LID SPN TRAP

1449578_at

Supt16h suppressor of Ty 16 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
2.293 0.146 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.719
Model: OLS Adj. R-squared: 0.675
Method: Least Squares F-statistic: 16.23
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.78e-05
Time: 04:31:22 Log-Likelihood: -98.492
No. Observations: 23 AIC: 205.0
Df Residuals: 19 BIC: 209.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 135.3407 175.009 0.773 0.449 -230.958 501.640
C(dose)[T.1] 538.4784 318.144 1.693 0.107 -127.406 1204.362
expression -8.8782 19.141 -0.464 0.648 -48.942 31.185
expression:C(dose)[T.1] -52.7975 34.689 -1.522 0.144 -125.403 19.808
Omnibus: 0.181 Durbin-Watson: 1.788
Prob(Omnibus): 0.913 Jarque-Bera (JB): 0.083
Skew: -0.114 Prob(JB): 0.960
Kurtosis: 2.816 Cond. No. 878.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.685
Model: OLS Adj. R-squared: 0.654
Method: Least Squares F-statistic: 21.76
Date: Thu, 21 Nov 2024 Prob (F-statistic): 9.57e-06
Time: 04:31:22 Log-Likelihood: -99.815
No. Observations: 23 AIC: 205.6
Df Residuals: 20 BIC: 209.0
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 282.2473 150.715 1.873 0.076 -32.138 596.632
C(dose)[T.1] 54.4128 8.337 6.527 0.000 37.022 71.803
expression -24.9540 16.481 -1.514 0.146 -59.332 9.424
Omnibus: 0.126 Durbin-Watson: 2.016
Prob(Omnibus): 0.939 Jarque-Bera (JB): 0.072
Skew: 0.088 Prob(JB): 0.964
Kurtosis: 2.788 Cond. No. 337.

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: 04:31:22 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: 0.015
Model: OLS Adj. R-squared: -0.032
Method: Least Squares F-statistic: 0.3101
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.583
Time: 04:31:22 Log-Likelihood: -112.94
No. Observations: 23 AIC: 229.9
Df Residuals: 21 BIC: 232.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 224.3226 259.759 0.864 0.398 -315.875 764.520
expression -15.7884 28.350 -0.557 0.583 -74.746 43.169
Omnibus: 4.366 Durbin-Watson: 2.485
Prob(Omnibus): 0.113 Jarque-Bera (JB): 1.667
Skew: 0.227 Prob(JB): 0.435
Kurtosis: 1.762 Cond. No. 336.

CP101

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

F-statistic p-value df difference
0.157 0.699 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.461
Model: OLS Adj. R-squared: 0.314
Method: Least Squares F-statistic: 3.137
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0693
Time: 04:31:22 Log-Likelihood: -70.664
No. Observations: 15 AIC: 149.3
Df Residuals: 11 BIC: 152.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 125.4463 314.762 0.399 0.698 -567.339 818.232
C(dose)[T.1] 273.1545 682.038 0.400 0.696 -1228.002 1774.311
expression -6.5567 35.546 -0.184 0.857 -84.794 71.680
expression:C(dose)[T.1] -24.7975 76.083 -0.326 0.751 -192.254 142.660
Omnibus: 2.592 Durbin-Watson: 0.833
Prob(Omnibus): 0.274 Jarque-Bera (JB): 1.757
Skew: -0.819 Prob(JB): 0.415
Kurtosis: 2.644 Cond. No. 910.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.456
Model: OLS Adj. R-squared: 0.365
Method: Least Squares F-statistic: 5.027
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0260
Time: 04:31:22 Log-Likelihood: -70.736
No. Observations: 15 AIC: 147.5
Df Residuals: 12 BIC: 149.6
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 173.3428 267.784 0.647 0.530 -410.109 756.795
C(dose)[T.1] 50.9273 16.238 3.136 0.009 15.549 86.306
expression -11.9695 30.235 -0.396 0.699 -77.846 53.907
Omnibus: 2.583 Durbin-Watson: 0.877
Prob(Omnibus): 0.275 Jarque-Bera (JB): 1.831
Skew: -0.826 Prob(JB): 0.400
Kurtosis: 2.553 Cond. No. 311.

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: 04:31:22 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: 0.010
Model: OLS Adj. R-squared: -0.066
Method: Least Squares F-statistic: 0.1292
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.725
Time: 04:31:22 Log-Likelihood: -75.226
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 -27.4089 337.004 -0.081 0.936 -755.461 700.643
expression 13.5647 37.739 0.359 0.725 -67.966 95.095
Omnibus: 0.284 Durbin-Watson: 1.512
Prob(Omnibus): 0.868 Jarque-Bera (JB): 0.443
Skew: 0.015 Prob(JB): 0.802
Kurtosis: 2.159 Cond. No. 301.