LID SPN TRAP

1460510_a_at

Coq10b coenzyme Q10 homolog B (S. cerevisiae)

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
0.056 0.815 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.655
Model: OLS Adj. R-squared: 0.601
Method: Least Squares F-statistic: 12.03
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.000122
Time: 23:04:42 Log-Likelihood: -100.86
No. Observations: 23 AIC: 209.7
Df Residuals: 19 BIC: 214.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 81.5922 255.232 0.320 0.753 -452.614 615.798
C(dose)[T.1] -194.7644 466.837 -0.417 0.681 -1171.865 782.336
expression -2.8959 26.984 -0.107 0.916 -59.373 53.581
expression:C(dose)[T.1] 24.9626 47.486 0.526 0.605 -74.427 124.352
Omnibus: 1.154 Durbin-Watson: 1.983
Prob(Omnibus): 0.561 Jarque-Bera (JB): 0.839
Skew: 0.075 Prob(JB): 0.657
Kurtosis: 2.076 Cond. No. 1.24e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.650
Model: OLS Adj. R-squared: 0.615
Method: Least Squares F-statistic: 18.57
Date: Thu, 03 Apr 2025 Prob (F-statistic): 2.76e-05
Time: 23:04:43 Log-Likelihood: -101.03
No. Observations: 23 AIC: 208.1
Df Residuals: 20 BIC: 211.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 5.3729 206.214 0.026 0.979 -424.783 435.529
C(dose)[T.1] 50.5157 14.782 3.417 0.003 19.681 81.350
expression 5.1645 21.798 0.237 0.815 -40.306 50.635
Omnibus: 0.510 Durbin-Watson: 1.924
Prob(Omnibus): 0.775 Jarque-Bera (JB): 0.597
Skew: 0.122 Prob(JB): 0.742
Kurtosis: 2.249 Cond. No. 465.

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, 03 Apr 2025 Prob (F-statistic): 3.51e-06
Time: 23:04:43 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: 0.446
Model: OLS Adj. R-squared: 0.419
Method: Least Squares F-statistic: 16.88
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.000501
Time: 23:04:43 Log-Likelihood: -106.32
No. Observations: 23 AIC: 216.6
Df Residuals: 21 BIC: 218.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -553.6255 154.225 -3.590 0.002 -874.354 -232.897
expression 65.1770 15.862 4.109 0.001 32.191 98.163
Omnibus: 4.289 Durbin-Watson: 2.347
Prob(Omnibus): 0.117 Jarque-Bera (JB): 1.512
Skew: 0.058 Prob(JB): 0.470
Kurtosis: 1.749 Cond. No. 282.

CP101

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

F-statistic p-value df difference
0.548 0.474 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.474
Model: OLS Adj. R-squared: 0.331
Method: Least Squares F-statistic: 3.305
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0612
Time: 23:04:43 Log-Likelihood: -70.481
No. Observations: 15 AIC: 149.0
Df Residuals: 11 BIC: 151.8
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 142.2385 688.257 0.207 0.840 -1372.605 1657.082
C(dose)[T.1] 178.1207 744.387 0.239 0.815 -1460.264 1816.505
expression -9.0543 83.288 -0.109 0.915 -192.370 174.262
expression:C(dose)[T.1] -14.3346 89.416 -0.160 0.876 -211.137 182.468
Omnibus: 3.623 Durbin-Watson: 0.816
Prob(Omnibus): 0.163 Jarque-Bera (JB): 2.032
Skew: -0.900 Prob(JB): 0.362
Kurtosis: 3.086 Cond. No. 1.26e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.473
Model: OLS Adj. R-squared: 0.385
Method: Least Squares F-statistic: 5.381
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0215
Time: 23:04:43 Log-Likelihood: -70.498
No. Observations: 15 AIC: 147.0
Df Residuals: 12 BIC: 149.1
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 244.9999 240.242 1.020 0.328 -278.442 768.442
C(dose)[T.1] 58.8327 20.162 2.918 0.013 14.903 102.763
expression -21.4916 29.045 -0.740 0.474 -84.775 41.792
Omnibus: 3.475 Durbin-Watson: 0.777
Prob(Omnibus): 0.176 Jarque-Bera (JB): 1.998
Skew: -0.894 Prob(JB): 0.368
Kurtosis: 3.019 Cond. No. 271.

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, 03 Apr 2025 Prob (F-statistic): 0.00629
Time: 23:04:43 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: 0.099
Model: OLS Adj. R-squared: 0.029
Method: Least Squares F-statistic: 1.425
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.254
Time: 23:04:43 Log-Likelihood: -74.520
No. Observations: 15 AIC: 153.0
Df Residuals: 13 BIC: 154.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -189.0007 236.999 -0.797 0.439 -701.006 323.004
expression 33.2492 27.854 1.194 0.254 -26.926 93.425
Omnibus: 0.950 Durbin-Watson: 1.449
Prob(Omnibus): 0.622 Jarque-Bera (JB): 0.710
Skew: 0.117 Prob(JB): 0.701
Kurtosis: 1.960 Cond. No. 212.