LID SPN TRAP

1440179_x_at

Rnf217 ring finger protein 217

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
0.156 0.697 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.652
Model: OLS Adj. R-squared: 0.597
Method: Least Squares F-statistic: 11.87
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000132
Time: 04:01:29 Log-Likelihood: -100.96
No. Observations: 23 AIC: 209.9
Df Residuals: 19 BIC: 214.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 130.6727 311.734 0.419 0.680 -521.794 783.139
C(dose)[T.1] 136.7405 580.940 0.235 0.816 -1079.182 1352.663
expression -6.6596 27.145 -0.245 0.809 -63.475 50.155
expression:C(dose)[T.1] -6.7186 49.188 -0.137 0.893 -109.670 96.233
Omnibus: 0.316 Durbin-Watson: 1.866
Prob(Omnibus): 0.854 Jarque-Bera (JB): 0.480
Skew: -0.027 Prob(JB): 0.787
Kurtosis: 2.294 Cond. No. 1.84e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.652
Model: OLS Adj. R-squared: 0.617
Method: Least Squares F-statistic: 18.72
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.62e-05
Time: 04:01:29 Log-Likelihood: -100.97
No. Observations: 23 AIC: 207.9
Df Residuals: 20 BIC: 211.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 154.1664 253.530 0.608 0.550 -374.687 683.020
C(dose)[T.1] 57.4117 13.530 4.243 0.000 29.189 85.635
expression -8.7058 22.075 -0.394 0.697 -54.753 37.341
Omnibus: 0.447 Durbin-Watson: 1.866
Prob(Omnibus): 0.800 Jarque-Bera (JB): 0.551
Skew: -0.029 Prob(JB): 0.759
Kurtosis: 2.244 Cond. No. 687.

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: 04:01:29 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: 0.338
Model: OLS Adj. R-squared: 0.307
Method: Least Squares F-statistic: 10.73
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00360
Time: 04:01:29 Log-Likelihood: -108.36
No. Observations: 23 AIC: 220.7
Df Residuals: 21 BIC: 223.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -655.6505 224.527 -2.920 0.008 -1122.579 -188.722
expression 62.8217 19.175 3.276 0.004 22.946 102.697
Omnibus: 2.138 Durbin-Watson: 2.313
Prob(Omnibus): 0.343 Jarque-Bera (JB): 1.473
Skew: 0.397 Prob(JB): 0.479
Kurtosis: 2.048 Cond. No. 451.

CP101

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

F-statistic p-value df difference
0.483 0.500 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.496
Model: OLS Adj. R-squared: 0.358
Method: Least Squares F-statistic: 3.604
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0494
Time: 04:01:29 Log-Likelihood: -70.166
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 282.6358 222.391 1.271 0.230 -206.845 772.116
C(dose)[T.1] -276.2497 441.054 -0.626 0.544 -1247.003 694.503
expression -21.3671 22.051 -0.969 0.353 -69.901 27.167
expression:C(dose)[T.1] 32.0205 42.895 0.746 0.471 -62.391 126.432
Omnibus: 2.013 Durbin-Watson: 1.157
Prob(Omnibus): 0.366 Jarque-Bera (JB): 1.348
Skew: -0.712 Prob(JB): 0.510
Kurtosis: 2.642 Cond. No. 704.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.470
Model: OLS Adj. R-squared: 0.382
Method: Least Squares F-statistic: 5.323
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0221
Time: 04:01:29 Log-Likelihood: -70.537
No. Observations: 15 AIC: 147.1
Df Residuals: 12 BIC: 149.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 197.4092 187.294 1.054 0.313 -210.669 605.488
C(dose)[T.1] 52.7564 16.259 3.245 0.007 17.331 88.182
expression -12.9053 18.562 -0.695 0.500 -53.348 27.538
Omnibus: 3.194 Durbin-Watson: 0.867
Prob(Omnibus): 0.203 Jarque-Bera (JB): 2.120
Skew: -0.910 Prob(JB): 0.347
Kurtosis: 2.725 Cond. No. 252.

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: 04:01:29 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: 0.005
Model: OLS Adj. R-squared: -0.071
Method: Least Squares F-statistic: 0.06832
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.798
Time: 04:01:29 Log-Likelihood: -75.261
No. Observations: 15 AIC: 154.5
Df Residuals: 13 BIC: 155.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 31.7191 237.215 0.134 0.896 -480.752 544.190
expression 6.0620 23.192 0.261 0.798 -44.041 56.165
Omnibus: 0.277 Durbin-Watson: 1.544
Prob(Omnibus): 0.870 Jarque-Bera (JB): 0.439
Skew: -0.021 Prob(JB): 0.803
Kurtosis: 2.163 Cond. No. 242.