LID SPN TRAP

1457268_at

Dot1l DOT1-like, histone H3 methyltransferase (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
0.966 0.338 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.668
Model: OLS Adj. R-squared: 0.616
Method: Least Squares F-statistic: 12.76
Date: Thu, 03 Apr 2025 Prob (F-statistic): 8.47e-05
Time: 22:57:34 Log-Likelihood: -100.42
No. Observations: 23 AIC: 208.8
Df Residuals: 19 BIC: 213.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 123.0461 161.678 0.761 0.456 -215.350 461.443
C(dose)[T.1] 165.8560 248.956 0.666 0.513 -355.214 686.926
expression -8.6244 20.242 -0.426 0.675 -50.991 33.742
expression:C(dose)[T.1] -12.5018 29.925 -0.418 0.681 -75.136 50.133
Omnibus: 0.260 Durbin-Watson: 1.815
Prob(Omnibus): 0.878 Jarque-Bera (JB): 0.447
Skew: 0.040 Prob(JB): 0.800
Kurtosis: 2.322 Cond. No. 609.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.665
Model: OLS Adj. R-squared: 0.632
Method: Least Squares F-statistic: 19.87
Date: Thu, 03 Apr 2025 Prob (F-statistic): 1.77e-05
Time: 22:57:34 Log-Likelihood: -100.52
No. Observations: 23 AIC: 207.0
Df Residuals: 20 BIC: 210.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 168.7016 116.665 1.446 0.164 -74.658 412.061
C(dose)[T.1] 61.9828 12.279 5.048 0.000 36.369 87.597
expression -14.3444 14.598 -0.983 0.338 -44.795 16.106
Omnibus: 0.456 Durbin-Watson: 1.937
Prob(Omnibus): 0.796 Jarque-Bera (JB): 0.554
Skew: -0.006 Prob(JB): 0.758
Kurtosis: 2.240 Cond. No. 230.

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: 22:57:34 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: 0.239
Model: OLS Adj. R-squared: 0.202
Method: Least Squares F-statistic: 6.585
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0180
Time: 22:57:34 Log-Likelihood: -109.97
No. Observations: 23 AIC: 223.9
Df Residuals: 21 BIC: 226.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -238.2912 124.089 -1.920 0.069 -496.348 19.766
expression 38.4533 14.985 2.566 0.018 7.290 69.617
Omnibus: 1.347 Durbin-Watson: 2.246
Prob(Omnibus): 0.510 Jarque-Bera (JB): 1.193
Skew: 0.413 Prob(JB): 0.551
Kurtosis: 2.250 Cond. No. 166.

CP101

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

F-statistic p-value df difference
0.109 0.747 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.535
Model: OLS Adj. R-squared: 0.408
Method: Least Squares F-statistic: 4.213
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0327
Time: 22:57:34 Log-Likelihood: -69.563
No. Observations: 15 AIC: 147.1
Df Residuals: 11 BIC: 150.0
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 159.8387 186.132 0.859 0.409 -249.834 569.512
C(dose)[T.1] -399.6384 325.612 -1.227 0.245 -1116.306 317.029
expression -10.2287 20.566 -0.497 0.629 -55.495 35.037
expression:C(dose)[T.1] 50.4016 36.447 1.383 0.194 -29.818 130.621
Omnibus: 1.772 Durbin-Watson: 1.247
Prob(Omnibus): 0.412 Jarque-Bera (JB): 1.287
Skew: -0.676 Prob(JB): 0.525
Kurtosis: 2.516 Cond. No. 482.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.454
Model: OLS Adj. R-squared: 0.363
Method: Least Squares F-statistic: 4.984
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0266
Time: 22:57:34 Log-Likelihood: -70.765
No. Observations: 15 AIC: 147.5
Df Residuals: 12 BIC: 149.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 14.8511 159.533 0.093 0.927 -332.741 362.443
C(dose)[T.1] 50.1402 15.927 3.148 0.008 15.439 84.841
expression 5.8197 17.613 0.330 0.747 -32.555 44.195
Omnibus: 2.174 Durbin-Watson: 0.913
Prob(Omnibus): 0.337 Jarque-Bera (JB): 1.557
Skew: -0.753 Prob(JB): 0.459
Kurtosis: 2.530 Cond. No. 185.

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: 22:57:34 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: 0.003
Model: OLS Adj. R-squared: -0.074
Method: Least Squares F-statistic: 0.03361
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.857
Time: 22:57:35 Log-Likelihood: -75.281
No. Observations: 15 AIC: 154.6
Df Residuals: 13 BIC: 156.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 130.5660 201.542 0.648 0.528 -304.839 565.971
expression -4.1238 22.495 -0.183 0.857 -52.722 44.474
Omnibus: 0.658 Durbin-Watson: 1.602
Prob(Omnibus): 0.720 Jarque-Bera (JB): 0.602
Skew: 0.057 Prob(JB): 0.740
Kurtosis: 2.025 Cond. No. 180.