LID SPN TRAP

1459584_at

Vps24 vacuolar protein sorting 24 (yeast)

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
0.306 0.586 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.660
Model: OLS Adj. R-squared: 0.607
Method: Least Squares F-statistic: 12.31
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000105
Time: 05:10:29 Log-Likelihood: -100.69
No. Observations: 23 AIC: 209.4
Df Residuals: 19 BIC: 213.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 183.9724 164.478 1.119 0.277 -160.283 528.228
C(dose)[T.1] -66.3308 202.738 -0.327 0.747 -490.665 358.004
expression -21.7198 27.511 -0.789 0.440 -79.301 35.862
expression:C(dose)[T.1] 19.9458 34.488 0.578 0.570 -52.238 92.129
Omnibus: 0.388 Durbin-Watson: 2.026
Prob(Omnibus): 0.824 Jarque-Bera (JB): 0.521
Skew: -0.049 Prob(JB): 0.771
Kurtosis: 2.269 Cond. No. 380.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.654
Model: OLS Adj. R-squared: 0.620
Method: Least Squares F-statistic: 18.93
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.43e-05
Time: 05:10:29 Log-Likelihood: -100.89
No. Observations: 23 AIC: 207.8
Df Residuals: 20 BIC: 211.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 108.1430 97.641 1.108 0.281 -95.533 311.818
C(dose)[T.1] 50.7781 9.855 5.152 0.000 30.220 71.336
expression -9.0275 16.312 -0.553 0.586 -43.054 24.999
Omnibus: 0.214 Durbin-Watson: 1.905
Prob(Omnibus): 0.898 Jarque-Bera (JB): 0.416
Skew: 0.004 Prob(JB): 0.812
Kurtosis: 2.341 Cond. No. 136.

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: 05:10:29 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: 0.196
Model: OLS Adj. R-squared: 0.157
Method: Least Squares F-statistic: 5.105
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0346
Time: 05:10:30 Log-Likelihood: -110.60
No. Observations: 23 AIC: 225.2
Df Residuals: 21 BIC: 227.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 362.6607 125.389 2.892 0.009 101.900 623.422
expression -48.4585 21.446 -2.260 0.035 -93.058 -3.859
Omnibus: 2.136 Durbin-Watson: 1.996
Prob(Omnibus): 0.344 Jarque-Bera (JB): 1.378
Skew: 0.341 Prob(JB): 0.502
Kurtosis: 2.013 Cond. No. 117.

CP101

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

F-statistic p-value df difference
1.916 0.192 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.601
Model: OLS Adj. R-squared: 0.492
Method: Least Squares F-statistic: 5.520
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0147
Time: 05:10:30 Log-Likelihood: -68.411
No. Observations: 15 AIC: 144.8
Df Residuals: 11 BIC: 147.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -37.1090 240.401 -0.154 0.880 -566.228 492.010
C(dose)[T.1] 467.5507 287.205 1.628 0.132 -164.583 1099.684
expression 18.4464 42.382 0.435 0.672 -74.836 111.729
expression:C(dose)[T.1] -73.0731 50.416 -1.449 0.175 -184.037 37.891
Omnibus: 1.309 Durbin-Watson: 1.164
Prob(Omnibus): 0.520 Jarque-Bera (JB): 1.011
Skew: -0.577 Prob(JB): 0.603
Kurtosis: 2.464 Cond. No. 354.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.525
Model: OLS Adj. R-squared: 0.445
Method: Least Squares F-statistic: 6.623
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0115
Time: 05:10:30 Log-Likelihood: -69.722
No. Observations: 15 AIC: 145.4
Df Residuals: 12 BIC: 147.6
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 255.5449 136.330 1.874 0.085 -41.494 552.583
C(dose)[T.1] 51.7737 14.734 3.514 0.004 19.671 83.877
expression -33.1944 23.983 -1.384 0.192 -85.448 19.059
Omnibus: 2.926 Durbin-Watson: 0.720
Prob(Omnibus): 0.231 Jarque-Bera (JB): 1.317
Skew: -0.338 Prob(JB): 0.518
Kurtosis: 1.715 Cond. No. 111.

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: 05:10:30 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: 0.036
Model: OLS Adj. R-squared: -0.039
Method: Least Squares F-statistic: 0.4795
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.501
Time: 05:10:30 Log-Likelihood: -75.028
No. Observations: 15 AIC: 154.1
Df Residuals: 13 BIC: 155.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 222.3631 186.123 1.195 0.254 -179.731 624.457
expression -22.5447 32.558 -0.692 0.501 -92.881 47.792
Omnibus: 0.323 Durbin-Watson: 1.553
Prob(Omnibus): 0.851 Jarque-Bera (JB): 0.462
Skew: -0.005 Prob(JB): 0.794
Kurtosis: 2.140 Cond. No. 110.