LID SPN TRAP

1457726_at

Rps15a ribosomal protein S15A

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
0.124 0.728 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.656
Model: OLS Adj. R-squared: 0.601
Method: Least Squares F-statistic: 12.06
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000120
Time: 05:23:36 Log-Likelihood: -100.84
No. Observations: 23 AIC: 209.7
Df Residuals: 19 BIC: 214.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 57.8686 100.266 0.577 0.571 -151.991 267.728
C(dose)[T.1] -18.5328 150.965 -0.123 0.904 -334.506 297.440
expression -0.5035 13.767 -0.037 0.971 -29.317 28.310
expression:C(dose)[T.1] 11.0807 22.244 0.498 0.624 -35.477 57.638
Omnibus: 0.305 Durbin-Watson: 1.796
Prob(Omnibus): 0.859 Jarque-Bera (JB): 0.476
Skew: 0.164 Prob(JB): 0.788
Kurtosis: 2.376 Cond. No. 294.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.651
Model: OLS Adj. R-squared: 0.616
Method: Least Squares F-statistic: 18.67
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.66e-05
Time: 05:23:36 Log-Likelihood: -100.99
No. Observations: 23 AIC: 208.0
Df Residuals: 20 BIC: 211.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 27.0159 77.353 0.349 0.731 -134.340 188.372
C(dose)[T.1] 56.4074 12.339 4.572 0.000 30.669 82.146
expression 3.7406 10.608 0.353 0.728 -18.388 25.869
Omnibus: 0.240 Durbin-Watson: 1.820
Prob(Omnibus): 0.887 Jarque-Bera (JB): 0.433
Skew: 0.105 Prob(JB): 0.805
Kurtosis: 2.362 Cond. No. 126.

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: 05:23:36 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: 0.287
Model: OLS Adj. R-squared: 0.253
Method: Least Squares F-statistic: 8.444
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00845
Time: 05:23:36 Log-Likelihood: -109.22
No. Observations: 23 AIC: 222.4
Df Residuals: 21 BIC: 224.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 289.3330 72.393 3.997 0.001 138.783 439.883
expression -30.4808 10.490 -2.906 0.008 -52.295 -8.667
Omnibus: 2.723 Durbin-Watson: 2.288
Prob(Omnibus): 0.256 Jarque-Bera (JB): 1.225
Skew: -0.005 Prob(JB): 0.542
Kurtosis: 1.870 Cond. No. 83.7

CP101

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

F-statistic p-value df difference
2.792 0.121 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.731
Model: OLS Adj. R-squared: 0.658
Method: Least Squares F-statistic: 9.972
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00181
Time: 05:23:36 Log-Likelihood: -65.448
No. Observations: 15 AIC: 138.9
Df Residuals: 11 BIC: 141.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 193.0203 125.164 1.542 0.151 -82.465 468.505
C(dose)[T.1] -364.3369 153.598 -2.372 0.037 -702.405 -26.269
expression -17.3233 17.226 -1.006 0.336 -55.237 20.590
expression:C(dose)[T.1] 57.0919 21.136 2.701 0.021 10.571 103.613
Omnibus: 1.626 Durbin-Watson: 1.288
Prob(Omnibus): 0.444 Jarque-Bera (JB): 0.364
Skew: -0.328 Prob(JB): 0.834
Kurtosis: 3.390 Cond. No. 284.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.553
Model: OLS Adj. R-squared: 0.478
Method: Least Squares F-statistic: 7.418
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00800
Time: 05:23:36 Log-Likelihood: -69.264
No. Observations: 15 AIC: 144.5
Df Residuals: 12 BIC: 146.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -81.8913 89.958 -0.910 0.381 -277.892 114.110
C(dose)[T.1] 49.3907 14.177 3.484 0.005 18.502 80.280
expression 20.5962 12.326 1.671 0.121 -6.259 47.452
Omnibus: 0.118 Durbin-Watson: 0.811
Prob(Omnibus): 0.943 Jarque-Bera (JB): 0.317
Skew: -0.135 Prob(JB): 0.854
Kurtosis: 2.342 Cond. No. 94.4

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: 05:23:36 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: 0.101
Model: OLS Adj. R-squared: 0.031
Method: Least Squares F-statistic: 1.453
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.250
Time: 05:23:36 Log-Likelihood: -74.505
No. Observations: 15 AIC: 153.0
Df Residuals: 13 BIC: 154.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -52.9985 122.056 -0.434 0.671 -316.684 210.687
expression 20.2441 16.795 1.205 0.250 -16.039 56.527
Omnibus: 0.442 Durbin-Watson: 1.586
Prob(Omnibus): 0.802 Jarque-Bera (JB): 0.543
Skew: -0.258 Prob(JB): 0.762
Kurtosis: 2.224 Cond. No. 93.8