LID SPN TRAP

1460687_at

Setd8 SET domain containing (lysine methyltransferase) 8

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
0.088 0.770 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.674
Model: OLS Adj. R-squared: 0.623
Method: Least Squares F-statistic: 13.11
Date: Thu, 21 Nov 2024 Prob (F-statistic): 7.14e-05
Time: 04:23:23 Log-Likelihood: -100.20
No. Observations: 23 AIC: 208.4
Df Residuals: 19 BIC: 213.0
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -90.7293 285.803 -0.317 0.754 -688.921 507.463
C(dose)[T.1] 577.6311 447.456 1.291 0.212 -358.905 1514.167
expression 14.6190 28.821 0.507 0.618 -45.704 74.942
expression:C(dose)[T.1] -53.8673 45.815 -1.176 0.254 -149.759 42.025
Omnibus: 0.664 Durbin-Watson: 1.983
Prob(Omnibus): 0.718 Jarque-Bera (JB): 0.727
Skew: -0.274 Prob(JB): 0.695
Kurtosis: 2.323 Cond. No. 1.27e+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): 2.71e-05
Time: 04:23:23 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 120.6142 224.314 0.538 0.597 -347.297 588.525
C(dose)[T.1] 51.6710 10.403 4.967 0.000 29.970 73.372
expression -6.6980 22.617 -0.296 0.770 -53.876 40.480
Omnibus: 0.259 Durbin-Watson: 1.803
Prob(Omnibus): 0.879 Jarque-Bera (JB): 0.446
Skew: 0.067 Prob(JB): 0.800
Kurtosis: 2.331 Cond. No. 509.

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: 04:23:23 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: 0.220
Model: OLS Adj. R-squared: 0.182
Method: Least Squares F-statistic: 5.909
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0241
Time: 04:23:23 Log-Likelihood: -110.25
No. Observations: 23 AIC: 224.5
Df Residuals: 21 BIC: 226.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 740.4025 271.858 2.723 0.013 175.043 1305.762
expression -67.4489 27.746 -2.431 0.024 -125.150 -9.748
Omnibus: 13.012 Durbin-Watson: 1.819
Prob(Omnibus): 0.001 Jarque-Bera (JB): 2.367
Skew: 0.048 Prob(JB): 0.306
Kurtosis: 1.431 Cond. No. 422.

CP101

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

F-statistic p-value df difference
0.495 0.495 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.516
Model: OLS Adj. R-squared: 0.384
Method: Least Squares F-statistic: 3.909
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0400
Time: 04:23:23 Log-Likelihood: -69.858
No. Observations: 15 AIC: 147.7
Df Residuals: 11 BIC: 150.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 199.1551 324.187 0.614 0.551 -514.376 912.686
C(dose)[T.1] 1282.8610 1214.021 1.057 0.313 -1389.181 3954.903
expression -13.1710 32.395 -0.407 0.692 -84.472 58.130
expression:C(dose)[T.1] -122.5856 120.745 -1.015 0.332 -388.344 143.173
Omnibus: 0.858 Durbin-Watson: 0.688
Prob(Omnibus): 0.651 Jarque-Bera (JB): 0.800
Skew: -0.386 Prob(JB): 0.670
Kurtosis: 2.173 Cond. No. 1.87e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.471
Model: OLS Adj. R-squared: 0.382
Method: Least Squares F-statistic: 5.334
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0220
Time: 04:23:23 Log-Likelihood: -70.530
No. Observations: 15 AIC: 147.1
Df Residuals: 12 BIC: 149.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 287.4050 312.716 0.919 0.376 -393.944 968.754
C(dose)[T.1] 50.4368 15.525 3.249 0.007 16.611 84.262
expression -21.9949 31.247 -0.704 0.495 -90.077 46.087
Omnibus: 2.011 Durbin-Watson: 0.825
Prob(Omnibus): 0.366 Jarque-Bera (JB): 1.555
Skew: -0.714 Prob(JB): 0.459
Kurtosis: 2.329 Cond. No. 412.

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: 04:23:23 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: 0.005
Model: OLS Adj. R-squared: -0.072
Method: Least Squares F-statistic: 0.06558
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.802
Time: 04:23:23 Log-Likelihood: -75.262
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 198.7174 410.332 0.484 0.636 -687.751 1085.185
expression -10.4723 40.893 -0.256 0.802 -98.815 77.871
Omnibus: 1.041 Durbin-Watson: 1.655
Prob(Omnibus): 0.594 Jarque-Bera (JB): 0.728
Skew: 0.086 Prob(JB): 0.695
Kurtosis: 1.934 Cond. No. 410.