.783
Prob(Omnibus): 0.255 Jarque-Bera (JB): 1.763
Skew: -0.637 Prob(JB): 0.414
Kurtosis: 1.906 Cond. No. 424.

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: 03:41:06 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: 0.329
Model: OLS Adj. R-squared: 0.277
Method: Least Squares F-statistic: 6.374
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0254
Time: 03:41:06 Log-Likelihood: -72.308
No. Observations: 15 AIC: 148.6
Df Residuals: 13 BIC: 150.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -736.7860 329.049 -2.239 0.043 -1447.652 -25.920
expression 98.4951 39.014 2.525 0.025 14.210 182.780
Omnibus: 0.838 Durbin-Watson: 1.246
Prob(Omnibus): 0.658 Jarque-Bera (JB): 0.693
Skew: -0.174 Prob(JB): 0.707
Kurtosis: 2.006 Cond. No. 338.