LID SPN TRAP

1458636_at

Rnf219 ring finger protein 219

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
3.257 0.086 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.698
Model: OLS Adj. R-squared: 0.651
Method: Least Squares F-statistic: 14.66
Date: Thu, 21 Nov 2024 Prob (F-statistic): 3.51e-05
Time: 04:59:50 Log-Likelihood: -99.325
No. Observations: 23 AIC: 206.6
Df Residuals: 19 BIC: 211.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -60.0458 89.105 -0.674 0.509 -246.545 126.454
C(dose)[T.1] 53.6326 130.155 0.412 0.685 -218.784 326.050
expression 16.9008 13.153 1.285 0.214 -10.629 44.430
expression:C(dose)[T.1] 1.4455 20.133 0.072 0.944 -40.693 43.584
Omnibus: 0.423 Durbin-Watson: 1.681
Prob(Omnibus): 0.809 Jarque-Bera (JB): 0.503
Skew: -0.272 Prob(JB): 0.778
Kurtosis: 2.521 Cond. No. 261.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.698
Model: OLS Adj. R-squared: 0.668
Method: Least Squares F-statistic: 23.13
Date: Thu, 21 Nov 2024 Prob (F-statistic): 6.27e-06
Time: 04:59:50 Log-Likelihood: -99.328
No. Observations: 23 AIC: 204.7
Df Residuals: 20 BIC: 208.1
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -64.2165 65.864 -0.975 0.341 -201.607 73.174
C(dose)[T.1] 62.9497 9.722 6.475 0.000 42.670 83.229
expression 17.5177 9.707 1.805 0.086 -2.731 37.767
Omnibus: 0.414 Durbin-Watson: 1.688
Prob(Omnibus): 0.813 Jarque-Bera (JB): 0.504
Skew: -0.266 Prob(JB): 0.777
Kurtosis: 2.508 Cond. No. 109.

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: 04:59:50 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: 0.066
Model: OLS Adj. R-squared: 0.021
Method: Least Squares F-statistic: 1.472
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.238
Time: 04:59:50 Log-Likelihood: -112.33
No. Observations: 23 AIC: 228.7
Df Residuals: 21 BIC: 230.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 189.6690 90.879 2.087 0.049 0.677 378.662
expression -16.9212 13.945 -1.213 0.238 -45.921 12.078
Omnibus: 3.820 Durbin-Watson: 2.567
Prob(Omnibus): 0.148 Jarque-Bera (JB): 1.661
Skew: 0.285 Prob(JB): 0.436
Kurtosis: 1.813 Cond. No. 86.9

CP101

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

F-statistic p-value df difference
1.273 0.281 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.647
Model: OLS Adj. R-squared: 0.551
Method: Least Squares F-statistic: 6.729
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00766
Time: 04:59:50 Log-Likelihood: -67.484
No. Observations: 15 AIC: 143.0
Df Residuals: 11 BIC: 145.8
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -38.3621 141.589 -0.271 0.791 -349.998 273.274
C(dose)[T.1] 443.2938 186.792 2.373 0.037 32.167 854.421
expression 16.4910 22.021 0.749 0.470 -31.976 64.958
expression:C(dose)[T.1] -62.5906 29.366 -2.131 0.056 -127.225 2.043
Omnibus: 1.322 Durbin-Watson: 1.198
Prob(Omnibus): 0.516 Jarque-Bera (JB): 0.897
Skew: -0.568 Prob(JB): 0.639
Kurtosis: 2.621 Cond. No. 253.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.502
Model: OLS Adj. R-squared: 0.419
Method: Least Squares F-statistic: 6.039
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0153
Time: 04:59:50 Log-Likelihood: -70.077
No. Observations: 15 AIC: 146.2
Df Residuals: 12 BIC: 148.3
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 187.4140 106.924 1.753 0.105 -45.553 420.381
C(dose)[T.1] 46.1835 15.203 3.038 0.010 13.060 79.307
expression -18.7037 16.580 -1.128 0.281 -54.829 17.422
Omnibus: 2.031 Durbin-Watson: 0.493
Prob(Omnibus): 0.362 Jarque-Bera (JB): 1.451
Skew: -0.726 Prob(JB): 0.484
Kurtosis: 2.536 Cond. No. 93.4

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: 04:59:50 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: 0.118
Model: OLS Adj. R-squared: 0.051
Method: Least Squares F-statistic: 1.745
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.209
Time: 04:59:50 Log-Likelihood: -74.355
No. Observations: 15 AIC: 152.7
Df Residuals: 13 BIC: 154.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 268.0520 132.358 2.025 0.064 -17.891 553.995
expression -27.5527 20.858 -1.321 0.209 -72.614 17.508
Omnibus: 0.606 Durbin-Watson: 1.555
Prob(Omnibus): 0.739 Jarque-Bera (JB): 0.581
Skew: -0.385 Prob(JB): 0.748
Kurtosis: 2.420 Cond. No. 90.2