LID SPN TRAP

1460563_at

Nus1 nuclear undecaprenyl pyrophosphate synthase 1 homolog (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
0.085 0.774 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.658
Model: OLS Adj. R-squared: 0.604
Method: Least Squares F-statistic: 12.20
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000112
Time: 05:14:50 Log-Likelihood: -100.76
No. Observations: 23 AIC: 209.5
Df Residuals: 19 BIC: 214.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 266.4938 322.830 0.825 0.419 -409.198 942.185
C(dose)[T.1] -259.8729 483.015 -0.538 0.597 -1270.835 751.089
expression -20.9460 31.848 -0.658 0.519 -87.604 45.712
expression:C(dose)[T.1] 30.4855 46.553 0.655 0.520 -66.951 127.922
Omnibus: 0.218 Durbin-Watson: 1.900
Prob(Omnibus): 0.897 Jarque-Bera (JB): 0.418
Skew: 0.034 Prob(JB): 0.811
Kurtosis: 2.343 Cond. No. 1.45e+03

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: 18.62
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.72e-05
Time: 05:14:50 Log-Likelihood: -101.01
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 121.8929 232.114 0.525 0.605 -362.289 606.075
C(dose)[T.1] 56.3071 13.426 4.194 0.000 28.302 84.313
expression -6.6784 22.895 -0.292 0.774 -54.436 41.079
Omnibus: 0.467 Durbin-Watson: 1.870
Prob(Omnibus): 0.792 Jarque-Bera (JB): 0.570
Skew: 0.099 Prob(JB): 0.752
Kurtosis: 2.255 Cond. No. 556.

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: 05:14:50 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: 0.343
Model: OLS Adj. R-squared: 0.312
Method: Least Squares F-statistic: 10.97
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00331
Time: 05:14:50 Log-Likelihood: -108.27
No. Observations: 23 AIC: 220.5
Df Residuals: 21 BIC: 222.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -604.6713 206.684 -2.926 0.008 -1034.493 -174.849
expression 66.1399 19.966 3.313 0.003 24.618 107.662
Omnibus: 3.645 Durbin-Watson: 2.301
Prob(Omnibus): 0.162 Jarque-Bera (JB): 1.513
Skew: -0.201 Prob(JB): 0.469
Kurtosis: 1.810 Cond. No. 369.

CP101

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

F-statistic p-value df difference
2.002 0.182 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.536
Model: OLS Adj. R-squared: 0.410
Method: Least Squares F-statistic: 4.239
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0321
Time: 05:14:50 Log-Likelihood: -69.538
No. Observations: 15 AIC: 147.1
Df Residuals: 11 BIC: 149.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 240.8613 449.179 0.536 0.602 -747.775 1229.497
C(dose)[T.1] 298.5299 542.906 0.550 0.593 -896.398 1493.458
expression -17.9677 46.521 -0.386 0.707 -120.360 84.425
expression:C(dose)[T.1] -25.2977 56.009 -0.452 0.660 -148.572 97.977
Omnibus: 0.250 Durbin-Watson: 0.937
Prob(Omnibus): 0.882 Jarque-Bera (JB): 0.426
Skew: -0.116 Prob(JB): 0.808
Kurtosis: 2.207 Cond. No. 1.03e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.528
Model: OLS Adj. R-squared: 0.449
Method: Least Squares F-statistic: 6.701
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0111
Time: 05:14:50 Log-Likelihood: -69.676
No. Observations: 15 AIC: 145.4
Df Residuals: 12 BIC: 147.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 409.3266 241.852 1.692 0.116 -117.625 936.278
C(dose)[T.1] 53.4115 14.872 3.591 0.004 21.008 85.815
expression -35.4208 25.032 -1.415 0.182 -89.960 19.119
Omnibus: 0.481 Durbin-Watson: 0.836
Prob(Omnibus): 0.786 Jarque-Bera (JB): 0.544
Skew: -0.125 Prob(JB): 0.762
Kurtosis: 2.101 Cond. No. 327.

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: 05:14:50 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: 0.020
Model: OLS Adj. R-squared: -0.056
Method: Least Squares F-statistic: 0.2633
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.616
Time: 05:14:50 Log-Likelihood: -75.150
No. Observations: 15 AIC: 154.3
Df Residuals: 13 BIC: 155.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 262.8705 329.911 0.797 0.440 -449.858 975.599
expression -17.4151 33.940 -0.513 0.616 -90.738 55.908
Omnibus: 0.465 Durbin-Watson: 1.677
Prob(Omnibus): 0.793 Jarque-Bera (JB): 0.538
Skew: 0.131 Prob(JB): 0.764
Kurtosis: 2.110 Cond. No. 322.